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arXiv AI By Muhammad Faraz Shoaib, Muhammad Qasim, Raisulhaq Mohammed Rizwan, Rahmatullah Safdar, Muzammil Adnan Shaik, Abdul Aleem Mohammed

LOGIC: An LLM Benchmark for Intent-Grounded Change Impact in Aerospace Electrical Systems

Read the original on arXiv AI →

LOGIC is a benchmark and evaluation framework that tests how language models can ground engineering requests in a deterministic inventory of candidate changes before propagating selected changes through an electrical traceability graph. The benchmark includes 168 scenarios—144 for selection and 24 for abstention—and evaluates three 7–8B models against intent‑agnostic, lexical, and structured‑evidence methods. Results show that structured evidence can achieve perfect candidate F1 on anchored cases, while large language models perform better on relational‑paraphrase cases; however, grounding accuracy drops as candidate inventories grow, and strict evidence gating reduces false positives but may also remove correct selections.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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