NEIGHBOR: Aligning Service Behaviour and Evidence Use in an LLM Agent for Community Resident Services
Zongyou Yang, Yinghan Hou, Xiaokun Yang
Abstract
Residents who contact a community service desk about a repair or a service charge need answers they can act on, with facts that hold for their case and commitments the desk can keep. In tool-augmented LLM agents, such facts come from documents and records, and the conduct of a service answer is usually specified in a system prompt. We present NEIGHBOR, an LLM agent for community resident services that trains this conduct into the model that drives its tools. NEIGHBOR is deployed on a live resident-service platform for a district of an East Asian city of more than ten million people. It adapts Qwen3-235B-A22B with LoRA on 7,824 supervised fine-tuning examples built from public community-service materials around six service-behaviour targets, among them commitment control and human referral, and the adapted model plans document retrieval and read-only text-to-SQL over the resident's own records. In an offline evaluation with seven configurations, an independent domain expert rated all 350 answers to 50 stratified held-out queries. NEIGHBOR scores 7.74/10 against 5.86 for the same agent running the un-tuned model (+1.88; paired Wilcoxon $p<10^{-8}$; higher on 46 of 50 queries) and improves all six rated dimensions. No tool-augmented answer received a red-line flag (standalone models: 8-14% of answers). An LLM judge ranks the seven configurations much as the expert does (Spearman $ρ=0.93$). Its rubric counts specifics absent from the reference answer as fabricated, so it scores tool-grounded answers 1-2 points lower, enough to change which configuration ranks first. For resident-facing agents, service behaviour is a practical fine-tuning target that raises expert-rated answer quality with the tools held fixed.