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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
#artificial intelligence Preprint Sep 2026

Sequential Functional Structured Tucker Compression for Large Language Model Attentions

Post-training compression of LLM attention is often formulated as independent matrix approximation, ignoring both the shared structure among attention projections and the representation shift introduced by earlier compression. We propose FTC, a sequential structured compression framework that adapts the approximation t...

Jiang-Feng Chen, Xin-Yu Wang, Tian-Shuo Yan et al. · 0 citations
#artificial intelligence Preprint Sep 2026

FINSKILLOPS: A Self-Evolving Multi-Agent System for SEC Filing QA

Financial QA systems are typically improved before deployment through better retrieval, prompting, or agent coordination, leaving their reliability behavior fixed thereafter. In practice, new SEC-filing questions repeatedly expose heterogeneous errors in period, entity, evidence use, and calculation. Existing self-impr...

Yanzhang Ma, Zhenghan Tai, Han-Wei Wu 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. · 0 citations

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