proof blockPage 368%
This equation captures one of the core mathematical components of the system. x, τ = (z, y) a generation trajectory sampled from
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VL-Calibration: Decoupled Confidence Calibration for Large Vision-Language Models Reasoning explores A reinforcement learning framework that decouples confidence calibration in large vision-language models into visual and reasoning components to reduce hallucinations and improve accuracy.. Commercial viability score: 7/10 in Vision-Language Models.
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Page Freshness
Canonical route: /paper/vl-calibration-decoupled-confidence-calibration-for-large-vision-language-models-reasoning
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 vl-calibration-decoupled-confidence-calibration-for-large-vision-language-models-reasoning | Route /paper/vl-calibration-decoupled-confidence-calibration-for-large-vision-language-models-reasoning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/vl-calibration-decoupled-confidence-calibration-for-large-vision-language-models-reasoningMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.09529"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "VL-Calibration: Decoupled Confidence Calibration for Large Vision-Language Models Reasoning",
"normalized_query": "2604.09529",
"route": "/paper/vl-calibration-decoupled-confidence-calibration-for-large-vision-language-models-reasoning",
"paper_ref": "vl-calibration-decoupled-confidence-calibration-for-large-vision-language-models-reasoning",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Paper proof page receipt window
/buildability/vl-calibration-decoupled-confidence-calibration-for-large-vision-language-models-reasoning
Subject: VL-Calibration: Decoupled Confidence Calibration for Large Vision-Language Models Reasoning
Verdict
Build Now
Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.
Time to first demo
Insufficient data
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Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Receipt path
/buildability/vl-calibration-decoupled-confidence-calibration-for-large-vision-language-models-reasoning
Paper ref
vl-calibration-decoupled-confidence-calibration-for-large-vision-language-models-reasoning
arXiv id
2604.09529
Generated at
2026-04-13T20:33:10.509Z
Evidence freshness
stale
Last verification
2026-04-13T20:33:10.509Z
Sources
4
References
0
Coverage
83%
Lineage hash
660ed47c2014170f07883390d0ac802b0d4ab2f9de96d89fb334f7e637dcb89a
Canonical opportunity-kernel lineage hash.
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
Pending verification refs / 4 sources / Verification pending
references
Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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Dimensions overall score 7.0
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This equation captures one of the core mathematical components of the system. x, τ = (z, y) a generation trajectory sampled from
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. Racc(y, y∗) = 1y≡y∗, where 1(·) is indicator func-
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This equation captures one of the core mathematical components of the system. Racc_conf(y, c, y∗) = 1y≡y∗−(c −1y≡y∗)2 R(τ, y∗, z∗ vis) = 1y≡y∗− Φ(ˆcvis, ˆcreas) −1y≡y∗ O
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