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[2602.17022] ReIn: Conversational Error Recovery with Reasoning Inception
Summary
The paper presents Reasoning Inception (ReIn), a method for improving conversational agents' error recovery without altering their parameters, enhancing their resilience in user interactions.
Why It Matters
As conversational agents become integral to user interactions, their ability to recover from errors is crucial. This research addresses the limitations of current models by proposing a method that enhances error recovery without costly modifications, making it relevant for developers and researchers in AI.
Key Takeaways
- ReIn improves conversational agents' ability to recover from user-induced errors.
- The method integrates error diagnosis and recovery plans without modifying model parameters.
- ReIn outperforms existing prompt-modification techniques in task success.
- The approach is efficient and adaptable, enhancing agent resilience in diverse scenarios.
- Jointly defining recovery tools with ReIn can improve the effectiveness of conversational agents.
Computer Science > Computation and Language arXiv:2602.17022 (cs) [Submitted on 19 Feb 2026] Title:ReIn: Conversational Error Recovery with Reasoning Inception Authors:Takyoung Kim, Jinseok Nam, Chandrayee Basu, Xing Fan, Chengyuan Ma, Heng Ji, Gokhan Tur, Dilek Hakkani-Tür View a PDF of the paper titled ReIn: Conversational Error Recovery with Reasoning Inception, by Takyoung Kim and 7 other authors View PDF HTML (experimental) Abstract:Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue datasets but remain vulnerable to unanticipated, user-induced errors. Rather than focusing on error prevention, this work focuses on error recovery, which necessitates the accurate diagnosis of erroneous dialogue contexts and execution of proper recovery plans. Under realistic constraints precluding model fine-tuning or prompt modification due to significant cost and time requirements, we explore whether agents can recover from contextually flawed interactions and how their behavior can be adapted without altering model parameters and prompts. To this end, we propose Reasoning Inception (ReIn), a test-time intervention method that plants an initial reasoning into the agent's decision-making process. Specifically, an external inception module identifies predefined errors within the dialogue context and generates recovery plans, which are subsequently integrated into the agent's internal reasoning pro...
