CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning

CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/core-seg-reasoning-driven-segmentation-for-complex-lesions-via-reinforcement-learning

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID core-seg-reasoning-driven-segmentation-for-complex-lesions-via-reinforcement-learning | Route /signal-canvas/core-seg-reasoning-driven-segmentation-for-complex-lesions-via-reinforcement-learning

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/core-seg-reasoning-driven-segmentation-for-complex-lesions-via-reinforcement-learning

MCP example

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source_context

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  "paper_ref": "core-seg-reasoning-driven-segmentation-for-complex-lesions-via-reinforcement-learning",
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  "benchmark_ref": null,
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Evidence Receipt

Route status: building

Claims: 7

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning

PDF: https://arxiv.org/pdf/2603.05911v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-19T18:48:05.835Z

Signal Canvas receipt window

Watch and verify: CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning

/buildability/core-seg-reasoning-driven-segmentation-for-complex-lesions-via-reinforcement-learning

Watchwatch

Subject: CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning

Verdict

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Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
we propose CORE-Seg, an end-to-end framework integrating reasoning with segmentation through a Semantic-Guided Prompt Adapter.
Implicationpartial
This is a direct statement from the abstract describing the proposed framework.
Verificationpartial
partial
Evidencepartial
In this paper, we introduce ComLesion-14K, the first diverse Chain-of-Thought (CoT) benchmark for reasoning-driven complex lesion segmentation.
Implicationpartial
The abstract explicitly states the introduction of this benchmark.
Verificationpartial
partial
Evidencepartial
Our Method achieves state-of-the-art results with a mean Dice of 37.06%
Implicationpartial
This is a specific quantitative result presented in the abstract.
Verificationpartial
partial
Evidencepartial
Our Method achieves state-of-the-art results with a mean Dice of 37.06% (14.89% higher than the second-best baseline)
Implicationpartial
This is a direct comparison of performance against a baseline, provided in the abstract.
Verificationpartial
partial
Evidencepartial
while reducing the failure rate to 18.42%.
Implicationpartial
This is a specific quantitative result related to failure rate reduction, stated in the abstract.
Verificationpartial
partial
Evidencepartial
We design a progressive training strategy from SFT to GRPO
Implicationpartial
The abstract mentions the training strategy used.
Verificationpartial
partial
Evidencepartial
equipped with an adaptive dual-granularity reward mechanism to mitigate reward sparsity.
Implicationpartial
The abstract describes a specific component of the training strategy.
Verificationpartial
partial

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CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning

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