Pith Number

pith:B2NA3JF3

pith:2025:B2NA3JF324PQW6LMGLFJ54RVDV

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Imagine while Reasoning in Space: Multimodal Visualization-of-Thought

Chengzu Li, Furu Wei, Huanyu Zhang, Ivan Vuli\'c, Li Dong, Shaoguang Mao, Wenshan Wu, Yan Xia

Multimodal models can improve spatial reasoning by generating images that visualize their step-by-step thinking process.

arxiv:2501.07542 v1 · 2025-01-13 · cs.CL · cs.CV · cs.LG

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Experimental results reveal that MVoT demonstrates competitive performance across tasks. Moreover, it exhibits robust and reliable improvements in the most challenging scenarios where CoT fails.

C2weakest assumption

That the generated visualizations faithfully capture the model's internal reasoning state and that the token discrepancy loss produces images that actually aid downstream reasoning rather than introducing new errors or hallucinations.

C3one line summary

MVoT lets multimodal models create coherent images during chain-of-thought reasoning via a token discrepancy loss, yielding competitive or better results than text-only CoT on dynamic spatial tasks.

References

29 extracted · 29 resolved · 12 Pith anchors

[1] GPT-4 Technical Report · arXiv:2303.08774

[2] Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets · arXiv:2311.15127

[3] [Bro16] G Brockman. Openai gym. arXiv preprint arXiv:1606.01540, · arXiv:1606.01540

[4] Chameleon: Mixed-Modal Early-Fusion Foundation Models · arXiv:2405.09818

[5] Anole: An open, autoregressive, native large multimodal models for interleaved image-text generation

Formal links

2 machine-checked theorem links

Cited by

33 papers in Pith

SPACENUM: Revisiting Spatial Numerical Understanding in VLMs

GeoWeaver: Grounding Visual Tokens with Geometric Evidence before Scene Reasoning

Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models

IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools

Reasoning Portability: Guiding Continual Learning for MLLMs in the RLVR Era

Receipt and verification

First computed	2026-05-17T23:38:46.287290Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0e9a0da4bbd71f0b796c32ca9ef2351d549a7882de4070b545bb9a883e501ede

Aliases

arxiv: 2501.07542 · arxiv_version: 2501.07542v1 · doi: 10.48550/arxiv.2501.07542 · pith_short_12: B2NA3JF324PQ · pith_short_16: B2NA3JF324PQW6LM · pith_short_8: B2NA3JF3

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/B2NA3JF324PQW6LMGLFJ54RVDV \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 0e9a0da4bbd71f0b796c32ca9ef2351d549a7882de4070b545bb9a883e501ede

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "fb9dd12f2813e9529e879c6373319691ab8b3b5b40155a077f85f959d28090e8",
    "cross_cats_sorted": [
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2025-01-13T18:23:57Z",
    "title_canon_sha256": "37cfa5d5cb1102bce80a85da6657c8f27044c9a7c4a40196b9aed375a5068f6a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2501.07542",
    "kind": "arxiv",
    "version": 1
  }
}