Papers by Junqi Gao
Simple Role Assignment is Extraordinarily Effective for Safety Alignment (2026.findings-acl)
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Zhou Ziheng, Jiakun Ding, Zhaowei Zhang, Ruosen Gao, Ying Nian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong, Junqi Wang
| Challenge: | a new study proposes a role-conditioned pipeline for value alignment . principles alone are incomplete, and they provide little guidance on when and how a value applies in context. |
| Approach: | They propose a role-conditioned pipeline with role-based critics and a model-free approach that is based on role conditioning. |
| Outcome: | The proposed approach outperforms principle-based, Chain-of-Thought and other benchmarks. |
ReviewRL: Towards Automated Scientific Review with RL (2025.emnlp-main)
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Sihang Zeng, Kai Tian, Kaiyan Zhang, Yuru Wang, Junqi Gao, Runze Liu, Sa Yang, Jingxuan Li, Xinwei Long, Jiaheng Ma, Biqing Qi, Bowen Zhou
| Challenge: | Existing automated review systems struggle with factual accuracy, rating consistency, and analytical depth. |
| Approach: | They propose a framework for generating comprehensive and factually grounded scientific paper reviews using supervised fine-tuning and reinforcement learning. |
| Outcome: | The proposed framework outperforms existing methods on ICLR 2025 papers. |
Dual-Phase Accelerated Prompt Optimization (2024.findings-emnlp)
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| Challenge: | Existing methods for prompt optimization make light of the importance of high-quality initialization and the identification of effective directions. |
| Approach: | They propose a method which uses a meta-instruction to generate high-quality initial prompts and iteratively optimize them at the sentence level. |
| Outcome: | The proposed method achieves consistent accuracy gain over baselines with less than five optimization steps. |
WIST: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement (2026.acl-long)
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| Challenge: | Existing methods for self-improvement of large language models with verifiable rewards (RLVR) can drift over iterations, while corpus-grounded approaches rely on curated data environments. |
| Approach: | They propose a Web-grounded Iterative Self-play Tree framework for domain-targeted reasoning improvement that learns directly from the open-web without requiring any pre-arranged domain corpus. |
| Outcome: | The proposed framework outperforms both purely endogenous self-evolution and corpus-grounded self-play baselines and is domain-steerable. |
Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning (2025.acl-long)
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| Challenge: | Existing methods for enhancing LLM reliability suffer from inefficient information aggregation and rigid reasoning schemes. |
| Approach: | They propose a method that explicitly models external knowledge integration capabilities by explicitly modeling knowledge relationships. |
| Outcome: | The proposed method outperforms existing methods in multiple graph reasoning tasks. |
SMR: State Memory Replay for Long Sequence Modeling (2024.findings-acl)
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| Challenge: | Existing state space models (SSMs) address non-uniform sampling, but their recursive structures impede efficient SSM computation via convolution. |
| Approach: | They propose a plug-and-play mechanism to solve the Non-Stable State problem by adjusting input sequences with early memories. |
| Outcome: | The proposed method overcomes the non-uniform sample processing problem . it can achieve Sampling Step Adaptation (SSA) by adjusting input sequences with early memories. |