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arXiv AI By Tianyang Liu, Jiayue Wang, Zhanhao Cheng, Changyang Wang, Shaoshu Zhu, Zhenghao Wu, Hadi Tabatabaee Malazi

LocalIntent: Outcome-Specific Structured Generation for Backend-Compatible Smart Building Automation with Local LLMs

Read the original on arXiv AI →

The paper introduces LocalIntent, a structured generation framework that routes smart building automation requests to either ordinary or persistent-rule branches using locally deployed large language models. LocalIntent improves outcome-specific prompting, yielding higher accuracy in intent understanding, device identification, and action/parameter correctness compared to generic two-stage prompting, especially for rule-based requests. Experiments on Gemma and Qwen models (2B‑9B) across 101 non‑persistent and 54 persistent‑rule records demonstrate significant performance gains in rule-mode correctness and overall intent handling.

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

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.

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