Challenge: Existing studies often overlook the act of communicating consistent with professional identities of LLM-based AI chatbots.
Approach: They propose a framework for designing professional service agents for medical question-and-answer services that aligns professional identities with a theory-guided task planning process.
Outcome: The proposed approach outperforms baseline methods on various LLMs across key metrics such as fluency, naturalness, empathy, patient-centricity, and ROUGE-L scores.

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Challenge: Existing large language models (LLMs) are brittle to input changes and can produce inconsistent results for the same inputs.
Approach: They propose to use large language models to reason about complex goals and orchestrate a set of pluggable tools or APIs to accomplish a goal.
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Characteristic AI Agents via Large Language Models (2024.lrec-main)

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Challenge: Commercial products have been devoted to creating character-driven chatbots using large language models, but academic research in this area remains relatively scarce.
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A Survey of LLM-based Agents in Medicine: How far are we from Baymax? (2025.findings-acl)

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Challenge: Large Language Models (LLMs) are transforming healthcare through their ability to understand and assist with medical tasks.
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LLM-Based Multi-Agent Systems for Clinical Workflows: A Survey of AI Hospitals (2026.acl-long)

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Challenge: Large Language Models (LLMs) are moving from isolated text generation toward agentic work inside clinical workflows.
Approach: They propose a workflow-level taxonomy for LLM-based multi-agent systems for clinical and healthcare workflows . they propose integration readiness levels, task-level instrumentation requirements and recurring workflow failure modes as a practical framework for comparing, evaluating and deploying clinical LLM agents and AI hospitals.
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ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models (2023.emnlp-demo)

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Challenge: Large language models (LLMs) have demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
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LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)

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Challenge: Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents.
Approach: They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety.
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Hello Again! LLM-powered Personalized Agent for Long-term Dialogue (2025.naacl-long)

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Challenge: Existing dialogue systems focus on brief single-session interactions, neglecting real-world needs for long-term companionship and personalized interactions.
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SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue (2026.findings-acl)

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Challenge: Large Language Models have demonstrated remarkable capabilities in open-domain dialogues, but their performance in service dialogues remains suboptimal.
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Evaluating Conversational Agents with Persona-driven User Simulations based on Large Language Models: A Sales Bot Case Study (2025.emnlp-industry)

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Challenge: Recent advances in LLMs enable sophisticated user simulations that can replace traditional rule-based evaluations.
Approach: They propose a persona-driven approach to conversational agent evaluation using Large Language Models (LLMs) they introduce a dataset of customer personas, which are then used to configure a single LLM-based user simulator.
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A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)

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Challenge: Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation.
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Outcome: The proposed models address fundamental challenges in traditional recommender systems . they include limited comprehension of complex user intents, insufficient interaction capabilities .

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