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Jianxin Li

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#machine learning Preprint Sep 2026

Hierarchical Response Preservation for Continual Adaptation of Zero-Shot Graph-Text Models

Hierarchical Response Preservation (HiRP) is proposed, a hierarchical response that keeps each historical-class probability and sums new-class probabilities, preserving historical distinctions and aggregate competition while allowing distinctions within the new class group to adapt.

Haopeng Zhang, Yu-Han Wang, Yu-Bing Su et al. · 0 citations
Preprint Aug 2026

Counterfactual Quotient Models: Learning What Actions Change, Not What the World Does

This work introduces the Counterfactual Quotient Model, which treats action-conditioned futures as equivalent when they differ only by a component shared across actions, and establishes the decision sufficiency, identifiability, common-mode invariance, approximation behavior, and regret properties of the resulting repr...

Jun-Lin Chen, Rui-Jie Wang, Jian-Xin Li · 1 citation
Jul 2026

Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

GTAlign is proposed, a surprisingly simple yet effective Graph-to-Table Alignment framework for text-free Graph Foundation Model, and a community-guided continual pre-training, where pseudo-labels derived from graph community are used to construct few-shot prediction episodes.

Chunyu Hu, Tianyin Liao, Ge Lan et al. · 0 citations

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