most citedImproving In-Context Learning with Reasoning Distillation

1 citations · 1 across the 24 of their papers we have counts for

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cs.AI2026

Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence

Mengru Wang, Junfeng Fang, Shuofei Qiao +17

AI models are increasingly used in scientific discovery and human decision-making. Yet how AI models work and what risks they pose remain poorly understood. As AI development becom…

cs.AI2026

Toward Latent Language Model Skills Steering and Optimization: An Empirical Study

Xunyi Jiang, Junda Wu, Yuxin Xiong +6

Skills, as a useful abstraction for the procedural capabilities of large language models (LLMs), capture how models perform structured, multi-step reasoning and program execution.…

cs.AI2026

How Hard Does It Think? Analyzing Step-Aware Reasoning Energy in LLM Chain-of-Thought Trajectories

Hui Wei, Junda Wu, Sheldon Yu +8

Understanding how computational effort is allocated across individual chain-of-thought (CoT) reasoning steps remains an open challenge: existing interpretability methods rely on ou…

cs.AI2026

OLIVIA: Online Learning via Inference-time Action Adaptation for Decision Making in LLM ReAct Agents

Sheldon Yu, Junda Wu, Xintong Li +6

Large language model agents interleave reasoning, action selection, and observation to solve sequential decision-making tasks. In deployed settings where agents repeatedly handle r…

cs.AI2026

ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spaces

Xin Xu, Tong Yu, Xiang Chen +3

Recent work explores latent reasoning to improve reasoning efficiency by replacing explicit reasoning trajectories with continuous representations in a latent space, yet its effect…