pith:DE5HB5NE
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Reinforcement learning with format and accuracy rewards enables explicit reasoning chains to guide image segmentation.
arxiv:2503.06520 v2 · 2025-03-09 · cs.CV · cs.MM
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Claims
Seg-Zero-7B achieves a zero-shot performance of 57.5 on the ReasonSeg benchmark, surpassing the prior LISA-7B by 18%.
That the format-plus-accuracy reward mechanism, applied only through reinforcement learning without any explicit reasoning supervision, reliably produces useful and generalizable chain-of-thought reasoning rather than superficial patterns that happen to score well on the training distribution.
Seg-Zero uses cognitive reinforcement learning on a decoupled reasoning-plus-segmentation architecture to produce explicit reasoning chains and reach 57.5 zero-shot accuracy on ReasonSeg, beating prior supervised LISA-7B by 18%.
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| First computed | 2026-05-17T23:38:47.874901Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/DE5HB5NEEANUS4WY2X3QLHYLFJ \
| 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: 193a70f5a4201b4972d8d5f7059f0b2a766b9922fa756479a597657115b20c1b
Canonical record JSON
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