Microsoft Research: LLMs Fail to Track Evolving User Intent in Multi-Turn Conversations
alan_ritter · x · 2026-07-28
A new paper from Microsoft Research introduces a framework transforming static, single-turn tasks into dynamic multi-turn conversations to evaluate LLMs. The study reveals that as user intent evolves—being incrementally disclosed, revised, or redirected—today's models struggle significantly to track and act on it. High performance in static settings does not transfer to dynamic scenarios, exposing a critical gap in LLMs acting as collaborative agents.
More from coding & agent
- Building a portable context vault in Notion so every AI agent knows you from day one — ivanhzhao · 2026-10-16
- Reflection AI open-sources Beam: 501B-param MoE model under Apache 2.0 for agentic coding — arena · 2026-10-16
- Deep Research API showdown: OpenAI, Perplexity, Parallel, Tavily, Gemini, Valyu compared — bar_raiser333 · 2026-10-16
- Geoffrey Huntley open-sources Overalls, a self-hosted AI developer platform under MIT — kieranklaassen · 2026-10-16
- Dev shows 199.7bn tokens over 200 days on OpenAI Codex, peak 8.6bn/day — Dimillian · 2026-10-16
- Ask Claude to open its changes in nvim and scroll to the exact line for review — max__drake · 2026-10-16