arXiv：cs.AI· Zihan Tang, Huibiao Zhu·· 16 小时前
LASER：面向长距离协议依赖的可废止证书搜索
LASER: Defeasible Certificate Search for Long-Range Protocol Dependencies
AI 导读
LASER 是一种面向稀疏、可废止协议证据的证明携带式搜索方法，将缺失支撑与未决更正表示为显式义务，由 occurrence-aware 符号检查器判定证书支持、反驳或无法判定某行为。
正文
Abstract:Protocol specifications rarely justify executable behavior in one place: a transition may inherit its source state from a remote control block, name its events only by number, and remain subject to a later correction. Recovering the nearby clause is therefore insufficient; the system must assemble a source-linked certificate and determine whether any stronger rule applies. We introduce LASER, a proof-carrying search method for sparse, defeasible protocol evidence. LASER represents missing support and unresolved corrections as explicit obligations, searches typed evidence routes, and lets an occurrence-aware symbolic checker alone decide whether a certificate warrants, refutes, or leaves a behavior unresolved. On 91 BehaviorCheck families under a matched 2K budget, LASER improves Joint Edge+Proof over the strongest local reader on two model families ($\Delta=+.132$, $p=.017$; $\Delta=+.198$, $p<.001$) while cutting false discoveries by 2--3$\times$. Controlled replays attribute the gain and its limits: neither reference routing nor deterministic citation expansion alone exceeds the local baseline; whole-document reading (29--65K tokens), two-round iterative retrieval, and even oracle minimal-certificate evidence all fail to reach LASER, localizing the bottleneck in typed obligation closure rather than evidence access. An authority-corruption replay shows source-link errors only cause abstention, while the only unsafe-warrant mechanism is mis-recording a Held erratum as Verified, confined to that erratum's family. LASER thus improves long-range dependency recovery while converting incomplete evidence and uncertain authority into explicit residual obligations rather than unsupported behavior.
| Comments: | 11 pages, 6 figures, 10 tables |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.3; I.2.7; D.2.4 |
| Cite as: | arXiv:2610.14552 [cs.SE] |
| (or arXiv:2610.14552v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14552 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zihan Tang [view email]
[v1]
Sat, 10 Oct 2026 04:18:25 UTC (3,769 KB)
来源：arXiv：cs.AI · arxiv.org