multi-turn

IdeaTrail is a dataset of multi-turn process trajectories for scientific ideation, synthesizing research processes from evidence gathering to proposal construction using a Generator–Advisor loop to ensure grounding.

EvoCUA-1.5 introduces an online reinforcement learning framework for multi-turn computer-use agents, achieving a 63.2% success rate on OSWorld-Verified and outperforming comparable open-weight models up to 35B parameters through step-level policy optimization and dynamic curriculum learning.

The article identifies a structural flaw in voice agents where they cannot detect when they over-promise across multiple conversation turns, and describes building a deterministic checker that flags contradictions without relying on LLM evaluation.

A second-order early warning signal for multi-turn prompt injection is introduced, based on information geometry and a statistical manifold. The method uses a meta rate derived from the second derivative of the stability parameter to predict adversarial trajectories before threshold crossing, providing proactive detection.

BayesBench evaluates how closely large language models' belief updates match Bayesian reasoning in multi-turn evidence accumulation tasks, finding that while scaling improves latent inference, models struggle to use that understanding for downstream predictions.

This paper investigates whether natural-language feedback leads to improvement beyond repeated attempts alone in multi-turn language agent settings. Using a controlled student-teacher protocol across multiple benchmarks, the authors find that self-generated feedback adds little, while strong external teachers yield larger gains, and that the student's ability to act on feedback is a key bottleneck.

The paper introduces ATOD, a hybrid online distillation algorithm combining on-policy distillation and reinforcement learning for training small language model agents in multi-turn tasks, featuring an annealed OPD-RL schedule and Turn-level Disagreement-Uncertainty Reweighting to improve dense supervision.

SWE-Interact is a new testbed that evaluates coding agents in realistic multi-turn, user-driven software engineering tasks, revealing that strong single-turn benchmark performance does not reliably transfer to interactive, iterative workflows where agents must discover user intent and adapt to evolving requirements.

SWE-Together is a multi-turn coding benchmark created from real user-agent interactions, featuring a reactive LLM simulator to evaluate agents based on both final correctness and interaction efficiency.

This article introduces a new natural-language testing system for AI agents that uses simulated isolates to automatically generate multi-turn simulations and evaluate agent behavior, helping developers catch regressions from prompt changes.

A new benchmark for multi-turn prompt injection attacks reveals that most current defenses fail to detect sophisticated, multi-step attacks.

Introduces EHRNote-ChatQA, a benchmark for evidence-grounded multi-turn clinical question answering over multiple discharge summaries, constructed with expert validation. Benchmarking 22 LLMs reveals challenges in evidence grounding and multi-turn error accumulation.

CacheRL trains small agent foundation models for multi-step tool-calling tasks, achieving 92% process accuracy (approaching GPT-5's 94%) with 100x less compute using cached rollouts and hybrid reward shaping, with innovations in knowledge transfer, cache-aware rewards, and iterative SFT/GRPO training.

DLawBench is a new benchmark for evaluating large language models in multi-turn legal consultation, covering Chinese and US law with four client types. Experiments show significant room for improvement, with the best model achieving only 0.562 on legal reasoning.

The Shopping Reasoning Bench is an expert-authored benchmark for evaluating multi-turn conversational shopping assistants, with 525 missions and over 10,000 binary rubrics. Evaluations of GPT, Claude, and Gemini show that current models achieve only 57-77% pass rates, revealing significant gaps in expert-level shopping reasoning.

HERO introduces a hindsight-enhanced self-distillation framework that uses environment observations as locally aligned feedback to improve multi-turn agent capabilities, outperforming existing methods on TauBench and WebShop, especially under limited turn budgets.

This paper introduces ISE, a three-stage synthesis paradigm for generating multi-turn OS-agent trajectories with grounded execution, demonstrating that fine-tuning on the resulting ISE-Trace dataset significantly improves agent performance on ClawEval.

IntentKV introduces a cross-turn intent-aware KV cache pruning method for multi-turn LLM agents, maintaining session-level query memory to efficiently prune cache without accuracy loss, significantly reducing token usage and KV reads.

This paper studies a deployed LLM-as-judge system for evaluating multi-turn conversational agents and finds it catches far fewer defects than human review, revealing a structured blind-spot taxonomy and routing failures.

Proposes Adwm, an autoregressive diffusion world model for off-policy evaluation of LLM agents, enabling reliable value estimates from pre-collected trajectories without online interaction.