Chart reasoning agents are increasingly used to extract actionable insights in critical domains, achieving state-of-the-art performance on multiple benchmarks. Yet, high benchmark accuracy alone is insufficient for deployment, where stakeholders must be able to audit and verify how a model reaches its answer. Existing...
Sanchit Sinha, Oana Frunza, Kashif Rasul et al.· 0 citations
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
This work proposes to view reasoning paths as phase-structured trajectories within fixed questions as PAIR, short for Phase-Aligned Intra-question Reasoning, and improves within-question trajectory ranking and Best-of-N trajectory selection across models and benchmarks.
Zheng-Hao He, Guang-Zhi Xiong, Sanchit Sinha et al.· 0 citations
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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