Pith Number

pith:2OIRDWB4

pith:2025:2OIRDWB4HPTRLCRPNC7MI4FZQD

not attested not anchored not stored refs resolved

REVISOR: Beyond Textual Reflection, Towards Multimodal Introspective Reasoning in Long-Form Video Understanding

Boshen Xu, Hao Yin, Jian Luan, Jianzhong Ju, Jiaze Li, Jingyang Chen, Wenhui Tan, Yijing Chen, Yuxun Qu, Zhenbo Luo

REVISOR lets multimodal models reflect on both text and specific video segments to improve long-form video reasoning.

arxiv:2511.13026 v3 · 2025-11-17 · cs.CV

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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

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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

REVISOR enables MLLMs to collaboratively construct introspective reflection processes across textual and visual modalities, significantly enhancing their reasoning capability for long-form video understanding without requiring supplementary supervised fine-tuning or external models.

C2weakest assumption

That adding visual segment rethinking and cross-modal interaction during reflection will overcome the stated limitations of purely text-based reflection when applied to long-form video, and that the DADR reward will produce genuine causal alignment rather than spurious correlations.

C3one line summary

REVISOR adds multimodal visual-text reflection and a Dual Attribution Decoupled Reward to improve long-form video reasoning in MLLMs without extra supervised fine-tuning.

References

66 extracted · 66 resolved · 21 Pith anchors

[1] GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning 2025 · arXiv:2507.19457

[2] Qwen2.5-VL Technical Report 2025 · arXiv:2502.13923

[3] arXiv preprint arXiv:2412.12075 , year=

[4] Rextime: A benchmark suite for reasoning-across-time in videos.Advances in Neural In- formation Processing Systems, 37:28662–28673, 2024 2024

[5] Sharegpt4video: Improving video understand- ing and generation with better captions.Advances in Neural Information Processing Systems, 37:19472–19495, 2024 2024

Formal links

2 machine-checked theorem links

Cited by

1 paper in Pith

Video-OPD: Efficient Post-Training of Multimodal Large Language Models for Temporal Video Grounding via On-Policy Distillation

Receipt and verification

First computed	2026-05-17T23:39:04.623182Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d39111d83c3be7158a2f68bec470b980c49733b79e7a66857eed52e3f26fdeca

Aliases

arxiv: 2511.13026 · arxiv_version: 2511.13026v3 · doi: 10.48550/arxiv.2511.13026 · pith_short_12: 2OIRDWB4HPTR · pith_short_16: 2OIRDWB4HPTRLCRP · pith_short_8: 2OIRDWB4

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/2OIRDWB4HPTRLCRPNC7MI4FZQD \
  | 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: d39111d83c3be7158a2f68bec470b980c49733b79e7a66857eed52e3f26fdeca

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6dff86d5b21bc42516c99984ee0e01c65643878402ebf23229975b5d28304e25",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-11-17T06:25:12Z",
    "title_canon_sha256": "7a7586c68ee8cfa058f88ceead1a7a6bbdb8dc944dee2ba4e03e8c344f942366"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2511.13026",
    "kind": "arxiv",
    "version": 3
  }
}