Papers by Fangyuan Zhang
MARS2: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation (2026.acl-long)
Copied to clipboard
Pengfei Li, Shijie Wang, Fangyuan Li, Yikun Fu, Kaifeng Liu, Kaiyan Zhang, Dazhi Zhang, Yuqiang Li, Biqing Qi, Bowen Zhou
| Challenge: | Existing approaches to reinforcement learning are decoupled from structured search due to limited trajectory diversity. |
| Approach: | They propose a unified RL framework that integrates multiple agents within a shared tree-structured search environment. |
| Outcome: | Experiments show that MARS2 improves performance across diverse model combinations and training settings. |
Evaluating the Validity of Word-level Adversarial Attacks with Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing adversarial examples can generate invalid adversarials due to significant changes in semantic meanings compared to their originals. |
| Approach: | They propose to use a large language model to evaluate adversarial examples by semantic constraints. |
| Outcome: | The proposed method can generate valid adversarial examples even when they are not equipped with semantic constraints. |
LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning (2026.findings-acl)
Copied to clipboard
Zhengjun Huang, Zhoujin Tian, Qintian Guo, Fangyuan Zhang, Yingli Zhou, Di Jiang, Zeying Xie, Xiaofang Zhou
| Challenge: | Large Language Models are constrained by limited context windows and lack of persistent memory . recent efforts address these limitations via external memory architectures . |
| Approach: | They propose an end-to-end agentic memory framework for real-time updating and retrieval that integrates hierarchical and temporal indexing layers. |
| Outcome: | The proposed framework outperforms established benchmarks in temporal reasoning, multi-session consistency, and retrieval efficiency. |
Breaking the Static Graph: Context-Aware Traversal for Graph-Based RAG (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in RAG focus on capturing multi-hop dependencies, but static Graphs fail to retrieve complete evidence chain. |
| Approach: | They propose a structure-aware approach to capture multi-hop dependencies using Knowledge Graphs and Personalized PageRank to capture semantic drift. |
| Outcome: | Experiments show that CatRAG outperforms state-of-the-art approaches . the proposed approach achieves substantial improvements in reasoning completeness . |