Dialogue
Research papers, repositories, and articles about dialogue
Showing 4 of 4 items
TIME: Temporally Intelligent Meta-reasoning Engine for Context Triggered Explicit Reasoning
TIME teaches dialogue models to drop short "thinking" blocks only when time gaps or context shifts actually demand deeper reasoning. Models keep answers compact while still reasoning hard when conversations get tricky or span days instead of seconds.
SpeakRL: Synergizing Reasoning, Speaking, and Acting in Language Models with Reinforcement Learning
Argues that current task-oriented agents are over-optimized as passive followers and under-use conversation as an action. SpeakRL introduces a reinforcement-learning setup that rewards models for asking clarifying questions when the user’s intent is ambiguous, balancing ‘asking’ vs ‘acting’. On synthetic task-oriented dialogue scenarios, the trained agents substantially improve task completion rates without bloating the number of turns, suggesting that proactive clarification is a powerful, underused control knob.
MAPO: Mixed Advantage Policy Optimization for Long-Horizon Multi-Turn Dialogue
Introduces a new optimization rule for training chat agents over long conversations. The goal: steadier learning and more helpful dialogue without exploding token and compute costs.
MAC: A Multi-Agent Framework for Interactive User Clarification in Multi-turn Conversations
Proposes a multi‑agent architecture where specialized conversational agents coordinate to decide when and how to ask clarification questions in ambiguous multi‑turn tasks. Instead of a monolithic assistant, MAC assigns roles and coordination rules so that the ‘right’ agent takes the lead on resolving uncertainty. This is a nice complement to SpeakRL: one focuses on *whether* to clarify, the other on *who* clarifies and how to coordinate in complex workflows.