Papers by Weizhi Gao

2 papers
PersonaAgent: Bridging Memory and Action for Personalized LLM Agents (2026.findings-acl)

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Challenge: Existing Large Language Model (LLM) enabled agents lack flexibility to respond to users’ varying needs and preferences.
Approach: They propose a test-time user-preference alignment strategy that optimizes the persona prompt, ensuring real-time preference alignment through textual loss feedback between simulated and ground-truth responses.
Outcome: The proposed framework outperforms baseline methods in real-time and in real applications.
Breaking through Deterministic Barriers: Randomized Pruning Mask Generation and Selection (2023.findings-emnlp)

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Challenge: Existing pruning methods focus on a single pruning criterion and lack variety.
Approach: They propose a model pruning strategy that generates several pruning masks randomly and then chooses the optimal mask from the pool of mask candidates.
Outcome: The proposed pruning strategy achieves state-of-the-art performance across eight datasets from GLUE, particularly excelling at high levels of sparsity.

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