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#artificial intelligence Preprint Oct 2026

Capturing In-Context Learning Dynamics with Task Operators

In-context learning (ICL) enables language models to perform new tasks from demonstrations without weight updates. However, every ICL inference requires processing the full set of examples, resulting in inefficient deployments, and how ICL works mechanistically is not fully understood. Prior work compresses ICL into fi...

Guang-Zhi Xiong, Zheng-Hao He, Bo-Han Liu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Reliable Parallel Decoding in Masked Diffusion Language Models

Masked diffusion language models (MDLMs) can generate text efficiently by predicting multiple masked tokens in parallel, but predictions from the same forward pass are not necessarily reliable when committed together. We study when parallel commitment is reliable. Our diagnostics show that confidence alone does not det...

Zheng-Hao He, Bo-Han Liu, Guang-Zhi Xiong et al. · 0 citations
#artificial intelligence Preprint Jan 2026

Triggering Chain-of-Thought via Latent Feature Interventions in Large Language Models

It is suggested that CoT prompting activates specific latent features to trigger reasoning, and that targeted intervention on these features offers an alternative pathway to elicit efficient reasoning behavior without explicit CoT prompting.

Zhenghao He, Guangzhi Xiong, Bohan Liu et al. · 6 citations · ⚡1

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