activity
20232026
most citedParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks

2 citations · 2 across the 6 of their papers we have counts for

collaborators

9 papers

cs.CL2026

CMT-RAG: Complementary Memory Traces for Multi-turn Multi-hop RAG

Lang Zhou, Yingjian Chen, Shuxuan Li +2

Multi-turn information-seeking conversations require both multi-hop reasoning and long-range dependency tracking across turns. However, existing RAG systems typically represent con…

cs.LG2026

From Approximation to Emergence: A Theory of Deep Learning

Zhilin Zhao

Deep learning has outgrown any single mathematical explanation. From Approximation to Emergence develops a unified, proof-oriented account of modern deep learning theory, tracing a…

cs.LG2026

Bridging Domain Expertise and Generalization for Performance Estimation

Shuxuan Li, Zhilin Zhao, Quyu Kong +1

Performance estimation under distribution shift aims to predict how a model behaves on an unlabeled test set whose distribution differs from the training data, a scenario that requ…

cs.CL2026

UT-ACA: Uncertainty-Triggered Adaptive Context Allocation for Long-Context Inference

Lang Zhou, Shuxuan Li, Zhuohao Li +3

Long-context inference remains challenging for large language models due to attention dilution and out-of-distribution degradation. Context selection mitigates this limitation by a…

cs.LG2024

Federated Neural Nonparametric Point Processes

Hui Chen, Xuhui Fan, Hengyu Liu +5

Temporal point processes (TPPs) are effective for modeling event occurrences over time, but they struggle with sparse and uncertain events in federated systems, where privacy is a…

cs.LG20242 cited

ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks

Zhangkai Wu, Xuhui Fan, Jin Li +3

The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling parameter spaces, offering a unified strategy for handling continuous, discretized, and discret…