16 citations · 17 across the 16 of their papers we have counts for
19 papers
HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training
Xinrui Chen, Mengyang Li, Ou Wu +1
Group-relative policy optimization (GRPO) makes learner-side activations a major memory-computation bottleneck: gradient checkpointing reduces activation memory through recomputati…
MemAgent: Learning to Manage Heterogeneous Memory Providers for LLM Agents
Yongxian Wei, Yilin Zhao, Runxi Cheng +5
Current agents remain largely stateless across tasks, limiting their ability to continually improve from prior interactions and making memory essential for long-horizon agentic beh…
CompKV: Compensation-Aware KV Selection for Long-Context LLM Inference
Zhen Huang, Ruizhe Yao, Danyi Liu +8
Despite their strong performance, large language models (LLMs) are bottlenecked by KV cache memory traffic during long-context inference. Sparse attention is widely used to acceler…
Token Utility Is Selection-Conditioned: Coupled Selection of Prompt Context and Response Supervision for Instruction Tuning
Can Wu, Xinrui Chen, Ou Wu +1
Token selection for instruction tuning is coupled: supervision utility depends on retained context, and vice versa. Existing methods largely optimize prompt compression or response…
Two to Tango: Coupled Task-Reference Selection for Safe LLM Fine-tuning
Xinrui Chen, Jianhao Zhang, Ou Wu +1
Fine-tuning safety aligned large language models (LLMs) on downstream data improves adaptation but may erode learned safety behavior. Existing methods use fixed safety examples, gl…
Is Class Signal Clustered or Routed in Task-Induced Implicit Neural Representation Weight Spaces?
Xinyi Guo, Mingyi He, Haobin Ding +7
Implicit neural representations (INRs) encode images as neural-network weights, making image classification a problem of weight-space classifiability. A natural geometric hypothesi…
One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
Xinrui Chen, Liu Yang, Ou Wu
In Large Language Model (LLM) fine-tuning, parameter and data selection are common strategies for reducing fine-tuning cost, yet they are typically driven by separate scoring mecha…
Co-Generative De Novo Functional Protein Design
Xinrui Chen, Yizhen Luo, Siqi Fan +1
De novo functional protein design aims to generate protein sequences that realize specified biochemical functions without relying on evolutionary templates, enabling broad applicat…
GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights
Qiming He, Jing Li, Tian Guan +26
Glomerular pathology is central to the diagnosis and prognosis of renal diseases, yet the heterogeneity of glomerular morphology and fine-grained lesion patterns remain challenging…
HookMIL: Revisiting Context Modeling in Multiple Instance Learning for Computational Pathology
Xitong Ling, Minxi Ouyang, Xiaoxiao Li +7
Multiple Instance Learning (MIL) has enabled weakly supervised analysis of whole-slide images (WSIs) in computational pathology. However, traditional MIL approaches often lose cruc…
Distinguishing Imitation Error from Intrinsic Motion Learning Difficulty
Zhaorui Meng, Lu Yin, Xinrui Chen +4
Physics-based motion imitation is central to humanoid control, yet current evaluation metrics (e.g., MPJPE) only quantify imitation outcomes, not their underlying causes. This conf…
OmniAlpha: Aligning Transparency-Aware Generation via Multi-Task Unified Reinforcement Learning
Hao Yu, Jinglin Wang, Jiabo Zhan +7
Transparency-aware generation requires modeling not only RGB appearance but also alpha-based opacity and cross-layer composition, which are essential for tasks such as image mattin…
Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis
Yongxian Wei, Yilin Zhao, Zixuan Hu +7
Data synthesis for training large reasoning models offers a scalable alternative to limited, human-curated datasets, enabling the creation of high-quality data. However, existing a…
From Generic to Specialized: A Subspecialty Diagnostic System Powered by Self-Supervised Learning for Cervical Histopathology
Yizhi Wang, Li Chen, Qiang Huang +24
Cervical cancer remains a major malignancy, necessitating extensive and complex histopathological assessments and comprehensive support tools. Although deep learning shows promise,…
Prune&Comp: Free Lunch for Layer-Pruned LLMs via Iterative Pruning with Magnitude Compensation
Xinrui Chen, Hongxing Zhang, Fanyi Zeng +5
Layer pruning has emerged as a promising technique for compressing large language models (LLMs) while achieving acceleration proportional to the pruning ratio. In this work, we ide…
A Simple Linear Patch Revives Layer-Pruned Large Language Models
Xinrui Chen, Haoli Bai, Tao Yuan +7
Layer pruning has emerged as a widely used technique for compressing large language models (LLMs). However, existing layer pruning approaches often incur substantial performance de…
Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis
Xitong Ling, Minxi Ouyang, Yizhi Wang +8
Histopathology analysis is the gold standard for medical diagnosis. Accurate classification of whole slide images (WSIs) and region-of-interests (ROIs) localization can assist path…
Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models
Xuanyu Lei, Zonghan Yang, Xinrui Chen +2
State-of-the-art Large Multi-Modal Models (LMMs) have demonstrated exceptional capabilities in vision-language tasks. Despite their advanced functionalities, the performances of LM…
Towards Unified Alignment Between Agents, Humans, and Environment
Zonghan Yang, An Liu, Zijun Liu +11
The rapid progress of foundation models has led to the prosperity of autonomous agents, which leverage the universal capabilities of foundation models to conduct reasoning, decisio…