multi-turn

Introduces EvoCode-Bench, a benchmark of 26 stateful coding tasks across 227 rounds that evaluates coding agents in multi-turn iterative interactions, revealing that single-round performance overestimates multi-round capabilities by 22–40 points.

This paper introduces Found in Conversation (FiC), a training framework using View-Asymmetric Self-Distillation to close the multi-turn performance gap in LLMs. The method teaches models to recover single-turn competence from underspecified multi-turn prompts, achieving 92-100% recovery across model families and sizes.

SEAL proposes a closed-loop framework for jointly evolving LLM agents and their training environments, using diagnosis-guided labels to align both sides. It achieves substantial gains in multi-turn tool-use tasks with only 400 training samples, demonstrating improved robustness and out-of-distribution transfer.

WBench is a comprehensive multi-turn benchmark for evaluating interactive world models across five dimensions using 289 test cases and 1,058 interaction turns, providing automatic sub-metrics and diagnostic insights. It reveals that no single model excels across all dimensions.

Arc Sentry detects multi-turn jailbreaks like Crescendo by reading model internal state rather than text output, catching attacks that text-based monitors miss entirely.

RankJudge is a benchmark generator that creates paired multi-turn conversations with injected flaws to evaluate LLM judges on their ability to correctly identify better and worse responses in complex dialogues.

This paper presents the first systematic study of credit assignment in multi-turn LLM agents, introducing SERL, a selective environment-reweighted learning framework. SERL uses environment feedback to sharpen the RL objective on causally relevant actions, achieving 90.0% and 80.1% success rates on ALFWorld and WebShop respectively.

π-Bench is a new benchmark comprising 100 multi-turn tasks with hidden user intents across 5 domain-specific user personas, designed to evaluate proactive assistance in long-horizon workflows for personal assistant agents.

This paper provides a mechanistic explanation for why LLMs lose track of instructions in long multi-turn interactions, introducing the Goal Accessibility Ratio (GAR) metric and a channel-transition framework. Through ablation studies and residual stream probes, it shows that attention to goal-defining tokens closes over turns while goal information persists in residual representations, with architecture-specific failure modes.

IndicMedDialog is a parallel multi-turn medical dialogue dataset spanning English and nine Indic languages, with a fine-tuned model for personalized symptom elicitation. The dataset is derived from MDDial, enhanced with LLM-generated synthetic consultations and expert verification, supporting multilingual healthcare AI.

This paper revisits Dataset Aggregation (DAgger) for training long-horizon LLM agents, demonstrating that turn-level teacher-student policy interpolation mitigates covariate shift and outperforms existing methods on software engineering benchmarks like SWE-bench Verified.

This paper introduces Sequor, a new benchmark for evaluating how well AI models follow constraints in long, multi-turn conversations. It highlights that current models struggle significantly with maintaining instruction adherence over extended interactions.

This paper introduces AEM, a supervision-free method for agentic reinforcement learning that adapts entropy dynamics at the response level to improve exploration-exploitation trade-offs. It demonstrates performance gains on benchmarks like ALFWorld and SWE-bench by aligning uncertainty estimation with action granularity.

This paper presents the winning system for SemEval-2026 Task 8's generation subtask, using a heterogeneous ensemble of seven LLMs with dual prompting strategies and a GPT-4o-mini judge to select the best response. The system achieved first place with a conditioned harmonic mean of 0.7827, outperforming all baselines and demonstrating the value of model diversity.

Anthropic provides a guide on designing rigorous automated evaluations for AI agents, addressing the complexities of multi-turn interactions and state modifications.