Evidence Receipt. Related Resources.
Evidence Receipt. Related Resources.
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Verification pending
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Page Freshness
Canonical route: /signal-canvas/clustered-self-assessment-a-simple-yet-effective-method-for-uncertainty-quantification-in-large-language-models
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
Canonical ID clustered-self-assessment-a-simple-yet-effective-method-for-uncertainty-quantification-in-large-language-models | Route /signal-canvas/clustered-self-assessment-a-simple-yet-effective-method-for-uncertainty-quantification-in-large-language-models
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/clustered-self-assessment-a-simple-yet-effective-method-for-uncertainty-quantification-in-large-language-modelsMCP example
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References: Pending verification
Proof: Verification pending
Freshness state: computing
Source paper: Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models
PDF: https://arxiv.org/pdf/2606.03846v1
Repository: https://github.com/ccqq77/clustered_self_assessment
Source count: 4
Coverage: 83%
Last proof check: 2026-06-03T20:32:57.750Z
Signal Canvas receipt window
/buildability/clustered-self-assessment-a-simple-yet-effective-method-for-uncertainty-quantification-in-large-language-models
Subject: Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models
Preparing verified analysis
Dimensions overall score 7.0
{"file name": "input.pdf", "number of pages": 15, "author": "Qi Cao; Takeshi Kojima; Andrew Gambardella; Helinyi Peng; Yutaka Matsuo; Yusuke Iwasawa"
Implication not extracted yet.
partial
Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.
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Verdict
Build Now
Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Receipt path
/buildability/clustered-self-assessment-a-simple-yet-effective-method-for-uncertainty-quantification-in-large-language-models
Paper ref
clustered-self-assessment-a-simple-yet-effective-method-for-uncertainty-quantification-in-large-language-models
arXiv id
2606.03846
Generated at
2026-06-03T20:32:57.750Z
Evidence freshness
fresh
Last verification
2026-06-03T20:32:57.750Z
Sources
4
References
0
Coverage
83%
Lineage hash
2623f127e7eca4883842242829ca5ceacaa882ed4e4a778e6e526e8d495f5778
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