面向以用户为中心的多轮智能体的意图驱动情境跟踪

中文摘要 AI 辅助

以用户为中心的多轮智能体必须依据不断变化的用户意图、积累的工具落地事实、缺失的信息以及执行约束所形成的动态任务情境来采取行动。现有的上下文管理方法改进了对过往交互历史的利用，但很少维护明确的情境状态，将落地事实与任务状态判断分离开来。因此，智能体常常需要从对话轨迹中隐式推断细粒度属性、任务依赖关系和约束满足情况。我们提出了意图驱动情境状态（Intent-Driven Situation States, IDSS），这是一种无需训练的框架，可在对话过程中维护明确的情境状态。IDSS将工具返回解析为具有来源感知的实体和属性，跟踪用户意图、所需变量、约束和执行状态，并将新事实传播到任务约束中以更新动作可执行性。这使得智能体能够避免不可行的动作、推进依赖目标并复用相关信息，而无需反复搜索原始历史。在针对八个大型语言模型（LLM）的三个交互式基准上进行的实验表明，IDSS提升了任务完成度、偏好 elicitation（ elicitation 为保留的术语）和交互效率，在涉及多实体协调、动态用户约束和约束感知重规划的任务上取得了显著增益。 ablation（ ablation 为保留的术语）和错误分析表明，这些改进来自事实持久性、以意图为中心的状态跟踪和约束建模之间的交互。这些结果表明，明确的情境跟踪为构建可靠的以用户为中心的多轮智能体提供了一种替代以历史为中心的上下文管理的有效方案。

英文摘要

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.

发表机构

• University of Electronic Science and Technology of China（电子科技大学）
• Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）

机构由 AI 辅助整理，请以论文原文为准。