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AI agents can learn to climb leaderboards in complex adversarial games by refining executable policies, but struggle with intricate rule sets
Most video world models fail basic physics tests, with even the best models exhibiting significant physical inconsistencies across mechanics, optics, and fluids.
Safety in AI can now evolve from within the model itself, drastically enhancing long-term interaction capabilities without external constraints.
Only 5.4% of coding agent attempts successfully complete a migration while preserving behavioral correctness, exposing a critical gap in current AI capabilities for software evolution.
Agents struggle to significantly improve training algorithms, with the best only achieving 25% of the potential optimization gap.
