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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.
Treating distillation targets as dynamic on-policy decisions rather than static teacher outputs prevents catastrophic error propagation when adapting compact vision-language models to out-of-distribution multimodal data.
SATATrack uses stable semantic descriptions to anchor tracking in rapidly changing environments, achieving state-of-the-art results in UAV Anti-UAV scenarios.