🗣️ Dialogue Systems¶

💬 ACL2026 · 10 paper notes

Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning: This paper proposes ConvAgent, which trains a conversational search agent to alternate between retrieval and reasoning across multi-turn interactions by decomposing the RL training reward into three complementary components: outcome reward, information gain reward, and mixed-initiative action reward.
APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI: APEX-MEM proposes a conversational memory system based on a Property Graph, append-only event storage, and a multi-tool retrieval agent. Through a domain-agnostic ontology and retrieval-time temporal reasoning, it achieves 88.88% and 86.2% accuracy on LOCOMO and LongMemEval respectively, significantly outperforming existing structured memory approaches.
Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review: This paper reframes academic rebuttal generation as an "author-in-the-loop" task, contributing the Re3Align dataset (3.4K papers, 440K sentence-level edit annotations, 15K review–rebuttal–revision triples), the REspGen controllable generation framework, and the REspEval evaluation suite comprising 20+ metrics. The framework is systematically validated across 5 state-of-the-art LLMs, demonstrating the effectiveness of author input, controllability, and evaluation-guided refinement.
Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky: This paper proposes DiaFORGE, a disambiguation-centric synthetic data generation pipeline combined with chain-of-thought fine-tuning and a dynamic evaluation framework, enabling open-source LLMs to achieve tool-calling success rates 27 percentage points higher than GPT-4o and 49 percentage points higher than Claude-3.5-Sonnet when facing near-duplicate enterprise APIs.
Discourse Coherence and Response-Guided Context Rewriting for Multi-Party Dialogue Generation: This paper proposes DRCR, the first framework to introduce context rewriting into multi-party dialogue generation, using dual feedback signals of discourse coherence and response quality to construct preference data, and enabling the rewriter and responder to mutually enhance each other through iterative training via dynamic self-evolution.
ETHICMIND: A Risk-Aware Framework for Ethical-Emotional Alignment in Multi-Turn Dialogue: ETHICMIND proposes an inference-time risk-aware alignment framework that jointly analyzes ethical risks and user emotions at each turn of multi-turn dialogue, plans high-level response strategies, and generates replies that balance ethical guidance and emotional resonance, achieving more consistent alignment performance in high-risk and morally ambiguous scenarios without requiring additional training.
SPASM: Stable Persona-driven Agent Simulation for Multi-turn Dialogue Generation: This paper proposes SPASM, a stability-centric persona-driven multi-turn dialogue simulation framework that significantly reduces role drift and echo effects in LLM-LLM conversations through three components: modular persona generation, Egocentric Context Projection (ECP), and termination detection, constructing 45,000 high-quality multi-turn dialogue instances.
Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings: This paper proposes TaDSE, a framework that leverages existing template information in dialogues as auxiliary anchors. Through three stages—template-aware data augmentation, paired contrastive training, and semantic compression inference—TaDSE significantly improves sentence embedding quality for task-oriented dialogue in an unsupervised setting, surpassing previous SOTA and even outperforming supervised commercial embedding models on five benchmarks.
Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation: This paper proposes ProChatIP, a framework that transforms customer service chatbots from passive response tools into proactive information harvesting engines. A dedicated dialogue policy module learns when to probe users for preset target information while minimizing conversation turns and user friction.
VoxMind: An End-to-End Agentic Spoken Dialogue System: This paper proposes VoxMind, a unified framework that endows end-to-end spoken dialogue models with agentic capabilities: explicit reasoning through a "Think-before-Speak" mechanism, combined with a multi-agent dynamic tool management architecture that decouples reasoning latency from tool scale, improving task completion rate from baseline 34.88% to 74.57%, surpassing Gemini-2.5-Pro.