3arXiv cs.CL (Computation and Language)·
DCC framework proposes dynamic commonsense coordination for empathetic dialogue generation
A new arXiv preprint introduces DCC (Dynamic Commonsense Coordination), a framework for empathetic response generation that uses three modules—residual-based commonsense interaction, association-guided filtering, and iterative commonsense-aware decoding—to dynamically coordinate commonsense knowledge across understanding and generation stages. Experiments on the Empathetic-Dialogues benchmark show improvements in emotion classification accuracy and response diversity over the CEM baseline. An LLM-based blind evaluation also finds gains in relevance, coherence, and informativeness. Code will be released publicly.
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Conceptual framework for analyzing dialogue dynamics in human-AI and multi-agent collaborative problem-solving
A new arXiv preprint proposes a hierarchical two-layer coding scheme for analyzing dialogue in collaborative problem-solving, integrating cognitive and metacognitive dimensions. The framework is validated across nine datasets spanning multiple domains and is positioned to apply to both human-AI and multi-agent collaboration contexts. A key finding is that metacognitive regulation is a strong discriminator of deeper collaboration quality.
C-DIC: Context-Driven Incremental Compression for efficient long-horizon multi-turn dialogue
A new arXiv preprint introduces Context-Driven Incremental Compression (C-DIC), a method for managing growing dialogue history in conversational agents by treating conversations as interleaved contextual threads with revisable per-thread compression states stored in a compact dialogue memory. A retrieve-revise-write-back loop shares information across turns and updates stale memories, while truncated backpropagation-through-time (TBPTT) is adapted to learn cross-turn dependencies. Experiments on long-form dialogue benchmarks show stable inference latency and perplexity over hundreds of turns, addressing compounding errors seen in existing context compressors.
Democratic ICAI uses structured persona debate to derive richer alignment steering principles
Researchers introduce Democratic ICAI, an extension of Inverse Constitutional AI (ICAI) that gathers multiple competing rationales through structured persona debate rather than single-pass explanations. The method derives natural-language steering principles from these richer signals and applies them via LLM-based and decision-tree judges. Experiments on creative preference benchmarks MuCE-Pref and LiTBench show improved preference prediction over deliberative prompting and principle-based baselines, with LLM annotators preferring the resulting constitutions. The work addresses a core limitation of pairwise preference labels — that they reveal final choices but not the underlying reasoning.
SIMAX framework generates annotated synthetic clinician-patient dialogues for AI communication coding evaluation
Researchers introduce SIMAX, a framework for generating controlled, annotated synthetic clinician-patient dialogues to support development and evaluation of AI-driven clinical communication coding systems. The framework produces dialogues with reference behavioral annotations using two codebooks (Global and WISER), generating 3,388 simulated dialogues across three medical specialties with varied personas and accent conditions. Evaluation shows reasonable speech naturalness and high transcription fidelity, with downstream testing revealing the framework can expose sensitivity gaps in communication coding systems. The work addresses a data scarcity bottleneck in deploying ambient AI scribes in clinical settings.
LLawCo framework teaches embodied multi-agent LLMs to derive and follow cooperation laws
Researchers from MERL propose LLawCo (Learning Laws of Cooperation), a framework that enables embodied LLM-based agents to autonomously align with partners and task objectives in decentralized, partially observable environments. Agents reflect on past failures to extract misaligned behavioral patterns and derive high-level behavioral laws (e.g., 'Talk when necessary', 'Wait for partner'), which are incorporated into reasoning via supervised fine-tuning. The authors also introduce PARTNR-Dialog, a new large-scale multi-agent communicative planning benchmark, and report average success rate improvements of 4.5% on PARTNR-Dialog and 6.8% on TDW-MAT over state-of-the-art open-source communicative agent frameworks across four backbone LLMs.
Pair-level judgement outperforms dialogue-level generation for LLM emotion-cause extraction
A new arXiv paper investigates how task formulation affects LLM performance on emotion-cause pair extraction in conversation (ECPEC), finding that pair-level judgement consistently outperforms dialogue-level generation across 18 controlled comparisons. The authors show LLMs can recognize 92.7–98.1% of emotion-cause relations when queried explicitly but struggle to discover complete pair sets autonomously. They introduce an auxiliary retriever targeting ambiguous boundary cases, achieving F1 improvements of 0.50–1.46 points across three datasets at only 1.49x inference cost. The findings have broader implications for how task decomposition and candidate scoping affect LLM utilization in structured extraction tasks.
MedDDC-Eval: Diagnosis-Decoupled Evaluation Framework for Multi-Turn Medical Consultation Agents
Researchers introduce MedDDC-Eval, a benchmark framework that decouples history-elicitation quality from diagnosis generation in multi-turn medical consultation agents, using a shared frozen reader to hold the history-to-diagnosis mapping constant. The paper demonstrates that varying only the diagnostic reader shifts diagnosis F1 by 2.2–19.0 points and reverses 18–36% of pairwise policy orderings, exposing confounds in coupled evaluation. The authors also apply Group Relative Policy Optimization (GRPO) to post-train Qwen3-32B using diagnosis-result and trajectory feedback, achieving 9.7 and 4.6 total-score point improvements on two evaluation splits. The work addresses a methodological gap in evaluating medical dialogue agents by enabling controlled attribution of policy quality.
SkillComposer: Structured skill composition for LLM agents via constrained autoregressive decoding
A new arXiv preprint introduces SkillComposer, a method that frames skill selection for LLM agents as a structured prediction problem — jointly deciding which skills to activate, how many, and in what order via a constrained autoregressive decoder over skill identifiers. The approach addresses a bottleneck in growing skill libraries where existing retrieval and full-context methods fail to capture the joint nature of skill composition. Evaluated on SkillsBench across two production-grade coding agents (GPT-5.2-Codex and Gemini-3-Pro-Preview), SkillComposer raises pass rates by +23.1 and +18.2 percentage points over no-skill baselines, matching gold-skill retrieval upper bounds at lower prompt-token cost.