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/when-large-language-models-fail-in-healthcare-evaluating-sensitivity-to-prompt-variations
Subject: When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations
Verdict
Ignore
Verdict is Ignore because current viability and proof state do not clear the buildability gate.
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": 12, "author": "Mahdi Alkaeed", "title": "When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations"
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": 12, "author": "Mahdi Alkaeed", "title": "When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations", "creation date": null, "modification date": null, "kids": []}
Inference
{"file name": "input.pdf", "number of pages": 12, "author": "Mahdi Alkaeed", "title": "When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations", "creation date": null, "modification date": null, "kids": []}
Hardware
{"file name": "input.pdf", "number of pages": 12, "author": "Mahdi Alkaeed", "title": "When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations", "creation date": null, "modification date": null, "kids": []}
Receipt path
/buildability/when-large-language-models-fail-in-healthcare-evaluating-sensitivity-to-prompt-variations
Paper ref
when-large-language-models-fail-in-healthcare-evaluating-sensitivity-to-prompt-variations
arXiv id
2606.07237
Generated at
2026-06-08T17:15:26.905Z
Evidence freshness
fresh
Last verification
2026-06-08T17:15:26.905Z
Sources
3
References
0
Coverage
50%
Lineage hash
e61e47ddf4bfd1378afc2e92861512b1d4eba748097d640e5705fb6dc174140e
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.
repo_url
references