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The Chinese University of Hong Kong

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#machine learning Preprint Oct 2026

A Refresh Interval Does Not Fully Specify a Refresh Schedule: Learning-Rate-Dependent Placement in Kronecker-Preconditioned Language Model Training

Kronecker-preconditioned optimizers recompute a cached curvature eigendecomposition only periodically, and implementations and adaptive methods control this through the refresh frequency. We show that frequency leaves part of a schedule free: at a fixed number of refreshes, when they occur changes held-out loss at the...

Xin-Yu Wang, Zhi-Wei Zhai, Yu Gu et al. · 0 citations

#natural language process... Preprint Sep 2026

Domain-Adaptive Pretraining Enhances Water Treatment Semantic Representation for Large-Scale Structured Literature Mining

This study provides a compact and scalable semantic foundation for large-scale information processing and evidence mapping in water treatment research by developing WaterBERT, a domain-adapted encoder model designed for semantic representation and structured information extraction from water treatment texts.

Mu-Di Zhai, Rui-Hong Qiu, Qing-Yun Zeng et al. · 0 citations

Jul 2026

FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering

FinSAgent is proposed, an evidence-grounded multi-agent framework that reframes SEC filing QA as corpus-aligned retrieval planning and corrects both ends with a single principle: inject corpus-side conditioning wherever model priors would otherwise dominate.

Jijun Chi, Zhenghan Tai, Han-Wei Wu et al. · arXiv.org · 0 citations

#artificial intelligence Preprint Aug 2026

DoGMA: A Central-Dogma-Guided Foundation Model for Multi-Omics Alignment and Multi-Task Learning in Oncology

DoGMA demonstrates that domain-specific inductive biases can improve the robustness and transferability of multi-omics foundation models, offering new insights into the design of attention mechanisms for multi-omics representation learning.

Junfei Ling, Bangzheng Pu, Bing-Sen Xue et al. · 0 citations

Preprint Aug 2026

DocNavRAG: Document-Structured Graph RAG with Stateful Evidence Construction for Complex Document Question Answering

DocNavRAG is introduced, which organizes document hierarchies and cross-region relations into a navigable graph, exposes graph operations for locating, navigating, expanding, and fetching, and maintains an evolving evidence state to guide retrieval until sufficient evidence is collected.

Dong Xie, Yao Tian, Hao Zhang et al. · 0 citations