Pith Number

pith:6KTIPVE3

pith:2026:6KTIPVE3T3Y35GPJUCBTWPZ26W

not attested not anchored not stored refs resolved

Topo-R1: Detecting Topological Anomalies via Vision-Language Models

Chao Chen, Dimitris Samaras, Kehan Qi, Meilong Xu, Qingqiao Hu, Shahira Abousamra, Weimin Lyu, Xiaoling Hu, Xin Yu

Fine-tuning a vision-language model with a topology-aware composite reward lets it localize and classify connectivity anomalies in tubular segmentation masks.

arxiv:2603.13054 v2 · 2026-03-13 · cs.CV

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{6KTIPVE3T3Y35GPJUCBTWPZ26W}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

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

Extensive experiments show that Topo-R1 substantially outperforms general-purpose VLMs and matches or exceeds supervised baselines across ID, OOD, and real-segmentation-output protocols, establishing a strong foundation for VLM-based topological understanding of structured visual data.

C2weakest assumption

The synthetic topological perturbations generated by the automated pipeline, annotated via Betti numbers, accurately capture the distribution and nature of topological anomalies present in real-world segmentation masks from medical and other domains.

C3one line summary

Topo-R1 fine-tunes a vision-language model using a topology-aware reward and GRPO to detect anomalies such as broken or spurious connections in tubular segmentation masks, outperforming standard VLMs.

References

105 extracted · 105 resolved · 15 Pith anchors

[1] In: NeurIPS (2022) 2022

[2] In: AISTATS (2024) 2024

[3] Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond 2023 · arXiv:2308.12966

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

[5] In: NeurIPS Workshop on Space in Vision, Language, and Embodied AI (2025) 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:09:22.819807Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f2a687d49b9ef1be99e9a0833b3f3af58835c3e54b2e8f37887c107f2481a8be

Aliases

arxiv: 2603.13054 · arxiv_version: 2603.13054v2 · doi: 10.48550/arxiv.2603.13054 · pith_short_12: 6KTIPVE3T3Y3 · pith_short_16: 6KTIPVE3T3Y35GPJ · pith_short_8: 6KTIPVE3

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/6KTIPVE3T3Y35GPJUCBTWPZ26W \
  | 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: f2a687d49b9ef1be99e9a0833b3f3af58835c3e54b2e8f37887c107f2481a8be

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "ac4e25ba4b345b82e3dca487a5ff0bbaec17cf06a65dcb5bf3298a1a1698339a",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-03-13T15:05:04Z",
    "title_canon_sha256": "f58bb0e96fbdc76f45f069d616a31dd13e4be381670120cc89bc7a0fd57e9915"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.13054",
    "kind": "arxiv",
    "version": 2
  }
}