Context-CoT: Enhancing Context Learning via High-Quality Reasoning Synthesis
The paper titled 'Context-CoT: Enhancing Context Learning via High-Quality Reasoning Synthesis' addresses the limitations of large language models (LLMs) in context learning. It highlights that these models perform poorly in dynamically extracting and applying new knowledge from complex contexts, achieving only 17.2% success in context-dependent tasks. The authors propose enhancements to improve this capability gap in LLMs.
- ▪The paper was submitted on May 25, 2026, by Hongbo Jin and eight co-authors.
- ▪LLMs struggle with context learning, which involves internalizing and applying new knowledge from specific contexts.
- ▪Recent evaluations show that frontier models only solve 17.2% of context-dependent tasks on average.
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Computer Science > Artificial Intelligence arXiv:2605.25354 (cs) [Submitted on 25 May 2026] Title:Context-CoT: Enhancing Context Learning via High-Quality Reasoning Synthesis Authors:Hongbo Jin, Mingnan Zhu, Jingqi Tian, Xu Jiang, Zhongjing Du, Haoran Tang, Siyi Xie, Qiaoman Zhang, Jiayu Ding View a PDF of the paper titled Context-CoT: Enhancing Context Learning via High-Quality Reasoning Synthesis, by Hongbo Jin and 8 other authors View PDF HTML (experimental) Abstract:While LLMs excel at reasoning over prompts using static pretrained knowledge, they struggle significantly with context learning-the ability to dynamically extract, internalize, and apply new knowledge from complex, task-specific contexts.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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