Hi, my name is Wenhao Wu (吴文浩). I am a third-year master's student at Nanjing University, supervised by Prof. Zhi Wang (王志). My research interests focus on Large Language Model Agents, Retrieval-Augmented Generation, Meta Reinforcement Learning, and Vision-Language-Action.
MemFuse: Multi-Source Memory Fusion from Fragmented Observations
Chao Li, Yuanfa Li, Wenhao Wu, Xule Liu, Zhi Wang, Kun Shao
Introduces MemFuseBench for evaluating temporal reasoning and cross-source evidence fusion, and a dual-layer causal fusion graph that preserves source provenance while organizing fragmented observations into retrievable memories.
From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG
Wenhao Wu, Zhentao Tang, Yafu Li, Shixiong Kai, Mingxuan Yuan, Zhenhong Sun, Chunlin Chen, Zhi Wang
Proposes a conflict-guided multi-round retrieval framework for medical reasoning. The method uses conflicts among sampled responses as retrieval signals, generates precise queries, and reranks historical reasoning trajectories with sequence-level entropy or a lightweight BERT verifier.
Look Before You Leap: Distilling Tree Search into Action Evaluation for Frozen VLA Models
Xinyi Xie, Zican Hu, Zhanyu Liu, Yicheng Dong, Wenhao Wu, Haoran Li, Chunlin Chen, Zhi Wang*, Pichao Wang
Proposes SVA (Search, Value, and Act), which keeps the VLA backbone frozen and equips it with long-term consequence awareness. Monte-Carlo tree search explores the policy's output distribution in simulation and is distilled into a lightweight Q-value evaluator that selects the best of N candidate actions at deployment without simulator access.
Mixture-of-Experts Meets In-Context Reinforcement Learning
Wenhao Wu, Fuhong Liu, Haoru Li, Zican Hu, Daoyi Dong, Chunlin Chen, Zhi Wang
Introduces a dual-MoE architecture for in-context RL. Token-wise MoE routes state, action, and reward information to specialized experts, while Task-wise MoE uses contrastive task representations to reduce gradient conflict in multi-task learning.
Text-to-Decision Agent: Offline Meta-Reinforcement Learning from Natural Language Supervision
Shilin Zhang*, Zican Hu*, Wenhao Wu*, Xinyi Xie, Jianxiang Tang, Chunlin Chen, Daoyi Dong, Yu Cheng, Zhenhong Sun, Zhi Wang
Aligns trajectory-derived decision embeddings with natural-language supervision. The framework trains a trajectory world model, performs contrastive alignment with LoRA-tuned language models, and conditions scalable policies on aligned text embeddings for zero-shot generalization.
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement
Zhi Wang, Li Zhang, Wenhao Wu, Yuanheng Zhu, Dongbin Zhao, Chunlin Chen
Formulates offline meta-RL as conditional sequence modeling with world-model disentanglement. A context-aware world model learns task representations, while self-guided and complementary prompts support generalization to new tasks.
CAR-bench Challenge — Open Track
IJCAI-ECAI 2026 Competition Track · Darwin Agent team
Wenhao Wu*, Menghao Zhang*, Xin Wang*, Zhi Wang, Kun Shao, Jian Luan
Developed TRACE, a self-evolving Skill Bank for consistent and limit-aware LLM agents. Our team won both the Rank Award and Innovation Award, ranking 1st in the Open Track on the official hidden-set evaluation.
