Papers by Zuchao Huang
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning (2026.acl-long)
Copied to clipboard
Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Jinhe Bi, Kristian Kersting, Jeff Z. Pan, Hinrich Schuetze, Volker Tresp, Yunpu Ma
| Challenge: | Large Language Models (LLMs) are stateless and limited by a finite context window, preventing them from maintaining knowledge across long conversations or evolving tasks. |
| Approach: | They propose a reinforcement learning framework that empowers LLMs to actively manage external memory through two specialized agents. |
| Outcome: | The proposed framework outperforms baselines and benchmarks across diverse question types, three benchmarks, and multiple model scales. |
TRACE: Traversal Retrieval-Augmented Chain of Evidence for Document Understanding (2026.acl-long)
Copied to clipboard
| Challenge: | Long-context Document Visual Question Answering (DocVQA) methods struggle with visual semantics or handling finite context windows. |
| Approach: | They propose a new approach to longcontext document visual question answering that transforms retrieval into adaptive evidence chain construction using a Bi-Layered Graph. |
| Outcome: | The proposed approach achieves an average accuracy improvement of 14.07% on M5BookVQA and exhibits robust generalization with a 13.38% gain across four established benchmarks. |
Moon IME: Neural-based Chinese Pinyin Aided Input Method with Customizable Association (P18-4)
Copied to clipboard
| Challenge: | a pinyin input method engine (IME) allows users to input Chinese into a computer by typing pinyan through the common keyboard. |
| Approach: | They present a pinyin IME that integrates neural machine translation and IR to offer amusive and customizable association ability. |
| Outcome: | The Moon IME integrates neural machine translation and IR to offer amusive association ability. |
Faster MoE LLM Inference for Extremely Large Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing inference optimizations for coarse-grained Mixture-of-Experts models implicitly assume a fixed activation budget, which is poorly understood. |
| Approach: | They propose a training-free policy that adapts token-level activation using router confidence and entropy while remaining within the model’s original budget. |
| Outcome: | The proposed skipping policy can provide substantial throughput gains, but optimal static schedules vary significantly across models and routing mechanisms. |
Teaching Your Models to Understand Code via Focal Preference Alignment (2025.emnlp-main)
Copied to clipboard
Jie Wu, Haoling Li, Xin Zhang, Xiao Liu, Yangyu Huang, Jianwen Luo, Yizhen Zhang, Zuchao Li, Ruihang Chu, Yujiu Yang, Scarlett Li
| Challenge: | Existing methods for supervised fine-tuning focus on unit test feedback to construct preference pairs. |
| Approach: | They propose a preference alignment framework that mimics human iterative debugging to refine Code LLMs. |
| Outcome: | Experiments show that Preference Learning improves on BigCodeBench and BigCodeBind tasks. |