不用微调，加一个显式情境状态就能让多轮智能体少做无效动作、记得关键事实，三个基准上八种模型都有效果提升。
IDSS 是一个无需训练的框架，在对话之外维护显式情境状态，把工具返回解析为带来源的实体和属性，并追踪用户意图、所需变量、约束和执行状态。在三个交互基准上对八种 LLM 的测试显示，IDSS 能提升任务完成率、偏好获取效果和交互效率，尤其在多实体协调、用户约束变化和约束感知重规划的任务上增益明显。消融和误差分析表明，收益来自事实持久化、以意图为中心的状态追踪与约束建模之间的配合。
Intent-Driven Situation Tracking for User-Centric Multi-Turn Agents
User-centric multi-turn agents must act on an evolving task situation shaped by changing user intents, accumulated tool-grounded facts, missing information, and execution constraints. Existing context-management methods improve the use of past interaction history, but rarely maintain an explicit situation state that separates grounded facts from task-state judgments. As a result, agents often need to infer fine-grained attributes, task dependencies, and constraint satisfaction implicitly from dialogue traces. We propose Intent-Driven Situation States (IDSS), a training-free framework that maintains an explicit situation state alongside the dialogue. IDSS parses tool returns into provenance-aware entities and attributes, tracks user intents, required variables, constraints, and execution status, and propagates new facts to task constraints to update action executability. This allows agents to avoid infeasible actions, advance dependent goals, and reuse relevant information without repeatedly searching raw history. Experiments on three interactive benchmarks across eight LLMs show that IDSS improves task completion, preference elicitation, and interaction efficiency, with clear gains on tasks involving multi-entity coordination, evolving user constraints, and constraint-aware replanning. Ablations and error analyses show that these improvements come from the interaction between fact persistence, intent-centered state tracking, and constraint modeling. These results suggest that explicit situation tracking offers an effective alternative to history-centric context management for reliable user-centric multi-turn agents.