# Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models

arXiv: https://arxiv.org/abs/2504.04717 · HTML: https://mlsanity.com/paper/2504.04717/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2025.

## Built on by (most cited first, 30 of 62)

- [Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning](https://mlsanity.com/paper/2511.16043/) (2025)
- [The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It](https://mlsanity.com/paper/2505.24119/) (2025)
- [BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design](https://mlsanity.com/paper/2508.21184/) (2025)
- [Get Experience from Practice: LLM Agents with Record & Replay](https://mlsanity.com/paper/2505.17716/) (2025)
- [HiMem: Hierarchical Long-Term Memory for LLM Long-Horizon Agents](https://mlsanity.com/paper/2601.06377/) (2026)
- [SAGE-OPD: Selective Agent-Guided Intervention for Multi-Turn On-Policy Distillation](https://mlsanity.com/paper/2606.19659/) (2026)
- [Visual Multi-Agent System: Mitigating Hallucination Snowballing via Visual Flow](https://mlsanity.com/paper/2509.21789/) (2025)
- [InfoPO: Information-Driven Policy Optimization for User-Centric Agents](https://mlsanity.com/paper/2603.00656/) (2026)
- [Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation](https://mlsanity.com/paper/2511.02303/) (2025)
- [Conformal Information Pursuit for Interactively Guiding Large Language Models](https://mlsanity.com/paper/2507.03279/) (2025)
- [Higher Satisfaction, Lower Cost: A Technical Report on How LLMs Revolutionize Meituan's Intelligent Interaction Systems](https://mlsanity.com/paper/2510.13291/) (2025)
- [FlowKV: Enhancing Multi-Turn Conversational Coherence in LLMs via Isolated Key-Value Cache Management](https://mlsanity.com/paper/2505.15347/) (2025)
- [Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks](https://mlsanity.com/paper/2510.02712/) (2025)
- [Attractor States Emerge in Multi-Turn LLM Conversations](https://mlsanity.com/paper/2606.30571/) (2026)
- [BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents](https://mlsanity.com/paper/2510.23458/) (2025)
- [From History to State: Constant-Context Skill Learning for LLM Agents](https://mlsanity.com/paper/2605.05413/) (2026)
- [LPG: Balancing Efficiency and Policy Reasoning in Latent Policy Guardrails](https://mlsanity.com/paper/2605.17329/) (2026)
- [ChatThero: An LLM-Supported Chatbot for Behavior Change and Therapeutic Support in Addiction Recovery](https://mlsanity.com/paper/2508.20996/) (2025)
- [Reducing Belief Deviation in Reinforcement Learning for Active Reasoning](https://mlsanity.com/paper/2510.12264/) (2025)
- [Consistency of Large Reasoning Models Under Multi-Turn Attacks](https://mlsanity.com/paper/2602.13093/) (2026)
- [State-Dependent Safety Failures in Multi-Turn Language Model Interaction](https://mlsanity.com/paper/2603.15684/) (2026)
- [HCAttention: Extreme KV Cache Compression via Heterogeneous Attention Computing for LLMs](https://mlsanity.com/paper/2507.19823/) (2025)
- [KVCompose: Efficient Structured KV Cache Compression with Composite Tokens](https://mlsanity.com/paper/2509.05165/) (2025)
- [Another Turn, Better Output? A Turn-Wise Analysis of Iterative LLM Prompting](https://mlsanity.com/paper/2509.06770/) (2025)
- [ShadowServe: Interference-Free KV Cache Fetching for Distributed Prefix Caching](https://mlsanity.com/paper/2509.16857/) (2025)
- [Test-Time Policy Adaptation for Enhanced Multi-Turn Interactions with LLMs](https://mlsanity.com/paper/2509.23166/) (2025)
- [Do Reasoning LLMs Refuse What They Infer in Long Contexts?](https://mlsanity.com/paper/2602.08874/) (2026)
- [Markovian Generation Chains in Large Language Models](https://mlsanity.com/paper/2603.11228/) (2026)
- [SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems](https://mlsanity.com/paper/2603.17373/) (2026)
- [Models Recall What They Violate: Constraint Adherence in Multi-Turn LLM Ideation](https://mlsanity.com/paper/2604.28031/) (2026)

## Abstract (arXiv)

Recent advances in large language models (LLMs) have substantially improved single-turn task performance, yet real-world applications increasingly demand sophisticated multi-turn interactions. This survey provides a comprehensive review of recent progress in evaluating and enhancing multi-turn LLM interactions. Centered on a task-oriented taxonomy-spanning instruction following in domains such as mathematics and coding, and conversational engagement in role-playing, healthcare, education, and adversarial jailbreak settings-we systematically examine the challenges of maintaining context, coherence, fairness, and responsiveness across prolonged dialogues. We organize existing benchmarks and datasets into coherent categories reflecting the evolving landscape of multi-turn dialogue evaluation, and review a broad spectrum of enhancement methodologies, including model-centric strategies (in-context learning, supervised fine-tuning, reinforcement learning, and architectural innovations), external integration approaches (memory augmentation, retrieval-based methods, and knowledge graphs), and agent-based techniques for collaborative interaction. Finally, we identify open challenges and promising directions for future research to further improve the robustness and effectiveness of multi-turn LLM interactions.