Author

Shaofeng Liang

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#artificial intelligence Apr 2026

ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding

ABLE (Attribution-Based Large-model Embedding), a framework that leverages the interpretability space to construct model representations by aggregating gradient-based feature attributions via a tokenizer-agnostic word-level alignment, captures model-specific input-sensitivity patterns rather than only surface-level outputs.

Zirui Wang, Yusen Hou, Shaofeng Liang et al. · arXiv.org · 0 citations

Preprint Aug 2026

CraftAlign: Feature-Grounded Evaluation and Revision Guidance for AI Stories

CraftAlign is introduced, a framework that aligns AI stories with the craft of human storytelling by both assessing Human/AI writing patterns and providing revision guidance by both assessing Human/AI writing patterns and providing revision guidance.

Yang Yang, Boyun Xu, Shaofeng Liang et al. · 0 citations