# Talking to a Know-It-All GPT or a Second-Guesser Claude? How Repair reveals unreliable Multi-Turn Behavior in LLMs

arXiv: https://arxiv.org/abs/2604.19245 · HTML: https://mlsanity.com/paper/2604.19245/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration](https://mlsanity.com/paper/2305.13626/) (2023)
- [Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem](https://mlsanity.com/paper/2403.03558/) (2024)
- [Asking Clarification Questions to Handle Ambiguity in Open-Domain QA](https://mlsanity.com/paper/2305.13808/) (2023)
- [Python Code Generation by Asking Clarification Questions](https://mlsanity.com/paper/2212.09885/) (2022)
- [STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents](https://mlsanity.com/paper/2405.12059/) (2024)
- [LLMs Struggle to Reject False Presuppositions when Misinformation Stakes are High](https://mlsanity.com/paper/2505.22354/) (2025)
- [Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions](https://mlsanity.com/paper/2506.08952/) (2025)
- [Referential ambiguity and clarification requests: comparing human and LLM behaviour](https://mlsanity.com/paper/2507.10445/) (2025)
- [Clarifying the Path to User Satisfaction: An Investigation into Clarification Usefulness](https://mlsanity.com/paper/2402.01934/) (2024)
- [Model Analysis & Evaluation for Ambiguous Question Answering](https://mlsanity.com/paper/2305.12483/) (2023)

## Built on by (most cited first, 1 of 1)

- [Interaction Readiness: A Framework for Building and Evaluating AI Agents in Human Roles](https://mlsanity.com/paper/2608.12358/) (2026)

## Abstract (arXiv)

Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we investigate how LLMs engage in the interactive process of repair in multi-turn dialogues around solvable and unsolvable math questions. We examine whether models initiate repair themselves and how they respond to user-initiated repair. Our results show strong differences across models: reactions range from being almost completely resistant to (appropriate) repair attempts to being highly susceptible and easily manipulated. We further demonstrate that once conversations extend beyond a single turn, model behavior becomes more distinctive and less predictable across systems. Overall, our findings indicate that each tested LLM exhibits its own characteristic form of unreliability in the context of repair.