Papers by Junlin Wu

5 papers
CARER: Contextualized Affect Representations for Emotion Recognition (D18-1)

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Challenge: Existing methods to model emotion-relevant content are based on rule-based and statistics-based approaches.
Approach: They propose a semi-supervised graph-based algorithm to produce rich structural descriptors . they use word embeddings to evaluate the algorithm on emotion recognition tasks .
Outcome: The proposed method outperforms state-of-the-art methods on emotion recognition tasks.
Collaborative Multi-Agent Scripts Generation for Enhancing Imperfect-Information Reasoning in Murder Mystery Games (2026.findings-acl)

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Challenge: Vision-language models have shown impressive capabilities in perceptual tasks . however, they degrade in complex multi-hop reasoning under multi-player game settings .
Approach: They propose a multi-agent framework for evaluating and synthesizing role-driven game scripts . they use curated and synthetic datasets to model uncertainty and deception .
Outcome: The proposed model significantly boosts the performance of VLMs in narrative reasoning and hidden fact extraction under uncertain, adversarial, and socially complex conditions.
From Personal to Collective: On the Role of Local and Global Knowledge in LLM Personalization (2026.findings-acl)

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Challenge: Large language models (LLMs) are the backbone of modern AI systems supporting a wide range of applications.
Approach: They propose a Local–Global knowledge framework that augments user-specific signals with a global knowledge encoding collective behavioral trends.
Outcome: The proposed framework outperforms existing methods on five personalization benchmarks.
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models (2024.acl-long)

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Challenge: Recent advances in Large Language Models (LLMs) have significantly enhanced the capabilities in natural language processing.
Approach: They propose a method to poison large language models by using annotators to rank a set of collected responses to generate longer tokens.
Outcome: The proposed method can generate longer tokens without harming the original safety alignment performance.
AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant (2025.findings-acl)

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Challenge: Existing agents lack generalization and specialization capabilities for open-ended tasks . specialized generalists are often underdeveloped in real-world environments .
Approach: They propose a platform to dynamically integrate heterogeneous agents for automating computer tasks . they propose specialized generalist agent MetaAgent with the AgentToken strategy .
Outcome: The proposed platform expands capabilities of existing agents in generalization and specialization . it can be used to automate open-ended tasks in real-world environments .

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