CL-bench Life: Can Language Models Learn from Real-Life Context?

• URL: http://arxiv.org/abs/2604.27043v1
• Date: Wed, 29 Apr 2026 17:44:32 GMT
• Title: CL-bench Life: Can Language Models Learn from Real-Life Context?
• Abstract summary: CL-bench Life is a benchmark comprising 405 context-task pairs and 5,348 verifications.<n>We evaluate ten frontier LMs and find that real-life context learning remains highly challenging.<n> CL-bench Life provides a crucial testbed for advancing real-life context learning.
• Score: 123.88885809530706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 models can reliably learn from such contexts and solve tasks grounded in them. To this end, we introduce CL-bench Life, a fully human-curated benchmark comprising 405 context-task pairs and 5,348 verification rubrics, covering common real-life scenarios. Solving tasks in CL-bench Life requires models to reason over complex, messy real-life contexts, calling for strong real-life context learning abilities that go far beyond those evaluated in existing benchmarks. We evaluate ten frontier LMs and find that real-life context learning remains highly challenging: even the best-performing model achieves only 19.3% task solving rate, while the average performance across models is only 13.8%. Models still struggle to reason over contexts such as messy group chat histories and fragmented behavioral records from everyday life. CL-bench Life provides a crucial testbed for advancing real-life context learning, and progress on it can enable more intelligent and reliable AI assistants in everyday life.

Related papers

• LOCA-bench: Benchmarking Language Agents Under Controllable and Extreme Context Growth [32.1520194112537]
  Large language models (LLMs) are increasingly capable of carrying out long-running, real-world tasks.<n>As the amount of context grows, their reliability often deteriorates, a phenomenon known as "context rot"
  arXiv  Detail & Related papers  (2026-02-08T13:20:39Z)
• CL-bench: A Benchmark for Context Learning [152.2879060355882]
  We term this capability context learning, a crucial ability that humans naturally possess but has been largely overlooked.<n>We introduce CL-bench, a real-world benchmark consisting of 500 contexts, 1,899 tasks, and 31,607 verifications.<n> CL-bench represents a step towards building LMs with this fundamental capability, making them more intelligent and advancing their deployment in real-world scenarios.
  arXiv  Detail & Related papers  (2026-02-03T14:37:47Z)
• A Controllable Examination for Long-Context Language Models [62.845852724511964]
  This study introduces $textbfLongBioBench, a benchmark for evaluating long-context language models.<n>We show that most models still exhibit deficiencies in semantic understanding and elementary reasoning over retrieved results.<n>Our further analysis indicates some design choices employed by existing synthetic benchmarks, such as contextual non-coherence.
  arXiv  Detail & Related papers  (2025-06-03T14:23:06Z)
• Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystack [33.178008350124315]
  We introduce Lifelong ICL, a problem setting that challenges long-context language models (LMs) to learn a sequence of language tasks through in-context learning (ICL)<n>We introduce Task Haystack, an evaluation suite dedicated to assessing and diagnosing how long-context LMs utilize contexts in Lifelong ICL.
  arXiv  Detail & Related papers  (2024-07-23T17:57:41Z)
• Scalable Language Model with Generalized Continual Learning [58.700439919096155]
  The Joint Adaptive Re-ization (JARe) is integrated with Dynamic Task-related Knowledge Retrieval (DTKR) to enable adaptive adjustment of language models based on specific downstream tasks. Our method demonstrates state-of-the-art performance on diverse backbones and benchmarks, achieving effective continual learning in both full-set and few-shot scenarios with minimal forgetting.
  arXiv  Detail & Related papers  (2024-04-11T04:22:15Z)
• Pre-Training to Learn in Context [138.0745138788142]
  The ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. We propose PICL (Pre-training for In-Context Learning), a framework to enhance the language models' in-context learning ability. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters.
  arXiv  Detail & Related papers  (2023-05-16T03:38:06Z)
• ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes [72.83187997344406]
  ARNOLD is a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes. ARNOLD is comprised of 8 language-conditioned tasks that involve understanding object states and learning policies for continuous goals.
  arXiv  Detail & Related papers  (2023-04-09T21:42:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.