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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.
Contextual contamination in few-shot vision-language learning is mitigated by a novel semantic purification and reinforcement-learning gating mechanism, unlocking superior performance.
