{ "summary": "Deep research harness — fan-out web searches, fetch sources, adversarially verify claims, synthesize a cited report.", "agentCount": 103, "logs": [ "Q: HOW exactly to implement a unified geometry+texture 3D DiT with per-modality tim…", "Decomposed into 5 angles: Primary spec — JointDiT exact sampling & gating formulas, Foundational mechanism — UniDiffuser per-modality timesteps & free CFG, Implementation/code — UniCon zero-init connections and warm-starting from specialists, Theory — validity of rectified-flow/velocity targets under mixed independent timesteps, Domain-specific & recent — joint appearance-geometry diffusion, timestep tokens, 3D/paired-modality follow-ups", "Primary spec — JointDiT exact sampling & gating formulas: 6 results", "Foundational mechanism — UniDiffuser per-modality timesteps & free CFG: 6 results", "Domain-specific & recent — joint appearance-geometry diffusion, timestep tokens, 3D/paired-modality follow-ups: 6 results", "Domain-specific & recent — joint appearance-geometry diffusion, timestep tokens, 3D/paired-modality follow-ups: 4 novel (2 filtered)", "Theory — validity of rectified-flow/velocity targets under mixed independent timesteps: 6 results", "Theory — validity of rectified-flow/velocity targets under mixed independent timesteps: 2 novel (4 filtered)", "Implementation/code — UniCon zero-init connections and warm-starting from specialists: 6 results", "Implementation/code — UniCon zero-init connections and warm-starting from specialists: 3 novel (3 filtered)", "Fetched 21 sources → 100 claims → verifying top 25", "\"Task switching is done purely by dialing per-modal…\": 3-0 ✓", "\"JointDiT is built by freezing a pre-trained Flux D…\": 3-0 ✓", "\"JointDiT's adaptive scheduling weights gate the jo…\": 3-0 ✓", "\"JointDiT's adaptive scheduling weights use exactly…\": 3-0 ✓", "\"All combinatorial tasks are performed purely by di…\": 3-0 ✓", "\"The cross-stream connection is a UniCon-style join…\": 3-0 ✓", "\"The exact unbalanced sampling density is f(t) = 1 …\": 3-0 ✓", "\"JointDiT's unbalanced timestep sampling: with 50% …\": 0-3 ✗", "\"JointDiT's cross-stream joint connection is the Un…\": 3-0 ✓", "\"JointDiT trains with a 50/50 mixture of timestep r…\": 3-0 ✓", "\"The depth branch is built by adding LoRA (rank 64,…\": 3-0 ✓", "\"JointDiT achieves all three inference modes — join…\": 3-0 ✓", "\"The unbalanced timestep sampling is an exact 50/50…\": 3-0 ✓", "\"JointDiT trains with 'adaptive scheduling weights'…\": 3-0 ✓", "\"JointDiT trains with a 50/50 mixture: half the tim…\": 3-0 ✓", "\"All inference modes are dialed purely by per-modal…\": 3-0 ✓", "\"JointDiT uses an 'unbalanced timestep sampling str…\": 3-0 ✓", "\"JointDiT's adaptive scheduling weight is w_x(t_x,t…\": 3-0 ✓", "\"JointDiT is built on a frozen Flux backbone and tr…\": 3-0 ✓", "\"Generation modes are dialed purely by per-modality…\": 3-0 ✓", "\"The unbalanced timestep density (Eq. 7) has the fo…\": 2-1 ✓", "\"UniDiffuser trains a single noise-prediction netwo…\": 3-0 ✓", "\"Exact conditioning (e.g., texture-given-mesh with …\": 3-0 ✓", "\"UniDiffuser gets classifier-free guidance for free…\": 3-0 ✓", "\"Marginal (single-modality) generation is achieved …\": 3-0 ✓", "Verify done: 25 claims → 24 confirmed, 1 refuted, 0 unverified" ], "result": { "question": "HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] fusion tokens; shape and tex SLAT tokens live on the SAME sparse voxel coords. We want to merge the two SLAT flows into ONE dual-stream DiT that supports, by dialing per-modality timesteps only: (a) joint shape+tex generation, (b) mesh-only (marginal), (c) texture-given-mesh with exact geometry lock (shape tokens clean, never integrated), (d) geometry-given-texture. Research the precise implementation recipe across: UniDiffuser (arXiv 2303.06555), JointDiT (2505.00482), UniCon (its joint-connection module source), Diffusion Forcing (2407.01392), COP-GEN (2603.03239), Orchid joint appearance-geometry diffusion, OneDiffusion, CoDi, per-frame-noise video works (SkyReels-V2/rolling diffusion), and any 2025-2026 multi-modal flow-matching follow-ups. Extract and VERIFY concrete details: (1) training timestep-pair sampling — JointDiT's exact unbalanced sampling distributions (the f/g densities with s=3.1582 and s=0.25, Eq.7) and the 50/50 diagonal/independent mixture; whether explicit corner sampling (t_other=clean or =max) is needed and at what ratios; (2) JointDiT adaptive scheduling weights — confirm the formula w=sigmoid(α(t_ratio−1/2)), α=3, and confirm it gates joint cross-attention OUTPUTS (features) not the loss; (3) how per-modality timesteps are injected — per-branch AdaLN vs prepended timestep tokens (COP-GEN) vs both, what each paper does and what breaks; (4) zero-initialized cross-stream connection details from UniCon/JointDiT — LoRA rank, where connections attach, why zero-init matters for warm-starting from two specialist checkpoints; (5) marginal-mode handling — other modality fed pure noise at t=max vs dropped tokens, which works and training requirements; (6) CFG in these models — UniDiffuser's free classifier-free guidance (uncond = other modality at max noise) exact formula, per-modality guidance scales, interaction with condition-dropout CFG we already use; (7) rectified-flow/velocity-target validity under mixed per-modality timesteps (Diffusion Forcing ELBO argument, any flow-matching-specific corrections); (8) reported failure modes: corner starvation, extreme-timestep degradation, joint-vs-specialist quality gaps, modality loss imbalance; (9) anything specific to sparse/structured 3D latents or shared-coordinate paired modalities. Output: a verified implementation checklist — each item with mechanism, exact formula/ratio where known, source, and confidence.", "summary": "The verified recipe for a unified shape+tex SLAT DiT with per-modality timesteps is essentially \"JointDiT mechanics on UniDiffuser theory\": train one network on all timestep pairs (t_shape, t_tex), where UniDiffuser (ICML 2023) proves conditioning = hold a modality clean at the data end (exact geometry lock, never integrated) and marginalization = feed the other modality as pure noise at the max-noise end — not dropped tokens — which also yields classifier-free guidance for free via ε̂ = (1+s)·ε(x,y_clean) − s·ε(x,ε_y@maxnoise) with no condition-dropout. JointDiT (ICCV 2025) supplies the verified concrete recipe: (a) 50/50 timestep mixture — half diagonal (one shared t from f(t)), half independent from f(t)/g(t) with the shifted-sigmoid transform t=1−σ(z)·s/(1+(s−1)σ(z)), z~N(0,1), s=3.1582 (kohya sd3/Flux default) and s=0.25, with NO explicit corner sampling; (b) adaptive scheduling weights w_x=sigmoid(α(t_y/(t_x+t_y)−1/2)), α=3, that multiply the joint cross-attention OUTPUTS (features, not the loss) so the noisier branch listens more to the cleaner one; (c) cross-stream connections = UniCon-style bidirectional query-exchange cross-attention followed by a ZERO-INITIALIZED linear projection, so at init the joint model exactly reproduces the pretrained specialist(s) — the key to warm-starting from existing checkpoints. Critical implementation trap: both papers use the Lipman flow-matching convention (t=0 noise, t=1 clean), the reverse of the TRELLIS/SD3 x_t=(1−t)x0+tε convention, so every formula needs a t→1−t remap (which also changes the scheduling-weight denominator from t_x+t_y to 2−t_x−t_y). Notably absent from surviving evidence: Diffusion Forcing's ELBO justification for mixed-timestep velocity targets, COP-GEN's timestep-token injection, and anything specific to sparse/shared-coordinate 3D latents — those must be treated as design decisions, not verified facts.", "findings": [ { "claim": "Task/mode switching is done purely by dialing per-modality timesteps at inference, with no per-task retraining or extra guidance: in JointDiT (t=0 noise, t=1 clean), t_x=0,t_y=0 gives joint RGB+depth generation; t_x=1,t_y=0 gives depth-from-image (image branch held clean, never integrated); t_x=0,t_y=1 gives depth-conditioned image generation. JointDiT uses 20 denoising steps for all modes and needs no ensembling for the estimation mode; joint generation works even without a text prompt. This is exactly the joint / mesh-only-marginal / texture-given-mesh / geometry-given-texture mode matrix the unified SLAT DiT needs, with the conditioning modality pinned at its clean corner as a hard lock.", "confidence": "high", "sources": [ "https://arxiv.org/abs/2505.00482", "https://openaccess.thecvf.com/content/ICCV2025/papers/Byung-Ki_JointDiT_Enhancing_RGB-Depth_Joint_Modeling_with_Diffusion_Transformers_ICCV_2025_paper.pdf", "https://openaccess.thecvf.com/content/ICCV2025/supplemental/Byung-Ki_JointDiT_Enhancing_RGB-Depth_ICCV_2025_supplemental.pdf", "https://byungki-k.github.io/JointDiT/" ], "evidence": "Verbatim: \"Initially setting t_x=0, t_y=0 leads to the joint generation of both images and depth maps. When t_x=1, t_y=0, it performs depth estimation from a given image, and when t_x=0, t_y=1, it becomes a depth-conditioned image generation\" — \"simply by adjusting t_x and t_y without any additional guidance.\" Supplement: \"We consistently use 20 denoising steps across all experiments... Unlike Marigold and GeoWizard, we do not use any ensemble technique.\" Depth estimation uses an empty text prompt; conditioning modality stays at the clean endpoint with zero remaining integration interval (hard lock by construction). Merged from 5 claims, all 3-0.", "vote": "3-0 (merged from claims 2, 6, 11, 12, 19)" }, { "claim": "JointDiT's training timestep-pair sampling is an exact 50/50 mixture: with 50% probability t_x and t_y are sampled INDEPENDENTLY from two different distributions f(t) and g(t) (branch assignment randomly swapped, 'or vice versa'); with the remaining 50% a SINGLE shared timestep sampled from f(t) is used for both (diagonal). The sampling transform is t = 1 − σ(z)·s/(1+(s−1)·σ(z)) with z~N(0,1), σ=sigmoid; s=3.1582 for f(t) (inherited from the kohya-ss sd-scripts sd3-branch Flux training default, --discrete_flow_shift 3.1582 = exp(1.15)) and s=0.25 for g(t). NO explicit corner sampling (forcing t_other to exactly clean or max noise) is used during training — the corner modes are still covered at inference, validated by the paper's corner-mode ablations. (This is supplement Eq. 1 / arXiv-merged Eq. 7.)", "confidence": "high", "sources": [ "https://openaccess.thecvf.com/content/ICCV2025/supplemental/Byung-Ki_JointDiT_Enhancing_RGB-Depth_ICCV_2025_supplemental.pdf", "https://openaccess.thecvf.com/content/ICCV2025/papers/Byung-Ki_JointDiT_Enhancing_RGB-Depth_Joint_Modeling_with_Diffusion_Transformers_ICCV_2025_paper.pdf", "https://arxiv.org/abs/2505.00482", "https://huggingface.co/papers/2505.00482" ], "evidence": "Supplement Sec. A.3 verbatim: \"the timesteps, i.e., tx and ty, are separately sampled from the timestep distributions f(t) and g(t), respectively, or vice versa. This is applied with a 50% probability during training, while for the remaining 50%, the same timestep sampled from f(t) is used for both tx and ty. ... f(t) = 1− σ(z)·s/(1+(s−1)·σ(z)), where z∼N(0,1). ... s is set to 3.1582 [footnote: github.com/kohya-ss/sd-scripts/tree/sd3]. We set s to 0.25 to obtain g(t).\" Verifiers grepped both full PDFs: 'corner' never appears as a training procedure; t=0/1 settings appear only as inference configs. Caution: arXiv v1 printed the equation WITHOUT the leading '1−' (added in v2/v3, current) — the two versions differ by a t↔1−t flip, so pin the noise-direction convention before porting the constants. Merged from 6 claims (five 3-0, one 2-1).", "vote": "3-0 / 2-1 (merged from claims 4, 7, 8, 14, 15, 16)" }, { "claim": "JointDiT's adaptive scheduling weights gate the joint cross-attention OUTPUTS in the residual stream — features, NOT the training loss: G_x = Attn(S_x) + w_x(t_x,t_y)·JointAttn(S_x,S_y), with w_x = sigmoid(α(t_y/(t_x+t_y) − 1/2)), w_y = sigmoid(α(t_x/(t_x+t_y) − 1/2)), α=3 in all experiments. Direction: \"more weight is given to the output of joint cross-attention for the noisier branch (relatively closer to t=0), letting it follow the domain structure of the cleaner branch.\" The weight is applied after the zero-initialized projection of the joint connection. Under the x_t=(1−t)x0+tε convention (t=1 noise), the roles of w_x/w_y swap via t→1−t — and note the exact substitution changes the normalizing denominator from t_x+t_y to 2−t_x−t_y, so a bare sign flip is an approximation.", "confidence": "high", "sources": [ "https://arxiv.org/abs/2505.00482", "https://openaccess.thecvf.com/content/ICCV2025/papers/Byung-Ki_JointDiT_Enhancing_RGB-Depth_Joint_Modeling_with_Diffusion_Transformers_ICCV_2025_paper.pdf", "https://openaccess.thecvf.com/content/ICCV2025/supplemental/Byung-Ki_JointDiT_Enhancing_RGB-Depth_ICCV_2025_supplemental.pdf", "https://byungki-k.github.io/JointDiT/" ], "evidence": "Eq. 5–6 verified verbatim in raw arXiv HTML and ICCV PDF independently by multiple verifiers: \"The adaptive scheduling weights are individually multiplied with the joint cross-attention outputs\"; \"We set α to 3 for all experiments.\" The JCFM loss (Eq. 4) contains no such weighting — no loss-gating anywhere. Supplement A.4: \"The adaptive scheduling weight is applied subsequently\" (after the zero-init projection). Merged from 5 claims, all 3-0.", "vote": "3-0 (merged from claims 0, 3, 9, 13, 17)" }, { "claim": "JointDiT's warm-start architecture: freeze the pretrained Flux backbone entirely; add the second-modality (depth) branch as LoRA on the frozen weights — rank 64 (alpha = rank/2) on the components before/after attention in MM-DiT and P-DiT blocks (img/txt mod.lin, attn.qkv, attn.proj; linear1, modulation.lin), rank 512/1024 at the input stage (vector_in.in_layer, vector_in.out_layer, txt_in); connect streams with UniCon's joint cross-attention (bidirectional query exchange: O_x=Softmax(Q_x K_y^T/√d)V_y and symmetric) followed by a ZERO-INITIALIZED linear projection. Zero-init means the joint model exactly reproduces the pretrained specialist at init — the mechanism for adding joint modeling without degrading a specialist. Rank ablation is monotone: 16/32/64 → NYUv2 AbsRel 9.1/6.6/5.7 (ScanNet 9.8/8.5/6.6); they stopped at 64 due to parameter growth. Caveat for our setting: JointDiT warm-starts from ONE pretrained RGB specialist (depth branch = LoRA-adapted copy of the same frozen backbone), not from two independently-pretrained specialist checkpoints as in our shape-SLAT + tex-SLAT case.", "confidence": "high", "sources": [ "https://arxiv.org/abs/2505.00482", "https://openaccess.thecvf.com/content/ICCV2025/supplemental/Byung-Ki_JointDiT_Enhancing_RGB-Depth_ICCV_2025_supplemental.pdf", "https://openaccess.thecvf.com/content/ICCV2025/papers/Byung-Ki_JointDiT_Enhancing_RGB-Depth_Joint_Modeling_with_Diffusion_Transformers_ICCV_2025_paper.pdf", "https://github.com/kaist-ami/JointDiT", "https://arxiv.org/abs/2410.11439" ], "evidence": "Verbatim: \"We train the LoRAs and joint connection modules while keeping the pre-trained backbone model frozen\"; \"We use a LoRA rank of 64 for both MM-DiT and P-DiT, and apply relatively larger ranks of 512 or 1024 to the input stage. The alpha value is set to half of the corresponding rank\"; \"we adopt the joint cross-attention module from UniCon, followed by a zero-initialized linear projection layer.\" Module list from supplement Table S1; rank ablation from Table S4 (\"We did not increase the LoRA rank beyond 64 because the number of model parameters grows exponentially\"). UniCon (arXiv 2410.11439) confirms bidirectional cross-attn with zero-initialized ProjOut. Official code kaist-ami/JointDiT corroborates. Merged from 4 claims, all 3-0.", "vote": "3-0 (merged from claims 1, 5, 10, 18)" }, { "claim": "UniDiffuser (the theoretical baseline) trains a single network over ALL modality-timestep pairs with the simplest possible scheme: t_x and t_y sampled independently and UNIFORMLY from {1,...,T} — no diagonal/corner mixture, no unbalanced density — and a single joint L2 loss on the concatenated noise of both modalities: E||ε_θ(x_{t_x}, y_{t_y}, t_x, t_y) − [ε_x, ε_y]||². This works but is exactly the scheme JointDiT's unbalanced sampling improves upon (corners hit with probability only ~1/T each — the corner-starvation motivation).", "confidence": "high", "sources": [ "https://arxiv.org/abs/2303.06555", "https://proceedings.mlr.press/v202/bao23a/bao23a.pdf" ], "evidence": "Eq. 5 verified verbatim in ar5iv full text and ICML 2023 proceedings PDF: \"t^x and t^y are uniformly sampled from {1,2,...,T} independently.\" Verifier confirmed the full text contains no diagonal mixture, corner up-weighting, or non-uniform timestep density — those appear only in JointDiT as explicit departures. Corners/diagonal appear in UniDiffuser only as inference settings. 3-0.", "vote": "3-0 (claim 20)" }, { "claim": "Exact conditioning (texture-given-mesh with hard geometry lock) = set the conditioning modality's timestep to its CLEAN end and feed the clean latents, never integrating them: UniDiffuser proves E[ε^x | x_{t_x}, y_0] with t_y=0(clean) recovers q(x_0|y_0) exactly at the population optimum (\"a zero timestep means conditioning on the corresponding modality\"); its conditional-sampling algorithm keeps y fixed at y_0 for all steps and only updates x. The mechanism is standard through 2026 — COP-GEN(-Beta) and others fix the conditioning set at the clean timestep and diffuse only the generation set. Caveats: exactness holds at the population optimum (a trained net approximates it), and since UniDiffuser trains t on {1..T}, the t=0 corner is a boundary query — the practical reason JointDiT adds unbalanced sampling that puts real mass near the corners.", "confidence": "high", "sources": [ "https://arxiv.org/abs/2303.06555", "https://proceedings.mlr.press/v202/bao23a/bao23a.pdf", "https://arxiv.org/pdf/2504.08548", "https://github.com/thu-ml/unidiffuser" ], "evidence": "Verbatim from Sec. 3.1: \"a zero timestep means conditioning on the corresponding modality... E[ε^x|x_{t^x}, y_0] corresponds to the conditional distribution q(x_0|y_0) by setting t^y=0.\" Algorithm 3 verified: y stays y_0 throughout; x_{t−1} updated from ε_θ^x(x_t, y_0, t, 0). Conditional identity stated without qualification (unlike the ≈ used for the marginal case). 3-0.", "vote": "3-0 (claim 21)" }, { "claim": "Marginal (mesh-only) generation = feed the OTHER modality as PURE NOISE at the max-noise timestep — tokens are fed, not dropped: E[ε^x | x_{t_x}, y_T] ≈ E[ε^x | x_{t_x}], recovering q(x_0). UniDiffuser's unconditional-sampling algorithm samples ε~N(0,I) fresh and evaluates ε_θ(x_t, ε, t, T) at every step; \"UniDiffuser does not need to specify a null token.\" The ≈ gap is \"negligible... for a large T\" under DDPM, and becomes EXACT under rectified flow where the max-noise endpoint is x_1=ε by construction. Practical requirement: marginal-mode quality depends on the max-noise corner being adequately covered during training (corner starvation under uniform-independent sampling; JointDiT's g(t) with s=0.25 skews mass toward that end).", "confidence": "high", "sources": [ "https://arxiv.org/abs/2303.06555", "https://proceedings.mlr.press/v202/bao23a/bao23a.pdf" ], "evidence": "Verbatim from Sec. 3.1: \"a maximum timestep T means marginalizing it. Namely, by setting t^y=T, we have E[ε^x|x_{t^x}, y_T] ≈ E[ε^x|x_{t^x}], which corresponds to the marginal distribution q(x_0).\" Appendix B Algorithm 2 verified: text-modality input is a fresh Gaussian tensor at timestep T each step, not a dropped/null token. Footnote 1: \"There is a negligible gap between y_T and the standard Gaussian noise ε^y for a large T (e.g., 1000 by default).\" 3-0.", "vote": "3-0 (claim 22)" }, { "claim": "Classifier-free guidance comes FREE from the joint model with no condition-dropout training: the unconditional branch is the same network with the conditioning modality replaced by pure noise at max timestep — ε̂^x_θ(x_t, y_0, t) = (1+s)·ε^x_θ(x_t, y_0, t, 0) − s·ε^x_θ(x_t, ε^y, t, T). The official code implements exactly this (text_N = torch.randn_like(text), t_text = N; x_out + scale·(x_out − x_out_uncond)). This composes with, rather than replaces, ordinary condition-dropout CFG on external conditions: JointDiT still applies standard 10% text-drop for its text-prompt CFG while modality-level guidance runs through timesteps — i.e., our existing [DINOv3;VLM] fusion-token dropout CFG stays as-is, and cross-modality guidance (e.g., texture guided more/less by geometry) is available separately via the noise-at-max-t trick with its own per-modality scale.", "confidence": "high", "sources": [ "https://arxiv.org/abs/2303.06555", "https://github.com/thu-ml/unidiffuser", "https://openaccess.thecvf.com/content/ICCV2025/papers/Byung-Ki_JointDiT_Enhancing_RGB-Depth_Joint_Modeling_with_Diffusion_Transformers_ICCV_2025_paper.pdf" ], "evidence": "UniDiffuser Sec. 3.2 verbatim: \"ε̂^x_θ(x_t,y_0,t) = (1+s)ε^x_θ(x_t,y_0,t,0) − s·ε^x_θ(x_t,ε^y,t,T)... UniDiffuser can perform CFG for free in conditional sampling because it captures both the conditional and unconditional models\" — \"without modifying the training process.\" Official sample_multi_v1.py verified to implement it. JointDiT verbatim: \"apply text drop with a probability of 10%\" for its text condition, alongside timestep-dialed modality modes. Note: the unidiffuser codebase also ships an alternative empty-context unconditional variant; noise-at-T is the paper's 'free' mechanism but not the only one in the code. Merged from claims 23 and 6 (CFG portion).", "vote": "3-0 (merged from claims 23, 6)" }, { "claim": "Timestep-convention trap (implementation-critical): JointDiT and UniDiffuser's flow-matching formulas use the Lipman convention — p_0 = noise prior, p_1 = data, i.e., t=0 pure noise / t=1 clean — the REVERSE of the TRELLIS/SD3/Flux rectified-flow convention x_t=(1−t)x0+tε (t=1 noise). Every ported formula needs a t→1−t remap: mode corners flip (geometry lock = t_shape at the CLEAN end of whatever convention you use), the f/g sampling constants flip direction (and the arXiv v1-vs-v3 '1−' discrepancy in Eq. 7 is exactly this flip), and the scheduling weights swap roles with the denominator changing t_x+t_y → 2−t_x−t_y. Verifiers flagged this independently on three separate claims; the refuted claim in this synthesis died partly on this convention ambiguity.", "confidence": "high", "sources": [ "https://arxiv.org/abs/2505.00482", "https://arxiv.org/abs/2303.06555", "https://openaccess.thecvf.com/content/ICCV2025/supplemental/Byung-Ki_JointDiT_Enhancing_RGB-Depth_ICCV_2025_supplemental.pdf" ], "evidence": "JointDiT preliminaries verbatim: \"p_0 represents a simple prior... and p_1 represents a data distribution\"; corner assignments (t_x=0,t_y=0 → joint gen from sampled noise; t_x=1 → given clean image) are only coherent under t=0=noise. Verifier on claim 3: \"the exact transform is t→1−t... the exact substitution also changes the normalizing denominator from tx+ty to 2−tx−ty, so a bare sign flip is an approximation.\" Verifier on claim 16: arXiv v1 vs v3 Eq. 7 differ by the leading '1−', i.e., a t↔1−t flip. Synthesized from verifier evidence across claims 2, 3, 16.", "vote": "3-0 (synthesized from verifier evidence on claims 2, 3, 16)" } ], "caveats": "1) Convention flip is the biggest porting hazard: all verified formulas are in the t=0-noise/t=1-clean convention; TRELLIS uses x_t=(1−t)x0+tε (t=1 noise). The arXiv v1 of JointDiT printed Eq. 7 without the leading '1−' (fixed in v2/v3, the current authoritative text) — use the v3/ICCV-supplement form and re-derive rather than sign-flip the scheduling weights. 2) Transfer gap: JointDiT warm-starts ONE frozen Flux specialist and LoRA-copies it into a depth branch; our setting has TWO independently pretrained 1.3B SLAT specialists (shape, tex) likely trained with full fine-tuning — the zero-init-connection principle transfers, but LoRA ranks, the frozen-backbone choice, and the rank-64 ablation numbers are Flux/RGB-depth-specific and were never tested for two-checkpoint fusion. 3) Coverage gaps: no claims survived on Diffusion Forcing's ELBO argument for mixed-timestep velocity targets, COP-GEN's prepended-timestep-token injection, per-branch AdaLN vs timestep-token comparisons, Orchid, OneDiffusion, CoDi, rolling/per-frame-noise video works, or anything sparse-3D/shared-coordinate-specific — the research question's items (3), (7), (8-partial), and (9) are therefore unanswered by verified evidence, not settled. 4) 'No explicit corner sampling' is a verified negative for JointDiT specifically (grep of both full PDFs), but JointDiT's conditional modes are side capabilities; whether corner oversampling helps when texture-given-mesh is a first-class product is untested. 5) Known JointDiT quality caveats (from verifier notes, not headline claims): ~19.8% parameter overhead, 1.6s→4.7s sampling slowdown, and joint-model depth estimation still trails specialist estimators on ETH3D — a real joint-vs-specialist gap exists. 6) One claim was refuted 0-3 (a paraphrase of Eq. 7 with imprecise density/provenance framing); its corrected content survives in the confirmed Eq. 7 claims. 7) Evidence is heavily concentrated in two papers (JointDiT, UniDiffuser) plus UniCon's module; unanimity is high but source diversity is moderate.", "openQuestions": [ "Is the rectified-flow velocity target v=ε−x0 still a valid regression target per-modality when the two modalities sit at different timesteps (the flow-matching analogue of Diffusion Forcing's ELBO argument), and does the cross-modality input change the optimal target in any way that needs a correction term? No surviving claim addressed this.", "How should per-modality timesteps be injected in our architecture — per-branch AdaLN (JointDiT inherits Flux's modulation via its LoRA'd modulation.lin layers, implying AdaLN-style) vs COP-GEN-style prepended timestep tokens vs both — and what breaks with each under sparse SLAT tokens? Not resolved by verified evidence.", "For shape+tex tokens on the SAME sparse voxel coordinates, can the UniCon-style joint cross-attention be replaced or augmented by coordinate-aligned token-wise fusion (concat/add at matching voxels), and does exact coordinate sharing change the corner-starvation and scheduling-weight story? No paper in the verified set handles shared-coordinate paired modalities.", "When conditional modes (texture-given-mesh with exact lock) are primary products rather than emergent side capabilities, does adding explicit corner sampling (t_other pinned exactly clean at some ratio, e.g., 10-20%) beat JointDiT's pure f/g unbalanced sampling — and does the answer differ for the max-noise (marginal) corner vs the clean (conditioning) corner?" ], "refuted": [ { "claim": "JointDiT's unbalanced timestep sampling: with 50% probability, t_x and t_y are sampled independently from two skewed densities f(t) (built from Flux's shifted logit-normal with s=3.1582) and g(t) (same form with s=0.25); with the remaining 50% probability, a single timestep drawn from f(t) is assigned to both modalities (diagonal sampling). The density has the form f(t) = 1 − s·σ(z)/(1+(s−1)·σ(z)) with z ~ N(0,1) (Eq. 7).", "vote": "0-3", "source": "https://arxiv.org/abs/2505.00482" } ], "unverified": [], "sources": [ { "url": "https://arxiv.org/abs/2505.00482", "quality": "primary", "angle": "Primary spec — JointDiT exact sampling & gating formulas", "claimCount": 5 }, { "url": "https://openaccess.thecvf.com/content/ICCV2025/papers/Byung-Ki_JointDiT_Enhancing_RGB-Depth_Joint_Modeling_with_Diffusion_Transformers_ICCV_2025_paper.pdf", "quality": "primary", "angle": "Primary spec — JointDiT exact sampling & gating formulas", "claimCount": 5 }, { "url": "https://openaccess.thecvf.com/content/ICCV2025/supplemental/Byung-Ki_JointDiT_Enhancing_RGB-Depth_ICCV_2025_supplemental.pdf", "quality": "primary", "angle": "Primary spec — JointDiT exact sampling & gating formulas", "claimCount": 5 }, { "url": "https://byungki-k.github.io/JointDiT/", "quality": "primary", "angle": "Primary spec — JointDiT exact sampling & gating formulas", "claimCount": 5 }, { "url": "https://www.themoonlight.io/en/review/jointdit-enhancing-rgb-depth-joint-modeling-with-diffusion-transformers", "quality": "secondary", "angle": "Primary spec — JointDiT exact sampling & gating formulas", "claimCount": 5 }, { "url": "https://huggingface.co/papers/2505.00482", "quality": "primary", "angle": "Primary spec — JointDiT exact sampling & gating formulas", "claimCount": 5 }, { "url": "https://arxiv.org/abs/2303.06555", "quality": "primary", "angle": "Foundational mechanism — UniDiffuser per-modality timesteps & free CFG", "claimCount": 5 }, { "url": "https://proceedings.mlr.press/v202/bao23a/bao23a.pdf", "quality": "primary", "angle": "Foundational mechanism — UniDiffuser per-modality timesteps & free CFG", "claimCount": 5 }, { "url": "https://github.com/thu-ml/unidiffuser", "quality": "primary", "angle": "Foundational mechanism — UniDiffuser per-modality timesteps & free CFG", "claimCount": 5 }, { "url": "https://huggingface.co/docs/diffusers/en/api/pipelines/unidiffuser", "quality": "primary", "angle": "Foundational mechanism — UniDiffuser per-modality timesteps & free CFG", "claimCount": 5 }, { "url": "https://openreview.net/forum?id=Urp3atR1Z3", "quality": "unreliable", "angle": "Foundational mechanism — UniDiffuser per-modality timesteps & free CFG", "claimCount": 0 }, { "url": "https://arxiv.org/pdf/2505.00482", "quality": "primary", "angle": "Foundational mechanism — UniDiffuser per-modality timesteps & free CFG", "claimCount": 5 }, { "url": "https://github.com/lixirui142/UniCon", "quality": "primary", "angle": "Implementation/code — UniCon zero-init connections and warm-starting from specialists", "claimCount": 5 }, { "url": "https://arxiv.org/abs/2410.11439", "quality": "primary", "angle": "Implementation/code — UniCon zero-init connections and warm-starting from specialists", "claimCount": 5 }, { "url": "https://github.com/lixirui142/UniCon/blob/main/patch/patch.py", 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"agentId": "a17f2da9b0148cee6", "model": "claude-fable-5", "state": "done", "startedAt": 1786399398461, "queuedAt": 1786399396374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "How exactly to implement a unified geometry+texture 3D DiT …", "promptPreview": "Decompose this research question into complementary search angles.\n\n## Question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cro…", "lastProgressAt": 1786399432529, "tokens": 23496, "toolCalls": 1, "durationMs": 34066, "resultPreview": "{\"question\":\"How exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\\\"iso-generation\\\") — merging TRELLIS.2-style shape-SLAT and tex-SLAT rectified-flow DiTs (shared sparse voxel coords, read-once cross-attn on DINOv3+VLM fusion tokens) into one dual-stream DiT supporting joint, marginal, and exact-conditioning modes by dialing per-modality timesteps only; extract …" }, { "type": "workflow_agent", "index": 2, "label": "search:Primary spec — JointDiT exact sampling & gating formulas", "phaseIndex": 2, "phaseTitle": "Search", "agentId": "ae6a9528b0986af5e", "model": "claude-fable-5", "state": "done", "startedAt": 1786399434036, "queuedAt": 1786399432562, "attempt": 1, "lastToolName": "StructuredOutput", "promptPreview": "## Web Searcher: Primary spec — JointDiT exact sampling & gating formulas\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned vi…", "lastProgressAt": 1786399482674, "tokens": 26958, "toolCalls": 4, "durationMs": 48637, "resultPreview": "{\"results\":[{\"url\":\"https://arxiv.org/abs/2505.00482\",\"title\":\"JointDiT: Enhancing RGB-Depth Joint Modeling with Diffusion Transformers (arXiv 2505.00482)\",\"relevance\":\"high\",\"snippet\":\"The primary source itself. Defines the two key techniques to verify: adaptive scheduling weights that depend on each modality's noise level, and the unbalanced timestep sampling strategy (Eq.7 f/g densities). Confi…" }, { "type": "workflow_agent", "index": 3, "label": "search:Foundational mechanism — UniDiffuser per-modality timesteps & free CFG", "phaseIndex": 2, "phaseTitle": "Search", "agentId": "a5a228df07ddfc3c4", "model": "claude-fable-5", "state": "done", "startedAt": 1786399434096, "queuedAt": 1786399432562, "attempt": 1, "lastToolName": "StructuredOutput", "promptPreview": "## Web Searcher: Foundational mechanism — UniDiffuser per-modality timesteps & free CFG\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs …", "lastProgressAt": 1786399500103, "tokens": 28577, "toolCalls": 4, "durationMs": 66007, "resultPreview": "{\"results\":[{\"url\":\"https://arxiv.org/abs/2303.06555\",\"title\":\"One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale (UniDiffuser)\",\"relevance\":\"high\",\"snippet\":\"The canonical UniDiffuser paper. States the core recipe directly: perturb ALL modalities, feed an individual timestep per modality, and predict noise for all modalities; marginal/conditional/joint distributions all redu…" }, { "type": "workflow_agent", "index": 4, "label": "search:Implementation/code — UniCon zero-init connections and warm-starting from specialists", "phaseIndex": 2, "phaseTitle": "Search", "agentId": "a3e20312e239ea424", "model": "claude-fable-5", "state": "done", "startedAt": 1786399434151, "queuedAt": 1786399432562, "attempt": 1, "lastToolName": "StructuredOutput", "promptPreview": "## Web Searcher: Implementation/code — UniCon zero-init connections and warm-starting from specialists\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows…", "lastProgressAt": 1786399575377, "tokens": 37854, "toolCalls": 14, "durationMs": 141226, "resultPreview": "{\"results\":[{\"url\":\"https://github.com/lixirui142/UniCon\",\"title\":\"GitHub - lixirui142/UniCon: A Simple Approach to Unifying Diffusion-based Conditional Generation (ICLR 2025)\",\"relevance\":\"high\",\"snippet\":\"Ground-truth source for the connection recipe. patch/patch.py: joint cross-attention is injected parallel to each block's self-attention via attn1n = copy.deepcopy(attn1) — i.e. the cross-strea…" }, { "type": "workflow_agent", "index": 5, "label": "search:Theory — validity of rectified-flow/velocity targets under mixed independent timesteps", "phaseIndex": 2, "phaseTitle": "Search", "agentId": "a850d53fa68268b81", "model": "claude-fable-5", "state": "done", "startedAt": 1786399434114, "queuedAt": 1786399432562, "attempt": 1, "lastToolName": "StructuredOutput", "promptPreview": "## Web Searcher: Theory — validity of rectified-flow/velocity targets under mixed independent timesteps\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flow…", "lastProgressAt": 1786399553658, "tokens": 33261, "toolCalls": 8, "durationMs": 119544, "resultPreview": "{\"results\":[{\"url\":\"https://arxiv.org/abs/2407.01392\",\"title\":\"Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion (arXiv 2407.01392)\",\"relevance\":\"high\",\"snippet\":\"The foundational theory result for the whole plan: training a denoiser where each token carries an INDEPENDENT noise level is proven (Theorem 3.1 / Appendix) to optimize a valid variational lower bound on the likelih…" }, { "type": "workflow_agent", "index": 6, "label": "search:Domain-specific & recent — joint appearance-geometry diffusion, timestep tokens, 3D/paired-modality follow-ups", "phaseIndex": 2, "phaseTitle": "Search", "agentId": "a26185478cecb32a2", "model": "claude-fable-5", "state": "done", "startedAt": 1786399434608, "queuedAt": 1786399432562, "attempt": 1, "lastToolName": "StructuredOutput", "promptPreview": "## Web Searcher: Domain-specific & recent — joint appearance-geometry diffusion, timestep tokens, 3D/paired-modality follow-ups\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1…", "lastProgressAt": 1786399525979, "tokens": 33327, "toolCalls": 9, "durationMs": 91371, "resultPreview": "{\"results\":[{\"url\":\"https://arxiv.org/abs/2603.03239\",\"title\":\"COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Data\",\"relevance\":\"high\",\"snippet\":\"The strongest recent evidence for the exact mechanism being asked about: each modality is a sequence of latent tokens processed by one shared transformer with INDEPENDENT per-modality diffusion timesteps (timestep embeddings/token…" }, { "type": "workflow_agent", "index": 7, "label": "fetch:arxiv.org", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "a2cfd02fb594f59a0", "model": "claude-fable-5", "state": "done", "startedAt": 1786399487700, "queuedAt": 1786399486320, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2025-05-01 (v1); last revised 2025-08-05 (v3); ICCV 2025", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399572172, "tokens": 27623, "toolCalls": 4, "durationMs": 84471, "resultPreview": "{\"claims\":[{\"claim\":\"JointDiT's unbalanced timestep sampling: with 50% probability, t_x and t_y are sampled independently from two skewed densities f(t) (built from Flux's shifted logit-normal with s=3.1582) and g(t) (same form with s=0.25); with the remaining 50% probability, a single timestep drawn from f(t) is assigned to both modalities (diagonal sampling). The density has the form f(t) = 1 − …" }, { "type": "workflow_agent", "index": 8, "label": "fetch:openaccess.thecvf.com", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "acb3f1d243f7e5179", "model": "claude-fable-5", "state": "done", "startedAt": 1786399495742, "queuedAt": 1786399486320, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2025-10 (ICCV 2025 proceedings, CVF Open Access version, pp…", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399668848, "tokens": 60179, "toolCalls": 13, "durationMs": 173106, "resultPreview": "{\"claims\":[{\"claim\":\"JointDiT's adaptive scheduling weights gate the joint cross-attention OUTPUTS (features), not the loss: Gx = Attn(Sx) + wx(tx,ty)·JointAttn(Sx,Sy) with wx = sigmoid(α(ty/(tx+ty) − 1/2)), wy = sigmoid(α(tx/(tx+ty) − 1/2)), and α=3 in all experiments; the weight is larger for the noisier branch (note the paper's convention is t=1 clean / t=0 noise, so the formula's sign must be …" }, { "type": "workflow_agent", "index": 9, "label": "fetch:openaccess.thecvf.com", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "a73f60c1688d0510c", "model": "claude-fable-5", "state": "done", "startedAt": 1786399487725, "queuedAt": 1786399486321, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "primary", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399616120, "tokens": 40523, "toolCalls": 9, "durationMs": 128395, "resultPreview": "{\"claims\":[{\"claim\":\"JointDiT trains with a 50/50 mixture of timestep regimes: with 50% probability the two modality timesteps tx and ty are sampled independently from two different distributions f(t) and g(t) (with the f/g assignment randomly swapped), and with the remaining 50% probability a single shared timestep sampled from f(t) is used for both modalities — i.e., a diagonal/independent mixtu…" }, { "type": "workflow_agent", "index": 10, "label": "fetch:byungki-k.github.io", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "a3b314d8ce62e5c3a", "model": "claude-fable-5", "state": "done", "startedAt": 1786399487818, "queuedAt": 1786399486321, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2025-10 (ICCV 2025 proceedings, October 2025)", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399545012, "tokens": 26032, "toolCalls": 4, "durationMs": 57193, "resultPreview": "{\"claims\":[{\"claim\":\"JointDiT achieves all three inference modes — joint RGB+depth generation, depth estimation (depth-given-image), and depth-conditioned image generation — purely by setting the timestep of each modality branch at inference, with no per-task retraining; this is the exact 'dial per-modality timesteps to switch modes' mechanism the unified shape+tex SLAT DiT needs.\",\"quote\":\"JointD…" }, { "type": "workflow_agent", "index": 11, "label": "fetch:themoonlight.io", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "a64e2a59929c549a2", "model": "claude-fable-5", "state": "done", "startedAt": 1786399488398, "queuedAt": 1786399486321, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "secondary", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399535027, "tokens": 25928, "toolCalls": 3, "durationMs": 46628, "resultPreview": "{\"claims\":[{\"claim\":\"JointDiT's training uses a 50/50 mixture: with half probability, timesteps t_x and t_y are sampled independently from two unbalanced distributions f(t) and g(t); with the other half probability, a single timestep sampled from f(t) is assigned to both modalities (diagonal/balanced mode).\",\"quote\":\"This strategy samples timesteps tx and ty independently from two unbalanced times…" }, { "type": "workflow_agent", "index": 12, "label": "fetch:huggingface.co", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "a5d41428ba8cb9850", "model": "claude-fable-5", "state": "done", "startedAt": 1786399487935, "queuedAt": 1786399486321, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2025-05-01 (arXiv 2505.00482, v1 May 2025; HF page displays…", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399567705, "tokens": 26637, "toolCalls": 4, "durationMs": 79770, "resultPreview": "{\"claims\":[{\"claim\":\"JointDiT trains with a 50/50 mixture: half the time t_x and t_y are sampled independently from two unbalanced distributions f(t) and g(t), and the other half a single timestep sampled from f(t) is assigned to both modalities (the diagonal t_x=t_y case).\",\"quote\":\"the unbalanced timestep sampling strategy samples t_x and t_y independently from two unbalanced timestep distributi…" }, { "type": "workflow_agent", "index": 13, "label": "fetch:arxiv.org", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "abd0f7cd92ae685f7", "model": "claude-fable-5", "state": "done", "startedAt": 1786399502955, "queuedAt": 1786399501559, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2023-03-12 (v1; v2 2023-05-30, ICML 2023)", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399584045, "tokens": 27284, "toolCalls": 5, "durationMs": 81090, "resultPreview": "{\"claims\":[{\"claim\":\"UniDiffuser trains a single noise-prediction network over ALL modality-timestep pairs by sampling the two per-modality timesteps independently and uniformly from {1,...,T} — no diagonal/corner mixture or unbalanced density is used — and the loss is a single joint L2 on the concatenated noise of both modalities (Eq. 5).\",\"quote\":\"tˣ and tʸ are uniformly sampled from {1,2,…,T} i…" }, { "type": "workflow_agent", "index": 14, "label": "fetch:proceedings.mlr.press", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "aae68261b1b40f673", "model": "claude-fable-5", "state": "done", "startedAt": 1786399504741, "queuedAt": 1786399501559, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2023-07 (ICML 2023, PMLR v202)", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399646148, "tokens": 51625, "toolCalls": 14, "durationMs": 141406, "resultPreview": "{\"claims\":[{\"claim\":\"UniDiffuser trains with per-modality timesteps sampled INDEPENDENTLY and UNIFORMLY from {1,...,T} (Algorithm 1, loss Eq. 5) — there is no diagonal/independent mixture, no corner oversampling, and no special weighting; a single forward-backward covers all distributions per update.\",\"quote\":\"tx and ty are uniformly sampled from {1, 2, . . . , T} independently. We call our method…" }, { "type": "workflow_agent", "index": 15, "label": "fetch:github.com", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "a61b394275875bed4", "model": "claude-fable-5", "state": "done", "startedAt": 1786399503234, "queuedAt": 1786399501560, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2023-03 (paper arXiv 2303.06555, code released March 2023)", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399566824, "tokens": 27669, "toolCalls": 5, "durationMs": 63590, "resultPreview": "{\"claims\":[{\"claim\":\"UniDiffuser injects per-modality timesteps as prepended sequence TOKENS (one learned embedding per modality via timestep_embedding -> MLP -> unsqueeze), not via AdaLN modulation; the transformer input sequence order is [t_img_token, t_text_token, data_type token, text, clip_img, img].\",\"quote\":\"t_img_token = self.time_img_embed(timestep_embedding(t_img, self.embed_dim)) ... t_…" }, { "type": "workflow_agent", "index": 16, "label": "fetch:huggingface.co", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "a67fd12ab5456f89f", "model": "claude-fable-5", "state": "done", "startedAt": 1786399503450, "queuedAt": 1786399501560, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "primary", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399561905, "tokens": 34515, "toolCalls": 4, "durationMs": 58454, "resultPreview": "{\"claims\":[{\"claim\":\"UniDiffuser's core mechanism is that marginal, conditional, and joint diffusion modeling are unified as noise prediction on perturbed data where each modality gets its own independent timestep (perturbation level) — directly supporting the 'iso-generation' design of dialing per-modality timesteps to switch between joint, marginal, and conditional shape/texture generation.\",\"qu…" }, { "type": "workflow_agent", "index": 17, "label": "fetch:openreview.net", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "acf8f921d0eb3b2c0", "model": "claude-fable-5", "state": "done", "startedAt": 1786399503002, "queuedAt": 1786399501560, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "unreliable", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399686196, "tokens": 29780, "toolCalls": 11, "durationMs": 183193, "resultPreview": "{\"claims\":[],\"sourceQuality\":\"unreliable\"}" }, { "type": "workflow_agent", "index": 18, "label": "fetch:arxiv.org", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "aa706cd42d5bfbc8a", "model": "claude-fable-5", "state": "done", "startedAt": 1786399529790, "queuedAt": 1786399501560, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2025-05-01", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399649792, "tokens": 30386, "toolCalls": 6, "durationMs": 120002, "resultPreview": "{\"claims\":[{\"claim\":\"JointDiT's unbalanced timestep sampling uses the density f(t)=σ(z)·s/(1+(s−1)·σ(z)) with z~N(0,1), with shift constant s=3.1582 for f(t) and s=0.25 for g(t); with 50% probability t_x and t_y are sampled independently from f and g (or the swapped assignment), and for the remaining 50% both branches share the same timestep sampled from f. No explicit corner sampling (forcing one…" }, { "type": "workflow_agent", "index": 19, "label": "fetch:arxiv.org", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "aa656c332ef9ed3f7", "model": "claude-fable-5", "state": "done", "startedAt": 1786399539827, "queuedAt": 1786399528273, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2026-03-03 (v1 submitted; v3 revised 2026-05-11)", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399614613, "tokens": 28002, "toolCalls": 5, "durationMs": 74786, "resultPreview": "{\"claims\":[{\"claim\":\"COP-GEN injects per-modality timesteps as prepended sequence tokens (sinusoidal timestep embeddings turned into dedicated tokens), not via per-branch AdaLN — each modality carries its own independent diffusion timestep t(i).\",\"quote\":\"Each modality is assigned its own diffusion timestep t(i), encoded via sinusoidal timestep embeddings and prepended as dedicated tokens.\",\"impor…" }, { "type": "workflow_agent", "index": 20, "label": "fetch:arxiv.org", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "a9d9fb14913fd0fac", "model": "claude-fable-5", "state": "done", "startedAt": 1786399549452, "queuedAt": 1786399528273, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2025-04-11 (v1; v2 2025-04-14; CVPR 2025 Workshop MORSE)", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399632125, "tokens": 27643, "toolCalls": 5, "durationMs": 82673, "resultPreview": "{\"claims\":[{\"claim\":\"COP-GEN-Beta injects per-modality timesteps as in-context tokens: each modality's timestep t(i) is embedded by a small modality-specific MLP and the resulting embeddings are prepended to the patch-token sequence (rather than injected via AdaLN), forming one concatenated transformer input [e1,...,em, h1,...,hm].\",\"quote\":\"timestamps t(i) are processed through ϕ(i) (a small MLP)…" }, { "type": "workflow_agent", "index": 21, "label": "fetch:arxiv.org", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "ad1e8b1a5a52e41e2", "model": "claude-fable-5", "state": "done", "startedAt": 1786399557987, "queuedAt": 1786399528273, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2025-01-22 (v1); v2 2025-08-23; accepted ICCV 2025", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399633872, "tokens": 27194, "toolCalls": 4, "durationMs": 75885, "resultPreview": "{\"claims\":[{\"claim\":\"Orchid does NOT use per-modality timesteps: it encodes RGB, depth, and normals into a single shared latent space via a joint VAE (7 input channels: 3 RGB, 1 depth, 3 normals; latent dim 8, 8x downsampling) and denoises that one latent in a single diffusion process with one shared timestep. This makes it the shared-latent alternative to the iso-generation / per-modality-timeste…" }, { "type": "workflow_agent", "index": 22, "label": "fetch:arxiv.org", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "a695affb903eedfb4", "model": "claude-fable-5", "state": "done", "startedAt": 1786399565426, "queuedAt": 1786399528273, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2025-09-29", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399649172, "tokens": 27476, "toolCalls": 4, "durationMs": 83745, "resultPreview": "{\"claims\":[{\"claim\":\"UniLat3D trains a single flow-matching DiT over a fused geometry+appearance latent using ONE shared timestep t for both modalities (velocity-prediction CFM loss on the unified latent), with no per-modality timestep mechanism — demonstrating a competing 'unify the latent' route that forgoes the per-modality-timestep dial entirely.\",\"quote\":\"L_CFM(θ) = E_{t,x_0,ε} ||v(x_uni, t) …" }, { "type": "workflow_agent", "index": 23, "label": "fetch:arxiv.org", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "af840e255a81eff1a", "model": "claude-fable-5", "state": "done", "startedAt": 1786399571035, "queuedAt": 1786399556003, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2024-07-01 (v1; latest revision v4 2024-12-10)", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399674916, "tokens": 27635, "toolCalls": 6, "durationMs": 103881, "resultPreview": "{\"claims\":[{\"claim\":\"Diffusion Forcing trains a single diffusion model to denoise a set of tokens where each token carries its own independently sampled noise level (sampled uniformly and independently per token during training), which is exactly the training-time mechanism needed for per-modality timesteps in a unified shape+texture DiT.\",\"quote\":\"This paper presents Diffusion Forcing, a new trai…" }, { "type": "workflow_agent", "index": 24, "label": "fetch:arxiv.org", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "ac57e14ed396363a0", "model": "claude-fable-5", "state": "done", "startedAt": 1786399570885, "queuedAt": 1786399556003, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2025-02-10 (v1; v2 2025-07-24, ICML 2025)", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399689679, "tokens": 29790, "toolCalls": 7, "durationMs": 118794, "resultPreview": "{\"claims\":[{\"claim\":\"Training with an independent noise level (timestep) per frame/token is theoretically valid: DFoT assigns every frame its own noise level k_t ∈ [0,1] sampled independently, and Theorem 4.1 proves this objective still optimizes a (reweighted) evidence lower bound on the expected log-likelihoods — the formal justification that mixed per-modality timesteps (shape t_s ≠ texture t_x…" }, { "type": "workflow_agent", "index": 25, "label": "fetch:github.com", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "ab2c67a910a5e03a8", "model": "claude-fable-5", "state": "done", "startedAt": 1786399578609, "queuedAt": 1786399577026, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2024-10 (arXiv 2410.11439); published at ICLR 2025", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399725066, "tokens": 41975, "toolCalls": 11, "durationMs": 146457, "resultPreview": "{\"claims\":[{\"claim\":\"UniCon's cross-stream connection is a zero-initialized residual branch: each transformer block's self-attention is duplicated (attn1n = deepcopy(attn1)) to attend from one stream's tokens to the other stream's tokens, its output is passed through a zero-initialized nn.Linear ('conv' post-projection), and added back residually with joint_scale=1.0 — so at initialization the joi…" }, { "type": "workflow_agent", "index": 26, "label": "fetch:arxiv.org", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "afc01503af9674887", "model": "claude-fable-5", "state": "done", "startedAt": 1786399578682, "queuedAt": 1786399577026, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "2024-10-15 (v1), revised 2025-04-05 (v3)", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399645030, "tokens": 26919, "toolCalls": 4, "durationMs": 66347, "resultPreview": "{\"claims\":[{\"claim\":\"UniCon trains a single joint diffusion model over a correlated pair (image x, condition y) by sampling each modality's diffusion timestep independently during training, with the network taking both noisy inputs and both timesteps (loss: ||eps - eps_theta(x_tx, y_ty, tx, ty)||^2, Eq. 6); no special non-uniform timestep-pair distribution or explicit corner-case (t=0/t=T) samplin…" }, { "type": "workflow_agent", "index": 27, "label": "fetch:github.com", "phaseIndex": 3, "phaseTitle": "Fetch", "agentId": "a3ac3053bcbe21c54", "model": "claude-fable-5", "state": "done", "startedAt": 1786399587471, "queuedAt": 1786399577026, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "primary", "promptPreview": "## Source Extractor\n\nResearch question: \"HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidden] f…", "lastProgressAt": 1786399662470, "tokens": 26542, "toolCalls": 4, "durationMs": 74999, "resultPreview": "{\"claims\":[{\"claim\":\"UniCon's cross-stream connection output projection is zero-initialized: a linear layer conv1n is passed through a zero_module() helper that sets all parameters to zero, so the joint connection contributes exactly nothing at initialization — enabling warm-start from pretrained specialist weights without perturbing them.\",\"quote\":\"def zero_module(module):\\n for p in module.pa…" }, { "type": "workflow_agent", "index": 28, "label": "v0:JointDiT's unbalanced timestep sampling:", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a77781bcd00d4ec26", "model": "claude-fable-5", "state": "done", "startedAt": 1786399728995, "queuedAt": 1786399727320, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "The 50/50 mixture and shift values are accurate, but the cl…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400066884, "tokens": 55549, "toolCalls": 12, "durationMs": 337889, "resultPreview": "{\"refuted\":true,\"evidence\":\"The 50/50 mixture and shift values are accurate, but the claimed density formula is wrong. Verified against raw arXiv HTML of BOTH v1 and v3 (downloaded and byte-grepped): Eq. (7) in Appendix A.3 reads exactly \\\"f(t)=\\\\frac{\\\\sigma(z)\\\\cdot s}{1+(s-1)\\\\cdot\\\\sigma(z)},\\\\quad\\\\text{where }z\\\\sim\\\\mathcal{N}(0,1)\\\" — there is NO \\\"1 −\\\" prefix (zero occurrences of \\\"=1-\\\"…" }, { "type": "workflow_agent", "index": 29, "label": "v1:JointDiT's unbalanced timestep sampling:", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "ae7d5ea2531d86db1", "model": "claude-fable-5", "state": "done", "startedAt": 1786399729196, "queuedAt": 1786399727320, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against primary source (arXiv HTML v1, Section A.3…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399843970, "tokens": 31035, "toolCalls": 10, "durationMs": 114774, "resultPreview": "{\"refuted\":true,\"evidence\":\"Verified against primary source (arXiv HTML v1, Section A.3, LaTeX alt text extracted directly): Eq. 7 reads \\\"f(t) = \\\\frac{\\\\sigma(z)\\\\cdot s}{1+(s-1)\\\\cdot\\\\sigma(z)}, where z ~ N(0,1)\\\" — WITHOUT the \\\"1 −\\\" prefix the claim asserts. The claim's stated density f(t) = 1 − s·σ(z)/(1+(s−1)·σ(z)) inverts the skew direction (paper's form with s=3.1582 skews toward high t…" }, { "type": "workflow_agent", "index": 30, "label": "v2:JointDiT's unbalanced timestep sampling:", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a09035dfab294402e", "model": "claude-fable-5", "state": "done", "startedAt": 1786399729271, "queuedAt": 1786399727320, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "The procedural half of the claim is confirmed verbatim by t…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399840627, "tokens": 33866, "toolCalls": 7, "durationMs": 111356, "resultPreview": "{\"refuted\":true,\"evidence\":\"The procedural half of the claim is confirmed verbatim by the primary source, but the stated equation is contradicted by the paper's actual Eq. 7. Verified against the raw arXiv HTML (LaTeX alttext) of 2505.00482: Appendix A.3 gives Eq. 7 as f(t) = σ(z)·s / (1+(s−1)·σ(z)), where z~N(0,1) — with NO \\\"1 −\\\" prefix — followed by \\\"s is set to 3.1582. We set s to 0.25 to ob…" }, { "type": "workflow_agent", "index": 31, "label": "v0:JointDiT's adaptive scheduling weights u", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a57b8f657bfc7f5d9", "model": "claude-fable-5", "state": "done", "startedAt": 1786399728929, "queuedAt": 1786399727330, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against raw arXiv HTML of 2505.00482 (JointDiT, IC…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399810239, "tokens": 35367, "toolCalls": 7, "durationMs": 81310, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against raw arXiv HTML of 2505.00482 (JointDiT, ICCV 2025), grepped independently of any LLM summarizer. Eq. 6 verbatim LaTeX: w_x(t_x,t_y)=sigmoid(α(t_y/(t_x+t_y)−1/2)), w_y(t_x,t_y)=sigmoid(α(t_x/(t_x+t_y)−1/2)); paper states verbatim \\\"We set α to 3 for all experiments.\\\" Eq. 5 verbatim: G_x = Attn(S_x) + w_x(t_x,t_y)·JointAttn(S_x,S_y) — the weight multipl…" }, { "type": "workflow_agent", "index": 32, "label": "v1:JointDiT's adaptive scheduling weights u", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a9fb94ea48e3d7b00", "model": "claude-fable-5", "state": "done", "startedAt": 1786399728949, "queuedAt": 1786399727330, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against raw LaTeX extracted from the arXiv HTML (h…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399931191, "tokens": 42428, "toolCalls": 14, "durationMs": 202242, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against raw LaTeX extracted from the arXiv HTML (https://arxiv.org/html/2505.00482v1), not a summary: Eq. 5 reads exactly \\\"G_x = Attn(S_x) + w_x(t_x,t_y)·JointAttn(S_x,S_y)\\\" (symmetric for G_y), and Eq. 6 reads exactly \\\"w_x(t_x,t_y) = sigmoid(α(t_y/(t_x+t_y) − 1/2))\\\", \\\"w_y(t_x,t_y) = sigmoid(α(t_x/(t_x+t_y) − 1/2))\\\"; paper text states verbatim \\\"We set α…" }, { "type": "workflow_agent", "index": 33, "label": "v2:JointDiT's adaptive scheduling weights u", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a7b918bd9e5234062", "model": "claude-fable-5", "state": "done", "startedAt": 1786399729145, "queuedAt": 1786399727330, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against primary source and official code. (1) Neut…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399881570, "tokens": 39346, "toolCalls": 10, "durationMs": 152425, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against primary source and official code. (1) Neutral verbatim transcription of arXiv HTML 2505.00482v2 (no formula leaked in the fetch prompt) reproduces exactly: Eq.6 \\\"w_x(t_x,t_y) = sigmoid(α(t_y/(t_x+t_y) − 1/2))\\\", \\\"w_y(t_x,t_y) = sigmoid(α(t_x/(t_x+t_y) − 1/2))\\\", the sentence \\\"We set α to 3 for all experiments\\\", Eq.5 \\\"G_x = Attn(S_x) + w_x(t_x,t_y)…" }, { "type": "workflow_agent", "index": 34, "label": "v0:JointDiT is built by freezing a pre-trai", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a06ba287d4fca2093", "model": "claude-fable-5", "state": "done", "startedAt": 1786399729208, "queuedAt": 1786399727338, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified directly against the primary source, arXiv 2505.00…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399828686, "tokens": 30304, "toolCalls": 8, "durationMs": 99478, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified directly against the primary source, arXiv 2505.00482 v3 (ICCV 2025). Every specific element of the claim appears verbatim in the paper: (1) frozen backbone — \\\"We train the LoRAs and joint connection modules while keeping the pre-trained backbone model frozen\\\"; the backbone is Flux (MM-DiT + P-DiT blocks) with a \\\"parallel depth branch alongside the pre-trai…" }, { "type": "workflow_agent", "index": 35, "label": "v1:JointDiT is built by freezing a pre-trai", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a67a76a6245df41ab", "model": "claude-fable-5", "state": "done", "startedAt": 1786399729251, "queuedAt": 1786399727338, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Every component verified against primary sources. (1) Paper…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399854365, "tokens": 37904, "toolCalls": 12, "durationMs": 125114, "resultPreview": "{\"refuted\":false,\"evidence\":\"Every component verified against primary sources. (1) Paper (arXiv 2505.00482 v1/v2 full text): JointDiT is built on Flux, backbone frozen — exact quote confirmed: \\\"We train the LoRAs and joint connection modules while keeping the pre-trained backbone model frozen\\\"; parallel depth branch alongside RGB branch confirmed; \\\"We consistently use the LoRA rank of 64\\\" with…" }, { "type": "workflow_agent", "index": 36, "label": "v2:JointDiT is built by freezing a pre-trai", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a0edfff10b5ea9bc0", "model": "claude-fable-5", "state": "done", "startedAt": 1786399729293, "queuedAt": 1786399727338, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against raw HTML of arXiv 2505.00482 (downloaded a…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399828382, "tokens": 44662, "toolCalls": 8, "durationMs": 99089, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against raw HTML of arXiv 2505.00482 (downloaded and grepped both v1 and v3). Every element of the claim appears verbatim in the latest revision (v3): (1) frozen backbone — \\\"We train the LoRAs and joint connection modules while keeping the pre-trained backbone model frozen\\\" (Sec. 3, matches supporting quote exactly); backbone is Flux — LoRA table lists \\\"the…" }, { "type": "workflow_agent", "index": 37, "label": "v0:Task switching is done purely by dialing", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "ac0e6146061d2aa6e", "model": "claude-fable-5", "state": "done", "startedAt": 1786399728888, "queuedAt": 1786399727360, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verbatim confirmed in the primary source (arXiv HTML 2505.0…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399805034, "tokens": 29970, "toolCalls": 5, "durationMs": 76145, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verbatim confirmed in the primary source (arXiv HTML 2505.00482v1): \\\"Initially setting t_x=0, t_y=0 leads to the joint generation of both images and depth maps. When t_x=1, t_y=0, it performs depth estimation from a given image, and when t_x=0, t_y=1, it becomes a depth-conditioned image generation,\\\" plus \\\"various tasks can be performed simply by adjusting t_x and t…" }, { "type": "workflow_agent", "index": 38, "label": "v1:Task switching is done purely by dialing", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a4ad13f12393bbc5f", "model": "claude-fable-5", "state": "done", "startedAt": 1786399729395, "queuedAt": 1786399727360, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verbatim quote confirmed in JointDiT full text (arXiv 2505.…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399819941, "tokens": 29868, "toolCalls": 5, "durationMs": 90546, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verbatim quote confirmed in JointDiT full text (arXiv 2505.00482, ICCV 2025): \\\"initially setting t_x=0, t_y=0 leads to the joint generation of both images and depth maps. When t_x=1, t_y=0, it performs depth estimation from a given image, and when t_x=0, t_y=1, it becomes a depth-conditioned image generation.\\\" The abstract independently states tasks are handled \\\"by …" }, { "type": "workflow_agent", "index": 39, "label": "v2:Task switching is done purely by dialing", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a440cd96be5095156", "model": "claude-fable-5", "state": "done", "startedAt": 1786399729528, "queuedAt": 1786399727360, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source (arXiv 2505.00482, Join…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399803236, "tokens": 29596, "toolCalls": 5, "durationMs": 73708, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source (arXiv 2505.00482, JointDiT, published at ICCV 2025). (1) The quote is genuine and verbatim in the paper (https://arxiv.org/html/2505.00482): \\\"Initially setting t_x=0, t_y=0 leads to the joint generation of both images and depth maps... When t_x=1, t_y=0, it performs depth estimation from a given image... When t_x=0, t_y=1, it becom…" }, { "type": "workflow_agent", "index": 40, "label": "v0:JointDiT's adaptive scheduling weights g", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "acb2c29f4677f816e", "model": "claude-fable-5", "state": "done", "startedAt": 1786399729366, "queuedAt": 1786399727368, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified directly against the primary source via two indepe…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399838143, "tokens": 30999, "toolCalls": 7, "durationMs": 108777, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified directly against the primary source via two independent fetches of arXiv 2505.00482 (JointDiT, ICCV 2025), including one neutral-prompt fetch that did not contain the claimed formulas. The paper's Section 3.1 states verbatim: Gx = Attn(Sx) + wx(tx,ty)·JointAttn(Sx,Sy) and Gy = Attn(Sy) + wy(tx,ty)·JointAttn(Sx,Sy), with wx(tx,ty) = sigmoid(α(ty/(tx+ty) − 1/2))…" }, { "type": "workflow_agent", "index": 41, "label": "v1:JointDiT's adaptive scheduling weights g", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "af10e34424364f1d3", "model": "claude-fable-5", "state": "done", "startedAt": 1786399729054, "queuedAt": 1786399727368, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source: arXiv HTML of JointDiT…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399816051, "tokens": 30612, "toolCalls": 6, "durationMs": 86997, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source: arXiv HTML of JointDiT (2505.00482, identical paper to the cited ICCV 2025 CVF PDF, which returned 403). The paper contains verbatim: Eq.5 \\\"Gx = Attn(Sx)+wx(tx,ty)·JointAttn(Sx,Sy), Gy = Attn(Sy)+wy(tx,ty)·JointAttn(Sx,Sy)\\\"; Eq.6 \\\"wx(tx,ty)=sigmoid(α(ty/(tx+ty)−1/2)), wy(tx,ty)=sigmoid(α(tx/(tx+ty)−1/2))\\\"; \\\"We set α to 3 for al…" }, { "type": "workflow_agent", "index": 42, "label": "v2:JointDiT's adaptive scheduling weights g", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "aed74ab8fa4a4a624", "model": "claude-fable-5", "state": "done", "startedAt": 1786399809958, "queuedAt": 1786399727368, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Primary source verified verbatim via arXiv HTML v2/v3 of 25…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399908144, "tokens": 30228, "toolCalls": 7, "durationMs": 98186, "resultPreview": "{\"refuted\":false,\"evidence\":\"Primary source verified verbatim via arXiv HTML v2/v3 of 2505.00482 (identical content to the ICCV 2025 camera-ready): \\\"Gx = Attn(Sx) + wx(tx,ty)·JointAttn(Sx,Sy)\\\", \\\"Gy = Attn(Sy) + wy(tx,ty)·JointAttn(Sx,Sy)\\\" — weights multiply the joint cross-attention OUTPUTS (architectural feature gating; no loss-weighting formulation exists in the paper). Formulas confirmed ex…" }, { "type": "workflow_agent", "index": 43, "label": "v0:The unbalanced timestep sampling is an e", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a28bd171965338b40", "model": "claude-fable-5", "state": "done", "startedAt": 1786399808186, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source by downloading and text…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400083210, "tokens": 54731, "toolCalls": 16, "durationMs": 275023, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source by downloading and text-extracting both official ICCV 2025 PDFs (saved at /tmp/claude-3166/-fsx-home-weikai-huang-3dgen-model/f40d8738-3613-4757-94ae-75f2e929bda9/scratchpad/jointdit_iccv.txt and jointdit_supp.txt). The supplementary Sec. A.3 contains the quoted passage VERBATIM, including the disputed '1−' prefix: \\\"f(t) = 1− σ(z)·s…" }, { "type": "workflow_agent", "index": 44, "label": "v1:The unbalanced timestep sampling is an e", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a38e0002195f39307", "model": "claude-fable-5", "state": "done", "startedAt": 1786399813274, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified verbatim against the primary source (arXiv 2505.00…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399977658, "tokens": 49441, "toolCalls": 12, "durationMs": 164383, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified verbatim against the primary source (arXiv 2505.00482v1 HTML, downloaded and grepped directly; ICCV PDF is 403-blocked). Main body Sec 3.1: \\\"samples t_x and t_y independently from two unbalanced timestep distributions, f(t) and g(t), with half probability during training. For the remaining half, the same timesteps sampled from f(t) are assigned to t_x and t_y…" }, { "type": "workflow_agent", "index": 45, "label": "v2:The unbalanced timestep sampling is an e", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a4550262828c49add", "model": "claude-fable-5", "state": "done", "startedAt": 1786399819757, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against three independent primary artifacts. (1) C…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400161055, "tokens": 56178, "toolCalls": 27, "durationMs": 341298, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against three independent primary artifacts. (1) CVF camera-ready supplemental §A.3 (downloaded, text-extracted) contains the quote VERBATIM: \\\"f (t) = 1− σ(z)·s / (1 + (s−1)·σ(z)), where z ∼ N(0,1)\\\" with \\\"s is set to 3.1582\\\" (\\\"suggested by our base training code\\\", footnote = kohya-ss/sd-scripts sd3 branch) and \\\"s to 0.25 to obtain g(t)\\\", plus the exact…" }, { "type": "workflow_agent", "index": 46, "label": "v0:The cross-stream connection is a UniCon-", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "ad39154000f8a812b", "model": "claude-fable-5", "state": "done", "startedAt": 1786399824502, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified verbatim against the downloaded ICCV 2025 primary …", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399999740, "tokens": 38782, "toolCalls": 14, "durationMs": 175237, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified verbatim against the downloaded ICCV 2025 primary sources. (1) The supporting quote appears word-for-word in the JointDiT ICCV 2025 SUPPLEMENTARY PDF, Sec. A.4 (openaccess.thecvf.com/content/ICCV2025/supplemental/Byung-Ki_JointDiT_Enhancing_RGB-Depth_ICCV_2025_supplemental.pdf): LoRA rank 64 for MM-DiT and P-DiT, ranks 512/1024 at input stage, alpha = half the…" }, { "type": "workflow_agent", "index": 47, "label": "v1:The cross-stream connection is a UniCon-", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a089a17d843d192f9", "model": "claude-fable-5", "state": "done", "startedAt": 1786399831509, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified directly against the primary sources (ICCV 2025 PD…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399994255, "tokens": 39572, "toolCalls": 17, "durationMs": 162746, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified directly against the primary sources (ICCV 2025 PDF downloaded + arXiv 2505.00482 full version with appendix). Every sub-claim checks out: (1) Appendix A.4 states verbatim: \\\"To design the joint connection module, we adopt the joint cross-attention module from UniCon [35], followed by a zero-initialized linear projection layer. The adaptive scheduling weight i…" }, { "type": "workflow_agent", "index": 48, "label": "v2:The cross-stream connection is a UniCon-", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a184de425cd109826", "model": "claude-fable-5", "state": "done", "startedAt": 1786399831693, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the downloaded ICCV 2025 camera-ready PDF …", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400009620, "tokens": 44221, "toolCalls": 15, "durationMs": 177927, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the downloaded ICCV 2025 camera-ready PDF and supplemental PDF (text-extracted, not paraphrased from search). (1) Supplemental Sec. A.4 contains the supporting quote verbatim: \\\"We use a LoRA rank of 64 for both MM-DiT and P-DiT, and apply relatively larger ranks of 512 or 1024 to the input stage. The alpha value is set to half of the corresponding ran…" }, { "type": "workflow_agent", "index": 49, "label": "v0:All combinatorial tasks are performed pu", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a748012538837197a", "model": "claude-fable-5", "state": "done", "startedAt": 1786399840950, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the arXiv HTML full text (2505.00482, iden…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399943338, "tokens": 31850, "toolCalls": 7, "durationMs": 102388, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the arXiv HTML full text (2505.00482, identical to the ICCV 2025 CVF PDF): (1) the supporting quote is verbatim-genuine — tx=0,ty=0 joint generation; tx=1,ty=0 depth estimation from a given image; tx=0,ty=1 depth-conditioned image generation, \\\"without any additional guidance\\\"; (2) the convention t=1=clean is confirmed by the preliminaries: \\\"p0 repre…" }, { "type": "workflow_agent", "index": 50, "label": "v1:All combinatorial tasks are performed pu", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "ad88fa8495dcc61ec", "model": "claude-fable-5", "state": "done", "startedAt": 1786399843759, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified directly against the primary source (JointDiT, ICC…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399935938, "tokens": 29941, "toolCalls": 6, "durationMs": 92179, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified directly against the primary source (JointDiT, ICCV 2025; arXiv 2505.00482 v2/v3 HTML, since the CVF PDF returns 403). (1) The supporting quote is verbatim in the paper and the claim's task mapping matches exactly: \\\"various tasks can be performed simply by adjusting tx and ty without any additional guidance. For example, initially setting tx=0,ty=0 leads to t…" }, { "type": "workflow_agent", "index": 51, "label": "v2:All combinatorial tasks are performed pu", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a5d6f451086ec7ab2", "model": "claude-fable-5", "state": "done", "startedAt": 1786399846997, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source (arXiv 2505.00482 HTML,…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399951887, "tokens": 30520, "toolCalls": 7, "durationMs": 104890, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source (arXiv 2505.00482 HTML, same paper as the ICCV 2025 PDF which returned 403). (a) The task-by-timestep mapping is quoted verbatim in the paper: \\\"various tasks can be performed simply by adjusting tx and ty without any additional guidance\\\"; tx=0,ty=0 -> joint generation; tx=1,ty=0 -> \\\"depth estimation from a given image\\\"; tx=0,ty=1…" }, { "type": "workflow_agent", "index": 52, "label": "v0:JointDiT trains with a 50/50 mixture of", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a0e3a09efc9680fd3", "model": "claude-fable-5", "state": "done", "startedAt": 1786399857695, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source directly: downloaded th…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400123127, "tokens": 33536, "toolCalls": 14, "durationMs": 265431, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source directly: downloaded the ICCV 2025 supplemental PDF (complete, 12,968,926 bytes = server Content-Length) and extracted section A.3 \\\"Unbalanced Timestep Sampling Strategy\\\", which states verbatim: \\\"the timesteps, i.e., tx and ty, are separately sampled from the timestep distributions f(t) and g(t), respectively, or vice versa. This …" }, { "type": "workflow_agent", "index": 53, "label": "v1:JointDiT trains with a 50/50 mixture of", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "ab0c87603bd81e072", "model": "claude-fable-5", "state": "done", "startedAt": 1786399884630, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Triple-verified against primary sources. (1) Downloaded the…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400060338, "tokens": 40453, "toolCalls": 17, "durationMs": 175707, "resultPreview": "{\"refuted\":false,\"evidence\":\"Triple-verified against primary sources. (1) Downloaded the ICCV 2025 supplemental PDF directly (curl, since WebFetch got 403) and extracted section A.3: the supporting quote is verbatim accurate — \\\"separately sampled from the timestep distributions f(t) and g(t), respectively, or vice versa. This is applied with a 50% probability during training, while for the remain…" }, { "type": "workflow_agent", "index": 54, "label": "v2:JointDiT trains with a 50/50 mixture of", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "aedc7c9074e93f36d", "model": "claude-fable-5", "state": "done", "startedAt": 1786399912968, "queuedAt": 1786399727374, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source (arXiv 2505.00482 HTML,…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786399990439, "tokens": 29113, "toolCalls": 6, "durationMs": 77471, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source (arXiv 2505.00482 HTML, identical text to ICCV 2025 supplemental Sec. A.3). Exact quote confirmed: \\\"the timesteps, i.e., tx and ty, are separately sampled from the timestep distributions f(t) and g(t), respectively, or vice versa. This is applied with a 50% probability during training, while for the remaining 50%, the same timestep …" }, { "type": "workflow_agent", "index": 55, "label": "v0:The exact unbalanced sampling density is", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "aea9e6254633a42ce", "model": "claude-fable-5", "state": "done", "startedAt": 1786399934632, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Downloaded the actual CVF openaccess supplemental PDF and e…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400038877, "tokens": 42937, "toolCalls": 9, "durationMs": 104245, "resultPreview": "{\"refuted\":false,\"evidence\":\"Downloaded the actual CVF openaccess supplemental PDF and extracted full text with pypdf. Section A.3, page 2, contains the quote verbatim: \\\"f (t) = 1− σ(z) · s / (1 + (s − 1) · σ(z)), where z ∼ N(0, 1). (1) The σ(·) denotes the sigmoid function. In f (t), which is suggested by our base training code†, s is set to 3.1582. We set s to 0.25 to obtain g(t).\\\" — with foot…" }, { "type": "workflow_agent", "index": 56, "label": "v1:The exact unbalanced sampling density is", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a25b6e3dfd356f4b4", "model": "claude-fable-5", "state": "done", "startedAt": 1786399940466, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source directly, not just the …", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400041851, "tokens": 30689, "toolCalls": 9, "durationMs": 101385, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source directly, not just the claim's quote: downloaded the ICCV 2025 supplemental PDF from openaccess.thecvf.com and extracted page 2 (Section A.3) with pypdf. It reads verbatim: \\\"f (t) = 1− σ(z) · s / 1 + (s − 1) · σ(z) , where z ∼ N(0, 1). (1) The σ(·) denotes the sigmoid function. In f (t), which is suggested by our base training code†…" }, { "type": "workflow_agent", "index": 57, "label": "v2:The exact unbalanced sampling density is", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a2bad62585c9b6957", "model": "claude-fable-5", "state": "done", "startedAt": 1786399946723, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source directly: downloaded th…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400017911, "tokens": 28071, "toolCalls": 6, "durationMs": 71188, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source directly: downloaded the ICCV 2025 supplemental PDF (openaccess.thecvf.com/content/ICCV2025/supplemental/Byung-Ki_JointDiT_Enhancing_RGB-Depth_ICCV_2025_supplemental.pdf) and extracted text with pdftotext. Section A.3 contains verbatim: \\\"The timestep distribution is as follows: f(t) = 1− σ(z)·s / (1 + (s−1)·σ(z)), where z ∼ N(0, 1).…" }, { "type": "workflow_agent", "index": 58, "label": "v0:JointDiT's cross-stream joint connection", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "aa69ff89621daf4d0", "model": "claude-fable-5", "state": "done", "startedAt": 1786399956077, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Downloaded both primary PDFs and extracted text directly. (…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400105507, "tokens": 50985, "toolCalls": 14, "durationMs": 149430, "resultPreview": "{\"refuted\":false,\"evidence\":\"Downloaded both primary PDFs and extracted text directly. (1) The supplement quote is verbatim-exact: Sec A.4, p.2 of the ICCV 2025 supplemental reads \\\"To design the joint connection module, we adopt the joint cross-attention module from UniCon [16], followed by a zero-initialized linear projection layer. The adaptive scheduling weight is applied subsequently.\\\" (2) F…" }, { "type": "workflow_agent", "index": 59, "label": "v1:JointDiT's cross-stream joint connection", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "ad516c9c319609682", "model": "claude-fable-5", "state": "done", "startedAt": 1786399980750, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against two primary sources. (1) Downloaded the of…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400102992, "tokens": 33581, "toolCalls": 13, "durationMs": 122242, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against two primary sources. (1) Downloaded the official ICCV 2025 supplemental PDF (openaccess.thecvf.com, retrieved via curl after WebFetch 403) and extracted text: Section A.4 contains the exact quote \\\"To design the joint connection module, we adopt the joint cross-attention module from UniCon [16], followed by a zero-initialized linear projection layer. T…" }, { "type": "workflow_agent", "index": 60, "label": "v2:JointDiT's cross-stream joint connection", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a511cbeb2651a31ca", "model": "claude-fable-5", "state": "done", "startedAt": 1786399993813, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Downloaded the primary source (openaccess.thecvf.com ICCV 2…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400104433, "tokens": 32596, "toolCalls": 11, "durationMs": 110619, "resultPreview": "{\"refuted\":false,\"evidence\":\"Downloaded the primary source (openaccess.thecvf.com ICCV 2025 supplemental PDF, 12 pages) and extracted text: Section A.4 contains the exact verbatim sentence \\\"To design the joint connection module, we adopt the joint cross-attention module from UniCon [16], followed by a zero-initialized linear projection layer. The adaptive scheduling weight is applied subsequently…" }, { "type": "workflow_agent", "index": 61, "label": "v0:The depth branch is built by adding LoRA", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a3e089cd280c3ee3c", "model": "claude-fable-5", "state": "done", "startedAt": 1786399997410, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified directly against the primary ICCV 2025 supplementa…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400125150, "tokens": 38203, "toolCalls": 14, "durationMs": 127739, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified directly against the primary ICCV 2025 supplemental PDF (downloaded, text-extracted). Every element checks out verbatim: (a) Sec A.4: \\\"we add LoRAs to the components connected before and after the attention mechanisms of the... (MM-DiT) and... (P-DiT) blocks... We use a LoRA rank of 64 for both MM-DiT and P-DiT, and apply relatively larger ranks of 512 or 102…" }, { "type": "workflow_agent", "index": 62, "label": "v1:The depth branch is built by adding LoRA", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "acefb630c9eae586d", "model": "claude-fable-5", "state": "done", "startedAt": 1786400003158, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source (downloaded and full-te…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400115116, "tokens": 45683, "toolCalls": 10, "durationMs": 111958, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source (downloaded and full-text extracted the ICCV 2025 supplemental PDF) AND the official code. Supplemental Sec. A.4 states verbatim: \\\"We use a LoRA rank of 64 for both MM-DiT and P-DiT, and apply relatively larger ranks of 512 or 1024 to the input stage. The alpha value is set to half of the corresponding rank,\\\" and describes adding L…" }, { "type": "workflow_agent", "index": 63, "label": "v2:The depth branch is built by adding LoRA", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a2b223c7a5e903224", "model": "claude-fable-5", "state": "done", "startedAt": 1786400012364, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source itself: downloaded the …", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400120843, "tokens": 33056, "toolCalls": 10, "durationMs": 108479, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source itself: downloaded the ICCV 2025 supplemental PDF from openaccess.thecvf.com and extracted full text. Section A.4 states verbatim: \\\"we add LoRAs to the components connected before and after the attention mechanisms of the multi-modal diffusion transformer (MM-DiT) and parallel diffusion transformer (P-DiT) blocks that constitute Flu…" }, { "type": "workflow_agent", "index": 64, "label": "v0:All inference modes are dialed purely by", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "aec73ba06d100ed2d", "model": "claude-fable-5", "state": "done", "startedAt": 1786400021162, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Independently verified against arXiv HTML v3 of JointDiT (2…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400121748, "tokens": 29312, "toolCalls": 6, "durationMs": 100586, "resultPreview": "{\"refuted\":false,\"evidence\":\"Independently verified against arXiv HTML v3 of JointDiT (2505.00482), same content as the ICCV 2025 supplemental. All claim elements confirmed verbatim: \\\"We consistently use 20 denoising steps across all experiments\\\"; joint generation \\\"initially setting tx=0 and ty=0 by sampling noises from a standard normal distribution\\\" (confirms t=0 = pure noise convention); de…" }, { "type": "workflow_agent", "index": 65, "label": "v1:All inference modes are dialed purely by", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a9ef663dfaf6a5257", "model": "claude-fable-5", "state": "done", "startedAt": 1786400042518, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the actual downloaded ICCV 2025 supplement…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400170341, "tokens": 43633, "toolCalls": 9, "durationMs": 127823, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the actual downloaded ICCV 2025 supplemental PDF (full text extracted, 12 pages). Sec A.1 contains every assertion verbatim: \\\"We consistently use 20 denoising steps across all experiments\\\"; joint generation \\\"initially setting tx = 0 and ty = 0 by sampling noises from a standard normal distribution\\\" (confirming t=0 = pure noise in their convention, …" }, { "type": "workflow_agent", "index": 66, "label": "v2:All inference modes are dialed purely by", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a24b1879a7c7dcf83", "model": "claude-fable-5", "state": "done", "startedAt": 1786400045280, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Downloaded and read the primary PDF (ICCV 2025 supplemental…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400197551, "tokens": 39056, "toolCalls": 15, "durationMs": 152271, "resultPreview": "{\"refuted\":false,\"evidence\":\"Downloaded and read the primary PDF (ICCV 2025 supplemental) plus the arXiv main paper (2505.00482). Every element verifies verbatim: (a) \\\"We consistently use 20 denoising steps across all experiments\\\" (supp A.1); (b) joint generation \\\"initially setting tx = 0 and ty = 0 by sampling noises from a standard normal distribution\\\" — confirming t=0 = pure noise in their …" }, { "type": "workflow_agent", "index": 67, "label": "v0:JointDiT achieves all three inference mo", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "afd672a5e8c810a46", "model": "claude-fable-5", "state": "done", "startedAt": 1786400063029, "queuedAt": 1786399727375, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Primary source verified at three levels. (1) The quote is v…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400131253, "tokens": 28948, "toolCalls": 6, "durationMs": 68223, "resultPreview": "{\"refuted\":false,\"evidence\":\"Primary source verified at three levels. (1) The quote is verbatim on the project page and in the arXiv 2505.00482 abstract. (2) The full paper makes the mechanism explicit: \\\"When tx=1, ty=0, it performs depth estimation from a given image\\\"; \\\"When tx=0, ty=1, it becomes a depth-conditioned image generation\\\" (JointDiT convention: t=1=clean, t=0=max noise). A single …" }, { "type": "workflow_agent", "index": 68, "label": "v1:JointDiT achieves all three inference mo", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a8ec1f7ef3b26c177", "model": "claude-fable-5", "state": "done", "startedAt": 1786400070168, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Claim survives adversarial checks. (1) Quote support: exact…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400156216, "tokens": 29768, "toolCalls": 8, "durationMs": 86048, "resultPreview": "{\"refuted\":false,\"evidence\":\"Claim survives adversarial checks. (1) Quote support: exact — the ICCV 2025 paper's abstract states JointDiT handles \\\"joint generation, depth estimation, and depth-conditioned image generation by simply controlling the timestep of each branch\\\"; claim restates this faithfully. (2) Independent confirmation of no per-task retraining: official repo kaist-ami/JointDiT shi…" }, { "type": "workflow_agent", "index": 69, "label": "v2:JointDiT achieves all three inference mo", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "af889fbbd70f8437b", "model": "claude-fable-5", "state": "done", "startedAt": 1786400086120, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against primary sources. The arXiv abstract (2505.…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400141742, "tokens": 27944, "toolCalls": 5, "durationMs": 55621, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against primary sources. The arXiv abstract (2505.00482) states verbatim that the two training techniques \\\"enable...JointDiT to naturally handle various combinatorial generation tasks, including joint generation, depth estimation, and depth-conditioned image generation by simply controlling the timesteps of each branch,\\\" and describes this as \\\"a single unif…" }, { "type": "workflow_agent", "index": 70, "label": "v0:JointDiT trains with 'adaptive schedulin", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a3acc06b440a56f8b", "model": "claude-fable-5", "state": "done", "startedAt": 1786400106190, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Direct WebFetch of the primary source (https://byungki-k.gi…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400153056, "tokens": 26135, "toolCalls": 4, "durationMs": 46866, "resultPreview": "{\"refuted\":false,\"evidence\":\"Direct WebFetch of the primary source (https://byungki-k.github.io/JointDiT/) confirms the exact supporting quote \\\"adaptive scheduling weights, which depend on the noise levels of each modality\\\" appears on the page, and confirms the page contains NO formula (no sigmoid, no alpha=3, no t_ratio equation) and does NOT specify whether the weights gate joint cross-attenti…" }, { "type": "workflow_agent", "index": 71, "label": "v1:JointDiT trains with 'adaptive schedulin", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a2778b93dfbb97692", "model": "claude-fable-5", "state": "done", "startedAt": 1786400107451, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified the project page (byungki-k.github.io/JointDiT) ab…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400163125, "tokens": 27260, "toolCalls": 5, "durationMs": 55673, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified the project page (byungki-k.github.io/JointDiT) abstract contains the exact quote \\\"adaptive scheduling weights, which depend on the noise levels of each modality\\\" and contains NO formula, no alpha value, and no statement of whether the weights gate attention outputs vs the loss — exactly as the claim states. The claim is deliberately calibrated (asserts only…" }, { "type": "workflow_agent", "index": 72, "label": "v2:JointDiT trains with 'adaptive schedulin", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a8d72704981d09d07", "model": "claude-fable-5", "state": "done", "startedAt": 1786400109080, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Fetched https://byungki-k.github.io/JointDiT/ directly: the…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400154415, "tokens": 26307, "toolCalls": 4, "durationMs": 45334, "resultPreview": "{\"refuted\":false,\"evidence\":\"Fetched https://byungki-k.github.io/JointDiT/ directly: the page contains the verbatim quote \\\"adaptive scheduling weights, which depend on the noise levels of each modality\\\" and also names \\\"the unbalanced timestep sampling strategy\\\" as the second technique, but contains NO mathematical formulas whatsoever — no sigmoid, no w=sigmoid(alpha*(t_ratio-1/2)), no alpha=3,…" }, { "type": "workflow_agent", "index": 73, "label": "v0:JointDiT uses an 'unbalanced timestep sa", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "aee09022357ed561b", "model": "claude-fable-5", "state": "done", "startedAt": 1786400118295, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Direct fetch of https://byungki-k.github.io/JointDiT/ confi…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400190138, "tokens": 28170, "toolCalls": 5, "durationMs": 71842, "resultPreview": "{\"refuted\":false,\"evidence\":\"Direct fetch of https://byungki-k.github.io/JointDiT/ confirms the exact phrase \\\"the unbalanced timestep sampling strategy\\\" appears in the page abstract (\\\"...adaptive scheduling weights, which depend on the noise levels of each modality, and the unbalanced timestep sampling strategy\\\"), and the page contains zero numeric detail: no densities f/g, no s=3.1582 or s=0.…" }, { "type": "workflow_agent", "index": 74, "label": "v1:JointDiT uses an 'unbalanced timestep sa", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a6861dbeb2fb72e86", "model": "claude-fable-5", "state": "done", "startedAt": 1786400124206, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Direct fetch of https://byungki-k.github.io/JointDiT/ confi…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400181090, "tokens": 27404, "toolCalls": 5, "durationMs": 56884, "resultPreview": "{\"refuted\":false,\"evidence\":\"Direct fetch of https://byungki-k.github.io/JointDiT/ confirms the exact quote: the abstract states the model's joint modeling is achieved via \\\"adaptive scheduling weights... and the unbalanced timestep sampling strategy,\\\" and the page contains no equations, no f/g densities, no s-parameter values, and no diagonal/independent mixture ratios — matching the claim's ass…" }, { "type": "workflow_agent", "index": 75, "label": "v2:JointDiT uses an 'unbalanced timestep sa", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "aa4ec33510b7d2a0d", "model": "claude-fable-5", "state": "done", "startedAt": 1786400125229, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Direct fetch of https://byungki-k.github.io/JointDiT/ confi…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400200397, "tokens": 27908, "toolCalls": 5, "durationMs": 75166, "resultPreview": "{\"refuted\":false,\"evidence\":\"Direct fetch of https://byungki-k.github.io/JointDiT/ confirms the abstract contains verbatim: \\\"adaptive scheduling weights, which depend on the noise levels of each modality, and the unbalanced timestep sampling strategy\\\" — and confirms the page contains NO densities, no s-parameters, and no mixture ratios (only prose-level description). Both halves of the claim are…" }, { "type": "workflow_agent", "index": 76, "label": "v0:JointDiT trains with a 50/50 mixture: ha", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a3c10c19a514cffd1", "model": "claude-fable-5", "state": "done", "startedAt": 1786400126620, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source full text (arXiv 2505.0…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400184459, "tokens": 27772, "toolCalls": 5, "durationMs": 57839, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source full text (arXiv 2505.00482, HTML v1): the paper states verbatim that t_x and t_y are sampled \\\"independently from two unbalanced timestep distributions, f(t) and g(t), with half probability during training\\\" and that \\\"for the remaining half, the same timesteps sampled from f(t) are assigned to t_x and t_y\\\" — exactly the claimed 50…" }, { "type": "workflow_agent", "index": 77, "label": "v1:JointDiT trains with a 50/50 mixture: ha", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a4ce5d42e2913d745", "model": "claude-fable-5", "state": "done", "startedAt": 1786400128693, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source (arXiv HTML full text, …", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400184949, "tokens": 28443, "toolCalls": 5, "durationMs": 56256, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source (arXiv HTML full text, 2505.00482) and the peer-reviewed ICCV 2025 version. The paper states verbatim that with 50% probability \\\"the timesteps, i.e., t_x and t_y, are separately sampled from the timestep distributions f(t) and g(t), respectively, or vice versa,\\\" and that \\\"for the remaining 50%, the same timestep sampled from f(t) …" }, { "type": "workflow_agent", "index": 78, "label": "v2:JointDiT trains with a 50/50 mixture: ha", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "ad52fd5d29f2f8942", "model": "claude-fable-5", "state": "done", "startedAt": 1786400134709, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source full text (arXiv 2505.0…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400196382, "tokens": 28004, "toolCalls": 5, "durationMs": 61673, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source full text (arXiv 2505.00482, Sec. 3.1 + Appendix A.3) and the ICCV 2025 camera-ready supplementary. Main text verbatim: \\\"samples t_x and t_y independently from two unbalanced timestep distributions, f(t) and g(t), with half probability during training. For the remaining half, the same timesteps sampled from f(t) are assigned to t_x …" }, { "type": "workflow_agent", "index": 79, "label": "v0:The unbalanced timestep density (Eq. 7)", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a45ad6479507f0889", "model": "claude-fable-5", "state": "done", "startedAt": 1786400146486, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the arXiv v3 source HTML (arxiv.org/html/2…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400304928, "tokens": 36438, "toolCalls": 10, "durationMs": 158442, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the arXiv v3 source HTML (arxiv.org/html/2505.00482v3): the author LaTeX for Eq. (7) in Appendix A.3 reads exactly \\\"f(t)=1-\\\\frac{\\\\sigma(z)\\\\cdot s}{1+(s-1)\\\\cdot\\\\sigma(z)},\\\\quad\\\\text{where }z\\\\sim\\\\mathcal{N}(0,1).\\\" followed by \\\"In f(t), which is suggested by our base training code [kohya-ss/sd-scripts sd3], s is set to 3.1582. We set s to 0.25…" }, { "type": "workflow_agent", "index": 80, "label": "v1:The unbalanced timestep density (Eq. 7)", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a46775a7799e98548", "model": "claude-fable-5", "state": "done", "startedAt": 1786400155877, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "The actual Eq. 7 in JointDiT (arXiv 2505.00482v1, Appendix …", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400262140, "tokens": 31798, "toolCalls": 9, "durationMs": 106263, "resultPreview": "{\"refuted\":true,\"evidence\":\"The actual Eq. 7 in JointDiT (arXiv 2505.00482v1, Appendix A.3), verified from the raw LaTeX in the arXiv HTML source, is f(t) = σ(z)·s / (1+(s−1)·σ(z)) with z ~ N(0,1) — with NO leading \\\"1 −\\\". The claim's \\\"1 − ...\\\" form is a misquote and is materially wrong: it inverts the density's skew (paper's form with s=3.1582 concentrates timesteps near t=1/high-noise; the cl…" }, { "type": "workflow_agent", "index": 81, "label": "v2:The unbalanced timestep density (Eq. 7)", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a52194ab0e6ab1fc7", "model": "claude-fable-5", "state": "done", "startedAt": 1786400158567, "queuedAt": 1786399727376, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source, not just the HF mirror…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400274955, "tokens": 32507, "toolCalls": 11, "durationMs": 116388, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source, not just the HF mirror. The current arXiv version (2505.00482v3, 2025-08-05) Section A.3 'Unbalanced Timestep Sampling Strategy' contains Eq. (7) verbatim as LaTeX: f(t)=1-\\\\frac{\\\\sigma(z)\\\\cdot s}{1+(s-1)\\\\cdot\\\\sigma(z)}, where z~N(0,1), immediately followed by: 'In f(t), which is suggested by our base training code [github.com/k…" }, { "type": "workflow_agent", "index": 82, "label": "v0:JointDiT's adaptive scheduling weight is", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "afc9c23024da73f3b", "model": "claude-fable-5", "state": "done", "startedAt": 1786400159836, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Full arXiv HTML text of JointDiT (arxiv.org/html/2505.00482…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400210915, "tokens": 27360, "toolCalls": 5, "durationMs": 51079, "resultPreview": "{\"refuted\":false,\"evidence\":\"Full arXiv HTML text of JointDiT (arxiv.org/html/2505.00482) confirms all three parts verbatim: (1) w_x(t_x,t_y)=sigmoid(α(t_y/(t_x+t_y)−1/2)) and symmetric w_y(t_x,t_y)=sigmoid(α(t_x/(t_x+t_y)−1/2)) — the claim's numerator assignment matches; (2) \\\"We set α to 3 for all experiments\\\" quoted directly; (3) the weights gate joint cross-attention OUTPUTS in the forward pa…" }, { "type": "workflow_agent", "index": 83, "label": "v1:JointDiT's adaptive scheduling weight is", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "ac95ef1fe2bd3b2fc", "model": "claude-fable-5", "state": "done", "startedAt": 1786400164121, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source via two independent ren…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400227820, "tokens": 29024, "toolCalls": 6, "durationMs": 63699, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source via two independent renderings (arxiv.org/html/2505.00482v1 and ar5iv.labs.arxiv.org/html/2505.00482). Both show verbatim: w_x(t_x,t_y)=sigmoid(α(t_y/(t_x+t_y)−1/2)) and w_y(t_x,t_y)=sigmoid(α(t_x/(t_x+t_y)−1/2)); exact sentence \\\"We set α to 3 for all experiments.\\\" Application point is feature-level: G_x = Attn(S_x) + w_x(t_x,t_y)·…" }, { "type": "workflow_agent", "index": 84, "label": "v2:JointDiT's adaptive scheduling weight is", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a853cfdf846d461f6", "model": "claude-fable-5", "state": "done", "startedAt": 1786400166427, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source (arXiv 2505.00482 HTML …", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400222496, "tokens": 28374, "toolCalls": 6, "durationMs": 56069, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source (arXiv 2505.00482 HTML full text, JointDiT, ICCV 2025) via two independent fetches. Eq. 6 states verbatim: w_x(t_x,t_y)=sigmoid(α(t_y/(t_x+t_y)−1/2)) and w_y(t_x,t_y)=sigmoid(α(t_x/(t_x+t_y)−1/2)); the paper states \\\"We set α to 3 for all experiments.\\\" Eq. 5 confirms the weights gate features, not loss: G_x = Attn(S_x) + w_x(t_x,t_y…" }, { "type": "workflow_agent", "index": 85, "label": "v0:JointDiT is built on a frozen Flux backb", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "ab507e0b7b74a9964", "model": "claude-fable-5", "state": "done", "startedAt": 1786400173504, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Primary source (arXiv 2505.00482 full text) confirms all su…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400235248, "tokens": 27473, "toolCalls": 5, "durationMs": 61744, "resultPreview": "{\"refuted\":false,\"evidence\":\"Primary source (arXiv 2505.00482 full text) confirms all substantive components verbatim: \\\"We train the LoRAs and joint connection modules while keeping the pre-trained backbone model frozen\\\"; \\\"We use a LoRA rank of 64 for both MM-DiT and P-DiT\\\"; \\\"To design the joint connection module, we adopt the joint cross-attention module from UniCon, followed by a zero-initi…" }, { "type": "workflow_agent", "index": 86, "label": "v1:JointDiT is built on a frozen Flux backb", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a59916bb6c9b071b9", "model": "claude-fable-5", "state": "done", "startedAt": 1786400185278, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified directly against the latest arXiv HTML of 2505.004…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400292663, "tokens": 35030, "toolCalls": 9, "durationMs": 107385, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified directly against the latest arXiv HTML of 2505.00482 (downloaded and grepped locally, not just a summary model). Every element of the claim appears verbatim: (1) backbone: \\\"JointDiT is built on Flux ... consists of multi-modal diffusion transformer (MM-DiT) and parallel diffusion transformer (P-DiT) blocks\\\"; (2) frozen + trained params: \\\"We train the LoRAs …" }, { "type": "workflow_agent", "index": 87, "label": "v2:JointDiT is built on a frozen Flux backb", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a0b6c22544b8d49f0", "model": "claude-fable-5", "state": "done", "startedAt": 1786400187717, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified directly against the primary source (arXiv 2505.00…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400293919, "tokens": 42062, "toolCalls": 9, "durationMs": 106200, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified directly against the primary source (arXiv 2505.00482, raw HTML of v1 and v3 grepped, not just summarized). Every element of the claim is stated verbatim in the paper: (a) \\\"JointDiT is built on Flux\\\" / MM-DiT and P-DiT blocks \\\"that constitute Flux\\\"; (b) \\\"We train the LoRAs and joint connection modules while keeping the pre-trained backbone model frozen\\\";…" }, { "type": "workflow_agent", "index": 88, "label": "v0:Generation modes are dialed purely by pe", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a723a2f01af6221e2", "model": "claude-fable-5", "state": "done", "startedAt": 1786400188096, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source (arXiv 2505.00482, Join…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400277051, "tokens": 30203, "toolCalls": 6, "durationMs": 88955, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source (arXiv 2505.00482, JointDiT, ICCV 2025). Full HTML text confirms all three numeric settings verbatim: joint generation by \\\"initially setting t_x=0, t_y=0\\\" with noises sampled from a standard normal; depth-conditioned image generation by \\\"setting t_x=0 and t_y=1\\\"; depth estimation by \\\"set t_x=1 and t_y=0 and provide an empty text…" }, { "type": "workflow_agent", "index": 89, "label": "v1:Generation modes are dialed purely by pe", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "aec9174d9cf863477", "model": "claude-fable-5", "state": "done", "startedAt": 1786400194023, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified directly against arXiv HTML of 2505.00482 (JointDi…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400283990, "tokens": 29130, "toolCalls": 5, "durationMs": 89967, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified directly against arXiv HTML of 2505.00482 (JointDiT). Exact sentences present: \\\"initially setting tx=0,ty=0 leads to the joint generation of both images and depth maps\\\"; \\\"We generate depth-conditioned images from given text prompts by initially setting tx=0 and ty=1. The conditioning depth maps are obtained by Depth-Anything-V2\\\" (Sec. A.1); \\\"To estimate t…" }, { "type": "workflow_agent", "index": 90, "label": "v2:Generation modes are dialed purely by pe", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a2c8ae9f6f6e58eea", "model": "claude-fable-5", "state": "done", "startedAt": 1786400199288, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source (arXiv HTML of 2505.004…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400305450, "tokens": 31933, "toolCalls": 6, "durationMs": 106162, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source (arXiv HTML of 2505.00482, fetched twice with independent prompts). All three timestep assignments are verbatim in the paper: \\\"initially setting tx=0,ty=0 leads to the joint generation of both images and depth maps\\\"; \\\"When tx=1,ty=0, it performs depth estimation from a given image\\\"; \\\"when tx=0,ty=1, it becomes a depth-conditione…" }, { "type": "workflow_agent", "index": 91, "label": "v0:UniDiffuser trains a single noise-predic", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "aa55b4890cbcc1c4b", "model": "claude-fable-5", "state": "done", "startedAt": 1786400200580, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Attempted refutation FAILED on all checks. (1) Primary-sour…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400281744, "tokens": 29233, "toolCalls": 5, "durationMs": 81164, "resultPreview": "{\"refuted\":false,\"evidence\":\"Attempted refutation FAILED on all checks. (1) Primary-source verification: the ar5iv full text of arXiv 2303.06555 confirms Equation 5 is exactly the quoted loss E_{x0,y0,eps_x,eps_y,tx,ty} ||eps_theta(x_tx, y_ty, tx, ty) - [eps_x, eps_y]||_2^2 (single joint L2 on the concatenated noise of both modalities), and the sentence below Eq. 5 reads verbatim \\\"t^x and t^y are…" }, { "type": "workflow_agent", "index": 92, "label": "v1:UniDiffuser trains a single noise-predic", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "aa1cede0967ef1dad", "model": "claude-fable-5", "state": "done", "startedAt": 1786400204314, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Primary source (ar5iv full text of arXiv 2303.06555) confir…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400313357, "tokens": 31078, "toolCalls": 6, "durationMs": 109043, "resultPreview": "{\"refuted\":false,\"evidence\":\"Primary source (ar5iv full text of arXiv 2303.06555) confirms every element: (a) Equation 5 is exactly the claimed loss, E||eps_theta(x_{t^x}, y_{t^y}, t^x, t^y) − [eps^x, eps^y]||²₂, described as \\\"a joint noise prediction network ... predict the noise injected to x_{t^x} and y_{t^y} together by minimizing the following regression loss\\\" — i.e., epsilon-prediction wit…" }, { "type": "workflow_agent", "index": 93, "label": "v2:UniDiffuser trains a single noise-predic", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a7990f5ba3fffd9ff", "model": "claude-fable-5", "state": "done", "startedAt": 1786400214219, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source (arXiv 2303.06555, Sec.…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400310145, "tokens": 30629, "toolCalls": 6, "durationMs": 95926, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source (arXiv 2303.06555, Sec. 3.1; also ICML 2023 / PMLR v202). The paper states verbatim: \\\"t^x and t^y are uniformly sampled from {1,2,…,T} independently\\\", and Eq. 5 is exactly the single joint L2 on concatenated noise E‖ε_θ(x_{t^x}, y_{t^y}, t^x, t^y) − [ε^x, ε^y]‖²₂ with no modality weighting. A targeted search of Sec. 3.2/4 and the a…" }, { "type": "workflow_agent", "index": 94, "label": "v0:Exact conditioning (e.g., texture-given-", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "ae24332b6402e57e3", "model": "claude-fable-5", "state": "done", "startedAt": 1786400225359, "queuedAt": 1786399727377, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified verbatim against the primary source (ar5iv HTML of…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400309240, "tokens": 29619, "toolCalls": 4, "durationMs": 83881, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified verbatim against the primary source (ar5iv HTML of arXiv 2303.06555, UniDiffuser, ICML 2023). Section 3.1 states exactly: \\\"a zero timestep means conditioning on the corresponding modality\\\" and \\\"E[eps^x | x_{t^x}, y_0] corresponds to the conditional distribution q(x_0|y_0) by setting t^y=0\\\" — matching the claim's quote word-for-word. The \\\"never integrated\\…" }, { "type": "workflow_agent", "index": 95, "label": "v1:Exact conditioning (e.g., texture-given-", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a54e87e193f7b168b", "model": "claude-fable-5", "state": "done", "startedAt": 1786400230968, "queuedAt": 1786399727378, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source (UniDiffuser, arXiv 230…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400331426, "tokens": 31041, "toolCalls": 5, "durationMs": 100458, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source (UniDiffuser, arXiv 2303.06555, ICML 2023, Section 3.1 via ar5iv full text). The paper states essentially verbatim: \\\"a zero timestep means conditioning on the corresponding modality\\\" and E[eps^x | x_{t^x}, y_0] \\\"corresponds to the conditional distribution q(x0|y0) by setting t^y = 0\\\". Critically, the paper marks only the MARGINAL…" }, { "type": "workflow_agent", "index": 96, "label": "v2:Exact conditioning (e.g., texture-given-", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a1834373fdf1db657", "model": "claude-fable-5", "state": "done", "startedAt": 1786400238726, "queuedAt": 1786399727378, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified verbatim against the full text (ar5iv/2303.06555, …", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400323087, "tokens": 29693, "toolCalls": 4, "durationMs": 84361, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified verbatim against the full text (ar5iv/2303.06555, Sec. 3): \\\"a zero timestep means conditioning on the corresponding modality... Formally, E[eps^x|x_{t^x}, y_0] corresponds to the conditional distribution q(x_0|y_0) by setting t^y=0\\\" — exactly the quoted support. Crucially, UniDiffuser attaches its only approximation caveat to the OTHER corner: for marginals …" }, { "type": "workflow_agent", "index": 97, "label": "v0:Marginal (single-modality) generation is", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a2551b796fc7b9117", "model": "claude-fable-5", "state": "done", "startedAt": 1786400265174, "queuedAt": 1786399727378, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source directly: downloaded ar…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400388719, "tokens": 38049, "toolCalls": 12, "durationMs": 123545, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source directly: downloaded arXiv 2303.06555 PDF and extracted text. Section 3.1 contains the quote verbatim: \\\"In particular, a maximum timestep T means marginalizing it. Namely, by setting ty = T, we have E[ϵx|xtx, yT ] ≈ E[ϵx|xtx], which corresponds to the marginal distribution q(x0).\\\" The 'fed as pure noise, not dropped' half is indepe…" }, { "type": "workflow_agent", "index": 98, "label": "v1:Marginal (single-modality) generation is", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a97c8f657a09e4538", "model": "claude-fable-5", "state": "done", "startedAt": 1786400279294, "queuedAt": 1786399727378, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source. The ar5iv full text of…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400348165, "tokens": 28312, "toolCalls": 5, "durationMs": 68871, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source. The ar5iv full text of arXiv 2303.06555 (UniDiffuser, ICML 2023) contains the supporting quote verbatim: \\\"a maximum timestep T means marginalizing it. Namely, by setting ty=T, we have E[eps_x|x_tx, y_T] ≈ E[eps_x|x_tx]\\\", corresponding to the marginal distribution q(x0). The claim's extra detail — the other modality is FED AS PURE …" }, { "type": "workflow_agent", "index": 99, "label": "v2:Marginal (single-modality) generation is", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a7456e8829ace3421", "model": "claude-fable-5", "state": "done", "startedAt": 1786400280246, "queuedAt": 1786399727378, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the primary source (ar5iv full text of arX…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400349331, "tokens": 28101, "toolCalls": 4, "durationMs": 69085, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the primary source (ar5iv full text of arXiv 2303.06555, UniDiffuser, Bao et al., ICML 2023). The paper contains the quoted passage verbatim: \\\"by setting t_y=T, we have E[eps^x | x_tx, y_T] ≈ E[eps^x | x_tx]\\\" and \\\"a maximum timestep T means marginalizing it,\\\" explicitly tied to the marginal distribution q(x0). The claim's second half is also confir…" }, { "type": "workflow_agent", "index": 100, "label": "v0:UniDiffuser gets classifier-free guidanc", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a232582c20bf2a1ac", "model": "claude-fable-5", "state": "done", "startedAt": 1786400284716, "queuedAt": 1786399727378, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against primary source and official code. (1) ar5i…", "promptPreview": "## Adversarial Claim Verifier (voter 1/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400372424, "tokens": 29966, "toolCalls": 6, "durationMs": 87708, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against primary source and official code. (1) ar5iv full text of arXiv 2303.06555 Sec. 3.2 contains the exact equation verbatim: \\\"ϵ̂ˣ_θ(x_t,y_0,t)=(1+s)ϵˣ_θ(x_t,y_0,t,0)−s·ϵˣ_θ(x_t,ϵʸ,t,T)\\\", with the stated mechanism that setting tʸ=T gives y_T≈ϵʸ (pure noise) so E[ϵˣ|x_t,y_T]≈E[ϵˣ|x_t], and the explicit statement that CFG is \\\"directly applicable... without…" }, { "type": "workflow_agent", "index": 101, "label": "v1:UniDiffuser gets classifier-free guidanc", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a708e69736f0064fb", "model": "claude-fable-5", "state": "done", "startedAt": 1786400286938, "queuedAt": 1786399727378, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Verified against the full paper text (ar5iv HTML of arXiv 2…", "promptPreview": "## Adversarial Claim Verifier (voter 2/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400360367, "tokens": 28865, "toolCalls": 5, "durationMs": 73429, "resultPreview": "{\"refuted\":false,\"evidence\":\"Verified against the full paper text (ar5iv HTML of arXiv 2303.06555) and the peer-reviewed ICML 2023 version (PMLR v202, Bao et al.). The paper contains the exact equation claimed: ε̂ˣ_θ(x_t, y₀, t) = (1+s)εˣ_θ(x_t, y₀, t, 0) − s·εˣ_θ(x_t, ε^y, t, T), with the conditional term evaluated at timestep pair (t, 0) on clean y₀ and the unconditional term at (t, T) with ε^y …" }, { "type": "workflow_agent", "index": 102, "label": "v2:UniDiffuser gets classifier-free guidanc", "phaseIndex": 4, "phaseTitle": "Verify", "agentId": "a811ddde314aefd20", "model": "claude-fable-5", "state": "done", "startedAt": 1786400295656, "queuedAt": 1786399727378, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "Primary source (arXiv 2303.06555, Sec 3.2, verified via ar5…", "promptPreview": "## Adversarial Claim Verifier (voter 3/3)\n\nBe SKEPTICAL. Try to REFUTE this claim. ≥2/3 refutations kill it.\n\n## Research question\nHOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLA…", "lastProgressAt": 1786400359405, "tokens": 28052, "toolCalls": 5, "durationMs": 63748, "resultPreview": "{\"refuted\":false,\"evidence\":\"Primary source (arXiv 2303.06555, Sec 3.2, verified via ar5iv full text) contains the exact formula: \\\"ϵ̂^x(x_t, y_0, t) = (1+s)ϵ^x(x_t, y_0, t, 0) − s ϵ^x(x_t, ϵ^y, t, T)\\\" — conditional branch uses clean y_0 at t^y=0, unconditional branch replaces y with random noise ϵ^y at t^y=T, matching the claim term-for-term. The \\\"for free\\\" quote is verbatim in the paper: \\\"Un…" }, { "type": "workflow_agent", "index": 103, "label": "synthesize", "phaseIndex": 5, "phaseTitle": "Synthesize", "agentId": "a1f9eeea62c84cfb5", "model": "claude-fable-5", "state": "done", "startedAt": 1786400391917, "queuedAt": 1786400390344, "attempt": 1, "lastToolName": "StructuredOutput", "lastToolSummary": "The verified recipe for a unified shape+tex SLAT DiT with p…", "promptPreview": "## Synthesis: research report\n\n**Question:** HOW exactly to implement a unified geometry+texture 3D DiT with per-modality timesteps (\"iso-generation\"). Context: we have a TRELLIS.2-style cascade — SS flow (sparse structure, stays separate) → shape-SLAT flow → tex-SLAT flow, rectified flow x_t=(1−t)x0+tε, both SLAT flows are 1.3B DiTs conditioned via read-once cross-attention on [DINOv3 ; VLM hidde…", "lastProgressAt": 1786400523893, "tokens": 44405, "toolCalls": 1, "durationMs": 131975, "resultPreview": "{\"summary\":\"The verified recipe for a unified shape+tex SLAT DiT with per-modality timesteps is essentially \\\"JointDiT mechanics on UniDiffuser theory\\\": train one network on all timestep pairs (t_shape, t_tex), where UniDiffuser (ICML 2023) proves conditioning = hold a modality clean at the data end (exact geometry lock, never integrated) and marginalization = feed the other modality as pure nois…" } ], "totalTokens": 3435142, "totalToolCalls": 796 }