Buildability / Receipt
This public receipt window renders only fields present in the canonical receipt object, deterministic fixture receipt, or canonical evidence receipt. Missing compute, demo, hash, signature, approval, telemetry, and adoption fields stay explicit.
Public buildability page 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
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.
Data
{"file name": "input.pdf", "number of pages": 15, "author": "Qi Cao; Takeshi Kojima; Andrew Gambardella; Helinyi Peng; Yutaka Matsuo; Yusuke Iwasawa"
Truth Boundary
Buildability surfaces only report computed viability and proof receipts. They do not claim live production usage, pilot outcomes, founder sign-off, public Brier calibration, judge divergence, or external adoption unless explicitly sourced.
Compute
{"file name": "input.pdf", "number of pages": 15, "author": "Qi Cao; Takeshi Kojima; Andrew Gambardella; Helinyi Peng; Yutaka Matsuo; Yusuke Iwasawa", "title": "Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models", "creation date": null
Inference
{"file name": "input.pdf", "number of pages": 15, "author": "Qi Cao; Takeshi Kojima; Andrew Gambardella; Helinyi Peng; Yutaka Matsuo; Yusuke Iwasawa", "title": "Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models", "creation date": null
Hardware
{"file name": "input.pdf", "number of pages": 15, "author": "Qi Cao; Takeshi Kojima; Andrew Gambardella; Helinyi Peng; Yutaka Matsuo; Yusuke Iwasawa", "title": "Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models", "creation date": null
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.
Canonical opportunity-kernel evidence is available for this receipt window.
references