Can Large Language Models Truly Understand Your Daily Life? Introducing CL‑Bench Life

Tencent Hunyuan’s research team released CL‑Bench Life, a fully manually crafted benchmark designed to measure a model’s ability to learn from real‑life context. It contains 405 real‑world tasks and 5,348 rubrics (average 13.2 rubrics per task) that cover three core categories.

Core Context Categories

Communication & Social Interaction : one‑to‑one chats, noisy group conversations, active community discussions. Models must infer implied meanings, track evolving relationships, and extract useful information from casual dialogue.

Fragmented Information & Revision Trace : scattered personal notes, public information streams, and document revision histories. Models need to reconstruct logical flows from disordered fragments and understand how ideas evolve over time.

Behavioral Records & Activity Trace : game logs, digital footprints, long‑term personal tracking data. Models must reason from sequences of actions to infer underlying causes, habits, or anomalies.

Each task is accompanied by detailed rubrics that are highly atomic, enabling fine‑grained evaluation of whether a model’s answer satisfies specific criteria.

Evaluation Results

The team tested twelve language models (full results are on the open‑source leaderboard). Across the suite, models solved on average 14.5% of tasks . The best performer, GPT‑5.5 (High) , solved only 22.2% . This performance is lower than on the original CL‑Bench (where models exceed 20% task‑solving), highlighting the added difficulty of everyday, noisy context.

Adjusting the rubric‑pass threshold dramatically changes absolute pass rates—looser thresholds raise all models’ scores—yet the relative ranking of models remains stable, showing that CL‑Bench Life can distinguish partial context understanding from perfect task resolution while supporting consistent model comparison.

Error Analysis

The dominant error type is context misuse : models see the context but misinterpret or apply it incorrectly. In group‑chat scenarios, errors include role confusion (e.g., mistaking “Alice” for a superior) and speaker attribution mistakes. Unlike CL‑Bench, where misuse often means applying newly learned rules incorrectly, CL‑Bench Life misuse stems from misunderstanding everyday references, outdated information, or treating drafts as final decisions. Format errors and outright refusals are comparatively rare.

Impact of Context Length and Noise

Longer inputs do increase difficulty, but length alone does not predict task failure. When models operate in reasoning mode, the correlation between context length and performance weakens, indicating that high‑noise, fragmented inputs are the primary bottleneck rather than sheer length.

Conclusions

CL‑Bench Life is not merely a harder version of CL‑Bench; it is a complementary benchmark that assesses robustness to chaotic, fragmented, and evolving real‑life contexts. The findings show that even state‑of‑the‑art models are far from truly understanding daily life, explaining why users often find AI assistants unintelligent when handling personal chats, notes, or activity logs. The authors argue that advancing context learning in both structured professional domains and messy everyday scenarios is essential for building genuinely useful personal assistants.