Papers by Changyuan Yang

2 papers
ThinkPersona: Thinking with Persona Graphs for Faithful Individualized Role-Playing (2026.acl-long)

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Challenge: Large Language Models are increasingly utilized as role-playing agents to simulate personas in interactive settings.
Approach: They propose a role-playing agent trained to explicitly ground responses in individual identity.
Outcome: The proposed agent can generate persona-consistent responses in long-context dialogues while maintaining general instruction-following capabilities.
IEvoAgent: Evolving Conversational Agent based on User Implicit Feedback (2026.acl-long)

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Challenge: Existing approaches to optimize conversational agents often rely on explicit preference pairs and expert evaluations.
Approach: They propose a conversational agent framework that leverages the structured dependency between agent responses and user reactions to extract implicit feedback.
Outcome: The proposed framework improves on MT-Bench-101, WildBench, and FB-Bech, and shows that mining implicit feedback supports better multi-turn alignment under evolving user preferences.

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