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/explaining-too-much-understanding-how-large-language-model-reasoning-traces-influence-performance-and-metacognition
Subject: Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition
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": 27, "author": "Daniela Fernandes; Daniel Buschek; Lev Tankelevitch; Thomas Kosch; Robin Welsch"
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": 27, "author": "Daniela Fernandes; Daniel Buschek; Lev Tankelevitch; Thomas Kosch; Robin Welsch", "title": "Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition", "creation date": "D:20260526021915+00'00'"
Inference
{"file name": "input.pdf", "number of pages": 27, "author": "Daniela Fernandes; Daniel Buschek; Lev Tankelevitch; Thomas Kosch; Robin Welsch", "title": "Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition", "creation date": "D:20260526021915+00'00'"
Hardware
{"file name": "input.pdf", "number of pages": 27, "author": "Daniela Fernandes; Daniel Buschek; Lev Tankelevitch; Thomas Kosch; Robin Welsch", "title": "Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition", "creation date": "D:20260526021915+00'00'"
Receipt path
/buildability/explaining-too-much-understanding-how-large-language-model-reasoning-traces-influence-performance-and-metacognition
Paper ref
explaining-too-much-understanding-how-large-language-model-reasoning-traces-influence-performance-and-metacognition
arXiv id
2605.25856
Generated at
2026-05-27T00:04:46.359Z
Evidence freshness
stale
Last verification
2026-05-27T00:04:46.359Z
Sources
3
References
0
Coverage
50%
Lineage hash
d4b8787cc25c1fed7df94c343cb95d28980cbac4132230782bd1e34604168e7e
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.
Some score or evidence fields are outside the preferred freshness window.
repo_url
references