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Training LLMs with long contexts can paradoxically weaken their ability to retain knowledge, leading to poorer performance when context is not available.
Programmatic skill learning can slash agent costs while enhancing performance, with SpeedRunner leading the charge in cost-efficient adaptation.
SelfCompact reveals that language models can autonomously manage context decay, achieving up to 18.1 points improvement in performance while cutting token costs by 30-70%.
Trust functions can transform weak supervision into a powerful training signal, enabling models to achieve near-lossless generalization even with unreliable labels.
Agents struggle to match human performance in long-horizon tasks, revealing critical gaps in their learning capabilities during deployment.
Even state-of-the-art LLMs struggle to follow complex instruction hierarchies, achieving only ~40% accuracy when navigating conflicts across a dozen privilege levels in agentic tasks.
A 1000x larger video reasoning dataset reveals early signs of emergent generalization, offering a new foundation for training and evaluating spatiotemporal AI.
