MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents

Modern task-oriented dialogue (TOD) systems increasingly rely on large language model (LLM) agents, leveraging Retrieval-Augmented Generation (RAG) and long-context capabilities for long-term memory utilization. However, these methods are primarily based on semantic similarity, overlooking task intent and reducing task coherence in multi-session dialogues. To address this challenge, we introduce MemGuide, a two-stage framework for intent-driven memory selection. (1) Intent‑Aligned Retrieval matches the current dialogue context with stored intent descriptions in the memory bank, retrieving QA‑formatted memory units that share the same goal. (2) Missing‑Slot Guided Filtering employs a chain‑of‑thought slot reasoner to enumerate unfilled slots, then uses a fine‑tuned LLaMA‑8B filter to re‑rank the retrieved units by marginal slot‑completion gain. The resulting memory units inform a proactive strategy that minimizes conversational turns by directly addressing information gaps. Based on this framework, we introduce the MS-TOD, the first multi-session TOD benchmark comprising 132 diverse personas, 956 task goals, and annotated intent-aligned memory targets, supporting efficient multi‑session task completion. Evaluations on MS-TOD show that MemGuide raises the task success rate by 11\% (88\%→99\%) and reduces dialogue length by 2.84 turns in multi-session settings, and maintains parity with single‑session benchmarks.