Language agents for optimal conversation stopping (stoppingagents.com)

Researchers propose "stopping agents": language-model-based decision agents that dynamically decide whether to wait for more conversational input or quit the exchange. Rather than passively following fixed timeouts or turn-taking rules, these agents observe the evolving conversation text and make sequential wait-or-quit decisions that explicitly trade off the benefit of gathering more information against the cost of waiting (latency, compute, or user annoyance). In effect, stopping agents treat dialog progression as an optimal-stopping problem and use the LLM’s contextual understanding to decide when additional context is unlikely to change the optimal outcome. This idea is significant because it embeds decision-theoretic reasoning directly in LLM-driven interaction loops, enabling more efficient, safer, and cost-aware conversations. Key technical implications include applying expected-value-of-information reasoning, learning policies that balance information gain with waiting costs, and integrating stopping logic into multi-turn, multi-agent, and human-AI settings. Practically, stopping agents could reduce unnecessary compute and latency, prevent premature or overly verbose replies, and improve coordination in systems where multiple modules or humans contribute incremental signals. The approach opens avenues for training and evaluating dialog policies that optimize end-to-end utility rather than fixed protocol heuristics.