When Users Change Their Minds: Measuring and Repairing Intent Drift in LLM Agents

arXiv:2609.32520 · cs.CL, cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Benchmark — 17× in this paper · explained in 2 episodes
• Calibration — 16× in this paper · explained in 7 episodes
• Distillation — 13× in this paper · explained in 2 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• International Conference on Learning Representations — 4× in this paper
• On-Policy Distillation — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Fidelity — 3× in this paper · explained in 2 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• Verifier — 2× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Findings of the Association for Computational Linguistics — 1× in this paper
• International Conference on Machine Learning — 1× in this paper
• RAG — 1× in this paper · explained in 3 episodes
• Text-to-SQL — 1× in this paper · explained in 3 episodes

Sources

• Evaluating Large Language Models Trained on Code
• Deliberative Alignment: Reasoning Enables Safer Language Models
• Orca: Progressive Learning from Complex Explanation Traces of GPT-4
• Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents
• LLMs Get Lost in Evolving User Intent
• When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

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