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Omni-modal foundation models achieve self-improvement via RL compute scaled to 1M context, leveraging asynchronous training and agentic grading for enhanced intelligence.
LLM agents still fail to reliably automate real-world workflows, with even the best models succeeding on only two-thirds of tasks in a new live benchmark.
Current autonomous agent benchmarks miss nearly half of safety violations and over 10% of robustness failures because they only check final outputs, a problem Claw-Eval directly addresses.