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"title": "Reproduction: DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models",
"emoji": "🎯",
"space_id": "Yashp2003/repro-diffthinker-towards-generative-multimodal-reasoning-with-diffusion-models",
"paper": {
"arxiv_id": "2512.24165"
},
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"icml2026-repro",
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],
"updated_at": "2026-07-23T06:31:57+00:00",
"root": {
"slug": "index",
"title": "Reproduction: DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models",
"file": "pages/index.md",
"children": [
{
"slug": "executive-summary",
"title": "Executive summary",
"file": "pages/executive-summary/page.md",
"children": []
},
{
"slug": "claim-1-diffthinker-achieves-an-87-4-average-score-across-vision-centric-reasoning-tasks-outperforming-gpt-5-21-1-avg-by-314-2-table-1",
"title": "Claim 1: DiffThinker achieves an 87.4% average score across vision-centric reasoning tasks, outperforming GPT-5 (21.1% avg) by 314.2% (Table 1).",
"file": "pages/claim-1-diffthinker-achieves-an-87-4-average-score-across-vision-centric-reasoning-tasks-outperforming-gpt-5-21-1-avg-by-314-2-table-1/page.md",
"children": []
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{
"slug": "claim-2-diffthinker-outperforms-gemini-3-flash-41-3-avg-by-111-6-on-the-same-multimodal-reasoning-benchmark-table-1",
"title": "Claim 2: DiffThinker outperforms Gemini-3-Flash (41.3% avg) by 111.6% on the same multimodal reasoning benchmark (Table 1).",
"file": "pages/claim-2-diffthinker-outperforms-gemini-3-flash-41-3-avg-by-111-6-on-the-same-multimodal-reasoning-benchmark-table-1/page.md",
"children": []
},
{
"slug": "claim-3-diffthinker-outperforms-a-fine-tuned-qwen3-vl-32b-baseline-62-9-avg-by-39-0-table-1",
"title": "Claim 3: DiffThinker outperforms a fine-tuned Qwen3-VL-32B baseline (62.9% avg) by 39.0% (Table 1).",
"file": "pages/claim-3-diffthinker-outperforms-a-fine-tuned-qwen3-vl-32b-baseline-62-9-avg-by-39-0-table-1/page.md",
"children": []
},
{
"slug": "claim-4-diffthinker-reformulates-multimodal-reasoning-as-an-image-to-image-generative-task-using-flow-matching-rather-than-text-centric-chain-of-thought-section-3",
"title": "Claim 4: DiffThinker reformulates multimodal reasoning as an image-to-image generative task using Flow Matching rather than text-centric chain-of-thought (Section 3).",
"file": "pages/claim-4-diffthinker-reformulates-multimodal-reasoning-as-an-image-to-image-generative-task-using-flow-matching-rather-than-text-centric-chain-of-thought-section-3/page.md",
"children": []
},
{
"slug": "claim-5-diffthinker-s-inference-latency-is-reported-at-approximately-1-1-seconds-with-training-efficiency-comparable-to-sft-baselines-figure-5",
"title": "Claim 5: DiffThinker's inference latency is reported at approximately 1.1 seconds, with training efficiency comparable to SFT baselines (Figure 5).",
"file": "pages/claim-5-diffthinker-s-inference-latency-is-reported-at-approximately-1-1-seconds-with-training-efficiency-comparable-to-sft-baselines-figure-5/page.md",
"children": []
},
{
"slug": "claim-6-ablations-show-performance-scales-consistently-with-training-data-size-and-peaks-at-a-classifier-free-guidance-scale-of-w-4-figures-8-9",
"title": "Claim 6: Ablations show performance scales consistently with training data size and peaks at a classifier-free guidance scale of w=4 (Figures 8-9).",
"file": "pages/claim-6-ablations-show-performance-scales-consistently-with-training-data-size-and-peaks-at-a-classifier-free-guidance-scale-of-w-4-figures-8-9/page.md",
"children": []
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{
"slug": "conclusion",
"title": "Conclusion",
"file": "pages/conclusion/page.md",
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}
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