Pith Number

pith:NGQGVISH

pith:2025:NGQGVISHEUGLHCFXJU4WYWMCX7

not attested not anchored not stored refs pending

Ovis2.5 Technical Report

Chengkun Hou, Gui Hu, Guodong Zheng, Haijun Li, Hailong Sun, Hui Sun, Huping Ding, Jiahe Li, Jiamang Wang, Jianshan Zhao, Jinlong Huang, Junke Tang, Junpeng Jiang, Kaifu Zhang, Lunhao Duan, Qing-Guo Chen, Sensen Gao, Shanshan Zhao, Shengze Shi, Shiyin Lu, Sijia Chen, Siran Yang, Tianli Zhou, Wanying Chen, Weihong Zhang, Weihua Luo, Wenjie Zhang, Wen Li, Yang Li, Yanqing Ma, Yibo Wang, Yi-Feng Wu, Yiliang Gu, Yinglun Li, Yuhui Chen, Yuping He, Yuwei Hu, Yu Xia, Yuxuan Han, Zhao Xu, Zhichao Wei, Zhixing Du

Ovis2.5 processes images at native resolutions and adds reflection to reach 78.3 on the OpenCompass multimodal leaderboard.

arxiv:2508.11737 v1 · 2025-08-15 · cs.CV · cs.AI · cs.CL · cs.LG

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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

Ovis2.5-9B averages 78.3 on the OpenCompass multimodal leaderboard, marking a substantial improvement over Ovis2-8B and achieving state-of-the-art results among open-source MLLMs in the sub-40B parameter range; Ovis2.5-2B scores 73.9 and establishes SOTA for its size.

C2weakest assumption

That the benchmark gains are primarily attributable to the native-resolution vision transformer and reflection mechanism rather than differences in training data volume, quality, or undisclosed hyperparameter tuning.

C3one line summary

Ovis2.5 introduces native-resolution visual processing and reflective chain-of-thought to reach SOTA open-source multimodal performance at 9B and 2B scales on benchmarks including STEM and chart analysis.

Formal links

3 machine-checked theorem links

Cited by

26 papers in Pith

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MapTab: Are MLLMs Ready for Multi-Criteria Route Planning in Heterogeneous Graphs?

VISTA-Bench: Do Vision-Language Models Really Understand Visualized Text as Well as Pure Text?

Why We Look Where We Look: Emergent Human-like Fixations of a Foveated Visual Language Model Maximizing Scene Understanding

iDiff: Interpretable Difference-aware Framework for Pairwise Image Quality Assessment

Receipt and verification

First computed	2026-05-17T23:38:50.260604Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

69a06aa247250cb388b74d396c5982bff1897a4b4e5fcd96726618264aa54fdd

Aliases

arxiv: 2508.11737 · arxiv_version: 2508.11737v1 · doi: 10.48550/arxiv.2508.11737 · pith_short_12: NGQGVISHEUGL · pith_short_16: NGQGVISHEUGLHCFX · pith_short_8: NGQGVISH

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/NGQGVISHEUGLHCFXJU4WYWMCX7 \
  | 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: 69a06aa247250cb388b74d396c5982bff1897a4b4e5fcd96726618264aa54fdd

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "a1d9b04e4e2d7624437f702c0157551661eccfa59b8bfbd4cd315543c7e0a673",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CL",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-08-15T17:01:08Z",
    "title_canon_sha256": "fc99a4c0a3021fc73f0bfa752295498c9fc389733691ba767bc28f3dddb295dc"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}