Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models

• URL: http://arxiv.org/abs/2509.21849v1
• Date: Fri, 26 Sep 2025 04:20:37 GMT
• Title: Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models
• Authors: Ziqi Liu, Ziyang Zhou, Yilin Li, Haiyang Zhang, Yangbin Chen,
• Abstract summary: Empathetic response generation is a crucial task for creating more human-like and supportive conversational agents.<n>Existing methods face a core trade-off between the analytical depth of specialized models and the generative fluency of Large Language Models.<n>We propose TRACE, a novel framework that models empathy as a structured cognitive process by decomposing the task into a pipeline for analysis and synthesis.
• Score: 19.450298798183166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Empathetic response generation is a crucial task for creating more human-like and supportive conversational agents. However, existing methods face a core trade-off between the analytical depth of specialized models and the generative fluency of Large Language Models (LLMs). To address this, we propose TRACE, Task-decomposed Reasoning for Affective Communication and Empathy, a novel framework that models empathy as a structured cognitive process by decomposing the task into a pipeline for analysis and synthesis. By building a comprehensive understanding before generation, TRACE unites deep analysis with expressive generation. Experimental results show that our framework significantly outperforms strong baselines in both automatic and LLM-based evaluations, confirming that our structured decomposition is a promising paradigm for creating more capable and interpretable empathetic agents. Our code is available at https://anonymous.4open.science/r/TRACE-18EF/README.md.

Related papers

• Synergizing Understanding and Generation with Interleaved Analyzing-Drafting Thinking [154.2388970262703]
  Unified Vision-Language Models (UVLMs) aim to advance multimodal learning by supporting both understanding and generation within a single framework.<n>We introduce the interleaved Analyzing-Drafting problem-solving loop (AD-Loop), a new think paradigm that alternates between analytic and drafting operations.<n>By interleaving textual thoughts with visual thoughts, AD-Loop enables models to iteratively refine both comprehension and outputs, fostering genuine synergy.
  arXiv  Detail & Related papers  (2026-02-24T23:26:09Z)
• Exploring a New Competency Modeling Process with Large Language Models [0.0]
  This study proposes a new competency modeling process built on large language models (LLMs)<n> Specifically, we leverage LLMs to extract behavioral and psychological descriptions from raw textual data.<n>We introduce a learnable parameter that adaptively integrates different information sources, enabling the model to determine the relative importance of behavioral and psychological signals.
  arXiv  Detail & Related papers  (2026-02-13T16:46:51Z)
• Toward Formalizing LLM-Based Agent Designs through Structural Context Modeling and Semantic Dynamics Analysis [13.919694566467053]
  We argue that this fragmentation stems from the absence of an analyzable, self-consistent formal model that enables characterization and comparison of LLM agents.<n>To address this gap, we propose the texttt Structural Context Model, a formal model for analyzing and comparing LLM agents from the perspective of context structure.<n>We demonstrate the effectiveness of the complete framework on dynamic variants of the monkey-banana problem, where agents engineered using our approach achieve up to a 32 percentage points improvement in success rate.
  arXiv  Detail & Related papers  (2026-02-09T05:15:11Z)
• Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection [59.04089915447622]
  ForenAgent is an interactive IFD framework that enables MLLMs to autonomously generate, execute, and refine Python-based low-level tools around the detection objective.<n>Inspired by human reasoning, we design a dynamic reasoning loop comprising global perception, local focusing, iterative probing, and holistic adjudication.<n>Experiments show that ForenAgent exhibits emergent tool-use competence and reflective reasoning on challenging IFD tasks.
  arXiv  Detail & Related papers  (2025-12-18T08:38:44Z)
• SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs [28.59157823781425]
  SEAL is a novel two-stage semantic parsing framework grounded in self-evolving agentic learning.<n> SEAL achieves state-of-the-art performance, especially in multi-hop reasoning, comparison, and aggregation tasks.<n>The results validate notable gains in both structural accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-12-04T14:52:30Z)
• Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models [99.85131798240808]
  We introduce a novel generative framework called textitGuided Topology Diffusion (GTD)<n>Inspired by conditional discrete graph diffusion models, GTD formulates topology synthesis as an iterative construction process.<n>At each step, the generation is steered by a lightweight proxy model that predicts multi-objective rewards.<n>Experiments show that GTD can generate highly task-adaptive, sparse, and efficient communication topologies.
  arXiv  Detail & Related papers  (2025-10-09T05:28:28Z)
• ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction [84.90394416593624]
  Agentic task-solving with Large Language Models (LLMs) requires multi-turn, multi-step interactions.<n>Existing simulation-based data generation methods rely heavily on costly autoregressive interactions between multiple agents.<n>We propose a novel Non-Autoregressive Iterative Generation framework, called ToolACE-MT, for constructing high-quality multi-turn agentic dialogues.
  arXiv  Detail & Related papers  (2025-08-18T07:38:23Z)
• Generalising from Self-Produced Data: Model Training Beyond Human Constraints [0.0]
  This paper introduces a novel framework in which AI models autonomously generate and validate new knowledge.<n>Central to this approach is an unbounded, ungamable numeric reward that guides learning without requiring human benchmarks.
  arXiv  Detail & Related papers  (2025-04-07T03:48:02Z)
• Factored Agents: Decoupling In-Context Learning and Memorization for Robust Tool Use [4.437184840125514]
  We propose a novel factored agent architecture designed to overcome the limitations of traditional single-agent systems in agentic AI.<n>Our approach decomposes the agent into two specialized components: (1) a large language model that serves as a high level planner and in-context learner, and (2) a smaller language model which acts as a memorizer of tool format and output.<n> Empirical evaluations demonstrate that our factored architecture significantly improves planning accuracy and error resilience, while elucidating the inherent trade-off between in-context learning and static memorization.
  arXiv  Detail & Related papers  (2025-03-29T01:27:11Z)
• Large Language Model Agent: A Survey on Methodology, Applications and Challenges [88.3032929492409]
  Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence.<n>This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy.<n>Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time.
  arXiv  Detail & Related papers  (2025-03-27T12:50:17Z)
• ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning [53.817538122688944]
  We introduce Reinforced Meta-thinking Agents (ReMA) to elicit meta-thinking behaviors from Reasoning of Large Language Models (LLMs)<n>ReMA decouples the reasoning process into two hierarchical agents: a high-level meta-thinking agent responsible for generating strategic oversight and plans, and a low-level reasoning agent for detailed executions.<n> Empirical results from single-turn experiments demonstrate that ReMA outperforms single-agent RL baselines on complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-03-12T16:05:31Z)
• Retrieval Augmented Generation for Topic Modeling in Organizational Research: An Introduction with Empirical Demonstration [0.0]
  This paper introduces Agentic Retrieval-Augmented Generation (Agentic RAG) as a method for topic modeling with LLMs.<n>It integrates three key components: (1) retrieval, enabling automatized access to external data beyond an LLM's pre-trained knowledge; (2) generation, leveraging LLM capabilities for text synthesis; and (3) agent-driven learning, iteratively refining retrieval and query formulation processes.<n>Our findings demonstrate that the approach is more efficient, interpretable and at the same time achieves higher reliability and validity in comparison to the standard machine learning approach.
  arXiv  Detail & Related papers  (2025-02-28T11:25:11Z)
• From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models [56.9134620424985]
  Cross-modal reasoning (CMR) is increasingly recognized as a crucial capability in the progression toward more sophisticated artificial intelligence systems. The recent trend of deploying Large Language Models (LLMs) to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness. This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy.
  arXiv  Detail & Related papers  (2024-09-19T02:51:54Z)
• Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation [49.27250832754313]
  We present AgentCOT, a llm-based autonomous agent framework. At each step, AgentCOT selects an action and executes it to yield an intermediate result with supporting evidence. We introduce two new strategies to enhance the performance of AgentCOT.
  arXiv  Detail & Related papers  (2024-09-19T02:20:06Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.