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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.