Papers by Xingzhi Guo
SafeSearch: Do Not Trade Safety for Utility in LLM Search Agents (2026.findings-eacl)
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| Challenge: | Large language model (LLM) based search agents are more likely to produce harmful outputs than base models. |
| Approach: | They propose a query-level shaping term that rewards safe queries and penalizes unsafe ones. |
| Outcome: | The proposed approach reduces harmfulness by over 70% across three red-teaming datasets while producing safe, helpful responses. |
MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning (2025.acl-long)
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| Challenge: | Existing studies focus on prompting and developing workflows with frozen LLMs. |
| Approach: | They propose a multi-agentic framework for collaborative LLMs with reinforcement learning that leverages multi-gendered frameworks to enhance collaboration. |
| Outcome: | The proposed model improves collaboration performance across multiple datasets with generalization to unseen domains compared to existing models. |