arXiv cs.AI· Pier Mattia Basciano, Gianvito Antenori, Cristiano Andolfi, Giovanni Farina, Salvatore Monteleone·· 3 小时前AI 评分38
NeSySpec：面向飞机工程规范审计的神经符号技术智能框架
NeSySpec: Neuro-Symbolic Technical Intelligence for Auditing Engineering Specifications with an Application to Aircraft
AI 导读
研究者提出 NeSySpec，一个基于 Logic Tensor Networks 的可解释神经符号审计框架，可在一条流水线中诊断违规约束、定位被篡改字段、给出最小修改修复并关联图像证据。
正文 · 原文
Abstract:Engineers who audit a declared specification of an engineered system need to know which physical constraints it respects, which it violates, which field was altered and how to correct it. NeSySpec is an integrated and interpretable neuro-symbolic audit framework, built on Logic Tensor Networks, that answers these questions in one pipeline. Its axioms encode the physical relations of the domain as differentiable graded predicates whose tolerances are learned from a fleet of real records; from the same degrees, it diagnoses the violated constraint, identifies the altered field by abduction, proposes a minimum-change correction and ties image evidence to the parts it describes, abstaining exactly where evidence is missing, and returns all of it in one verification report. We validate NeSySpec on 107 aircraft types, with 16 test aircraft of 9 families held out of training, on specification records paired with ground photographs and satellite imagery and on synthetic errors: generated corruptions, edits labeled from the edit alone and deliberate falsifications. NeSySpec combines all the assessed audit outputs in one pipeline, which no learned, rule-based, solver-based, neuro-symbolic or language-model comparator does; on the test aircraft none is ahead of it in the macro-F1 of its constraint verdicts, in naming the violated constraint and the altered field or in the repair error, and it is competitive in record and image-pair detection. Its diagnosis names the violated constraint in 94.2% of corrupted test records, against 79.5% for classical Fellegi-Holt diagnosis, and on edits labeled from the edit alone, without retraining, abduction finds the altered field in 59.3% of records, against 30.6% for minimum-cardinality localization with random tie- breaking. Only the knowledge base, the visual predicates, the corruption generator and the data are specific to the domain.
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Symbolic Computation (cs.SC) |
| Cite as: | arXiv:2610.35403 [cs.AI] |
| (or arXiv:2610.35403v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35403 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pier Mattia Basciano [view email]
[v1]
Mon, 26 Oct 2026 13:18:14 UTC (1,814 KB)
来源：arXiv cs.AI · arxiv.org