multi-turn

This paper systematically studies context interference in multi-turn LLM-based search agents, finding that interference primarily arises from the latest retrieved documents, and introduces a distill-based context refiner to mitigate it. Incorporating context refinement into RL training pipelines significantly improves reliability and efficiency.

This paper proposes a dual-loop self-evolution framework for multi-turn empathetic dialogue, using verifiable emotion feedback to optimize policy and adapt training distribution. On SAGE, it improves Qwen3-8B Overall from 53.87 to 79.24, outperforming uniform emotion-reward RL by 7.23 points.

This paper introduces a three-condition experimental framework and a benchmark of 24,300 prompts to study how biased user turns modulate cognitive bias expression in frontier LLMs under multi-turn interactions. It finds that biased conversational context amplifies bias in most models, while explicit bias cues can trigger alignment-related suppression.

This paper proposes DASH-OPD, a discrepancy-aware switching method for on-policy distillation in multi-turn LLM agent training, which adaptively toggles between teacher and student executors based on drift and recovery evidence to improve efficiency and performance.

M3-DuplexBench is a new multi-turn, multilingual, multidomain benchmark for evaluating full-duplex spoken dialogue systems, supporting English and Japanese across casual conversation and question answering domains.

MMShopBench introduces a real-log benchmark for multimodal, multi-turn shopping agents, requiring joint inference from user images and dialogue, with an offline sandbox and training set to improve open-source model performance.

Introduces CMT-RAG, a complementary memory framework for multi-turn multi-hop conversational RAG that aligns conversational memory with retrieval using sub-question-level reasoning traces. Also presents MuMu-QA, a benchmark with cross-turn sub-question dependencies.

Introduces PALATE, a scalable benchmark for evaluating role-playing agents using person-aligned LLM-simulated users and personalized rubrics, addressing limitations of fixed-history evaluation.

This paper presents an evaluation of multi-turn multimodal diagnostic reasoning using challenging real-world clinical cases, aiming to assess AI models' ability to handle complex medical scenarios.

CallBench is a Chinese benchmark for evaluating dual-goal coordination in phone call assistants, containing 50,000 multi-turn dialogues across six scenarios, with a preset-aware evaluation protocol covering semantic understanding, safety, and dialogue rhythm.

EvoCode-Bench is a multi-turn coding benchmark with 26 tasks across 5 domains, designed to evaluate AI agents on evolving specifications and cumulative testing in a persistent workspace, revealing that single-turn scores dramatically overstate reliability.

This paper introduces a controlled multi-turn environment to systematically study long-horizon planning ability in foundation model agents across pre-training, post-training (via GRPO and on-policy distillation), and integration (via multi-teacher on-policy distillation) stages.

This paper introduces a framework to convert static tasks into dynamic multi-turn conversations to evaluate how well LLMs track evolving user intent, finding that strong static performance does not transfer to dynamic settings.

StabilityBench is a benchmark operator that transforms single-turn LLM evaluations into multi-turn interactions with user simulations and baiting modules, revealing significant performance instability in current models and motivating more realistic evaluation protocols.

This paper identifies and characterizes two failure modes in LLM-based autonomous agents—Safety Drift and Operational Hallucination—and proposes a lightweight architectural layer to intercept violations without false positives.

User reports that Gemma 4 is lazy and poor at multi-turn agentic tasks compared to other models like Qwen, DeepSeek, and GPT-OSS, despite being a good chatbot.

LAPO proposes a leave-one-turn attribution method for self-generated process rewards in multi-turn search reasoning, enabling fine-grained credit assignment without external reward models. It achieves state-of-the-art results across seven datasets.

This paper proposes ReOPD, a method for on-policy distillation of LLM agents that reuses pre-collected teacher trajectories as replayed prefixes, achieving improved efficiency and accuracy without new environment interactions.

The paper introduces ThReadMed-QA, a multi-turn medical dialogue dataset, and evaluates five LLMs on correcting patient misconceptions, finding substantial degradation over subsequent turns.

This paper proposes DC-GRPO, a turn-level credit assignment framework for multi-turn LLM jailbreak learning, achieving over 98% attack success rates across benchmarks, outperforming existing methods.