3 citations · 4 across the 14 of their papers we have counts for
17 papers
XferAgent: Cross-Agent Experience Transfer for Self-Improving LLM Agents
Weihao Li, Jun Bai, Shijie Chen +3
Large language model (LLM) agents can reuse experience from prior task executions to improve performance on subsequent tasks. Although existing methods enable cross-agent experienc…
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation
Ziyang Song, Lia Shen, Yixuan Li +6
Biomedical language models (LMs) provide strong representations for clinical tasks, but they often fail to capture the hierarchical structure among medical concepts. This limitatio…
JARQ: Joint Alternating Refinement for Quantization
Xinyu Wang, Sicheng Lyu, Xiao-Wen Chang
Group-wise post-training quantizers for large language models round weights onto a grid that is not refit to the resulting integer codes. We show that this leaves accuracy on the t…
Patients-like-me: A Variational LM--GNN Framework for Explainable Clinical Prediction
Xinyu Wang, Yixuan Li, Hanwei Wu +4
Language models (LMs) offer strong textual representations for electronic health records (EHRs), but they encode patient sequences in isolation and provide limited explainability.…
MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers
Linrui Ma, Chun Hei Lo, Xinyu Wang +12
The quadratic computational cost of traditional attention mechanisms poses a major bottleneck to the scalability and practical deployment of large language models (LLMs), particula…
TARQ: Tail-Aware Reconstruction Quantization for Rare-Word Robust Automatic Speech Recognition
Xinyu Wang, Ziyu Zhao, Ke Bai +4
Data-aware post-training quantization (PTQ) minimizes a per-token reconstruction loss on a small calibration corpus, implicitly weighting positions by their empirical frequency. Fo…
HoloQ-VLA: Uniform W4A4 Quantization of Vision-Language-Action Models
Xinyu Wang, Mingze Li, Sicheng Lyu +6
Vision-Language-Action (VLA) models unify perception, reasoning, and control in a single policy, but their multi-billion-parameter backbones and diffusion-based action heads make o…
TABQAWORLD: Optimizing Multimodal Reasoning for Multi-Turn Table Question Answering
Tung Sum Thomas Kwok, Xinyu Wang, Xiaofeng Lin +7
Multimodal reasoning has emerged as a powerful framework for enhancing reasoning capabilities of reasoning models. While multi-turn table reasoning methods have improved reasoning…
Co-Evolution of Policy and Internal Reward for Language Agents
Xinyu Wang, Hanwei Wu, Jingwei Song +8
Large language model (LLM) agents learn by interacting with environments, but long-horizon training remains fundamentally bottlenecked by sparse and delayed rewards. Existing metho…
OJBKQ: Objective-Joint Babai-Klein Quantization
Xinyu Wang, Ziyu Zhao, Peng Lu +2
Post-training quantization (PTQ) is widely used to compress large language models without retraining. However, many existing weight-only methods rely on heuristic objectives and gr…
InfMem: Learning System-2 Memory Control for Long-Context Agent
Xinyu Wang, Mingze Li, Peng Lu +6
Reasoning over ultra-long documents requires synthesizing sparse evidence scattered across distant segments under strict memory constraints. While streaming agents enable scalable…
Beyond Hard Writes and Rigid Preservation: Soft Recursive Least-Squares for Lifelong LLM Editing
Xinyu Wang, Sicheng Lyu, Yu Gu +4
Model editing updates a pre-trained LLM with new facts or rules without retraining while preserving unrelated behavior. In real deployment, edits arrive as long streams, creating a…
EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing
Sicheng Lyu, Yu Gu, Xinyu Wang +5
Large language models (LLMs) require continual updates to rectify outdated or erroneous knowledge. Model editing has emerged as a compelling paradigm for introducing targeted modif…
PoTPTQ: A Two-step Power-of-Two Post-training for LLMs
Xinyu Wang, Vahid Partovi Nia, Peng Lu +4
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language processing (NLP) tasks. However, their deployment is challenging due to the su…
Resona: Improving Context Copying in Linear Recurrence Models with Retrieval
Xinyu Wang, Linrui Ma, Jerry Huang +5
Recent shifts in the space of large language model (LLM) research have shown an increasing focus on novel architectures to compete with prototypical Transformer-based models that h…
Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning
Sitao Luan, Qincheng Lu, Chenqing Hua +3
Over the past decade, Graph Neural Networks (GNNs) have achieved great success on machine learning tasks with relational data. However, recent studies have found that heterophily c…
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Sitao Luan, Chenqing Hua, Qincheng Lu +11
Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of…