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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.
Compound LLM systems do not need generative managers: swapping meta-agent models for a deterministic slot-merging operator consistently beats even oracle candidate selection by up to 19.5 points.
Self-writing evaluators can significantly improve the accuracy of response assessments by autonomously identifying defects in generated content.
A trust-tiered librarian can eliminate 6,845 contradictions in research reports while generating evidence-grounded narratives at unprecedented speed.