multi-turn-dialogue

This paper introduces ReBIND, a framework that measures, predicts, and repairs behavioral relapse in black-box LLM dialogues where models continue to follow revoked constraints. Experiments with HumanEval show that ahead-of-time compilation significantly reduces relapse compared to a baseline, while adaptive interventions add no detectable gain.

Introduces Hy-MultiTurn, a Chinese benchmark for deep multi-turn dialogue understanding, evaluating 22 frontier models across six controlled modes (constraint memory, precise execution, constraint synthesis, object localization, action suppression, reference resolution) with 209 tasks spanning 12-76 turns. Even the strongest model, GPT-5.5, satisfies only 41.1% of responses.

MedDDC-Eval introduces a diagnosis-decoupled evaluation testbed for multi-turn medical consultation agents, isolating the policy-elicited conversation history from diagnosis generation to enable cleaner measurement of evidence acquisition and diagnostic usefulness.

This paper introduces EYT-Bench, a human-centered benchmark for evaluating LLMs in multi-turn dialogues with a decoupled user simulation, target modeling, and judging design. It reveals that closed- and open-source models differ significantly on objective intent-tracking but are similar on subjective dimensions, and that reasoning improves objective tracking while persona format strongly affects trajectory spread.

This paper identifies a novel failure mode in reasoning models called unfaithful capitulation, where the chain-of-thought remains factually correct across adversarial multi-turn dialogues but the final answer flips wrong, highlighting limitations of current evaluation methods.

This paper theoretically identifies and mitigates context distribution shift in multi-turn dialogue RL, proposing Calibrated Interactive RL that couples interactive RL with simulator alignment to reduce the sim-to-real gap and achieve state-of-the-art performance.

Proposes SKG-Eval, a quasi-deterministic evaluation framework for multi-turn dialogue that uses incremental semantic knowledge graphs to detect cross-turn inconsistencies, contradiction, and topic drift, achieving higher correlation with human judgments.

SCICONVBENCH is a benchmark that evaluates LLMs on multi-turn clarification for ill-posed scientific queries across computational science domains, finding that even frontier models struggle with disambiguation and frequently make silent assumptions.

This paper describes a system for SemEval-2026 Task 8 that uses a three-stage pipeline involving query rewriting with a fine-tuned Qwen model, hybrid retrieval, and cross-encoder reranking to improve multi-turn retrieval performance.

This paper introduces SOMA, a framework for efficient multi-turn LLM serving that uses small language models adapted via soft prompts and LoRA fine-tuning to reduce latency and cost.

This paper introduces TRACE, a framework for turn-aware credit assignment in multi-turn LLM jailbreaking attacks using reinforcement learning, claiming significant improvements in attack success rates and defense alignment.

Presents TurnGate, a turn-level monitor that detects hidden malicious intent in multi-turn dialogues by identifying the earliest turn where a response would enable harmful action, along with the Multi-Turn Intent Dataset (MTID) to support training and evaluation.

Study shows GPT and Claude exhibit distinct, unreliable repair behaviors in multi-turn math dialogues, with some models resisting correction and others over-correcting.

This paper introduces Bipredictability (P) and the Information Digital Twin (IDT), a lightweight method to monitor conversational consistency in multi-turn LLM interactions using token frequency statistics without embeddings or model internals. The approach achieves 100% sensitivity in detecting contradictions and topic shifts while establishing a practical monitoring framework for extended LLM deployments.

Context-Agent proposes a novel framework that models multi-turn dialogue history as dynamic tree structures rather than flat sequences, better capturing the hierarchical and branching nature of natural conversation. The paper introduces the NTM benchmark for evaluating non-linear dialogue scenarios and demonstrates improved task completion rates and token efficiency across various LLMs.