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Natural paper revisions can be harnessed to train AI agents for precise and context-aware editing of complex scientific diagrams.
Self-improving agents can evolve autonomously with minimal human input, reshaping our approach to AI adaptability and deployment.
Forget agents and world models – the future of computing could be learned directly from I/O traces, turning the model itself into the computer.
LLMs can explore codebases far more effectively by planning test sequences that balance immediate coverage with long-term reachability, boosting branch coverage by up to 77% compared to greedy methods.
