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/human-machine-co-boosted-bug-report-identification-with-mutualistic-neural-active-learning
Subject: Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning
Verdict
Watch
Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Data
111:50 Guoming Long, Shihai Wang, Hui Fang, and Tao Chen significantly reduced efforts required for labeling. Specifically, the superiority lies in readability and identifiability
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
111:50 Guoming Long, Shihai Wang, Hui Fang, and Tao Chen significantly reduced efforts required for labeling. Specifically, the superiority lies in readability and identifiability, with improvements of 78.6% and 171.5%, respectively, supported by significant statistical evidence
Inference
111:50 Guoming Long, Shihai Wang, Hui Fang, and Tao Chen significantly reduced efforts required for labeling. Specifically, the superiority lies in readability and identifiability, with improvements of 78.6% and 171.5%, respectively, supported by significant statistical evidence
Hardware
111:50 Guoming Long, Shihai Wang, Hui Fang, and Tao Chen significantly reduced efforts required for labeling. Specifically, the superiority lies in readability and identifiability, with improvements of 78.6% and 171.5%, respectively, supported by significant statistical evidence
Receipt path
/buildability/human-machine-co-boosted-bug-report-identification-with-mutualistic-neural-active-learning
Paper ref
human-machine-co-boosted-bug-report-identification-with-mutualistic-neural-active-learning
arXiv id
2604.18862
Generated at
2026-04-22T20:26:24.460Z
Evidence freshness
stale
Last verification
2026-04-22T20:26:24.460Z
Sources
3
References
0
Coverage
50%
Lineage hash
89e94a558bcd9d37086c14cf0d150af9ada2febac4ad10ff7dc582d40b8e757f
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