CL-bench Life: Can Language Models Learn from Real-Life Context?
Source
- Person key:
ysymyth - Source kind:
paper - Canonical URL: https://arxiv.org/abs/2604.27043
- License:
NOASSERTION - Public handling:
public-metadata-summary-hash-link-only - Semantic hash:
11e45f71017bef9479ee94f219c6f748e58425523a04ad2c37dc925a2a14d6cd - 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
Today’s AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move beyond professional settings into everyday life, the nature of the contexts they must handle also shifts. Real-life contexts are often messy, fragmented, and deeply tied to personal and social experience, such as multi-party conversations, personal archives, and behavioral traces. Yet it remains unclear whether current frontier language mod…
What This Teaches
How to turn agent behavior into measurable tasks, success criteria, and repeatable benchmark environments.
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