CL-bench: A Benchmark for Context Learning
Source
- Person key:
ysymyth - Source kind:
paper - Canonical URL: https://arxiv.org/abs/2602.03587
- License:
NOASSERTION - Public handling:
public-metadata-summary-hash-link-only - Semantic hash:
5f5bd235f5ff4b401165a7013f4a70a63884b80bf4debcd0de3614dc4acb3403 - First seen: 2026-05-16
- Last changed: 2026-05-16
- Identity guard: Do not confuse with yao-shunyu-alfred, the physics-to-AI researcher at alfredyao.github.io.
Classification
- Category: Evaluation / benchmark design
- Topic hub: shunyu-yao-public-corpora
- Project taxonomy: shunyu-yao-project-taxonomy
- Paper map: shunyu-yao-paper-map
Summary
Current language models (LMs) excel at reasoning over prompts using pre-trained knowledge. However, real-world tasks are far more complex and context-dependent: models must learn from task-specific context and leverage new knowledge beyond what is learned during pre-training to reason and resolve tasks. We term this capability context learning, a crucial ability that humans naturally possess but has been largely overlooked. To this end, we introduce CL-bench, a real-world benchmark consisting of 500 complex context…
What This Teaches
How to turn agent behavior into measurable tasks, success criteria, and repeatable benchmark environments.
Related
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