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CAP is proposed, a single-stage humanoid locomotion policy that recovers this signal with a perceptive world-model encoder trained as a learned denoiser to reconstruct clean depth from a corrupted input, together with a co-active proprioceptive variational encoder that supplies depth-free body-state information.
Cross-model KV sharing can boost accuracy and cut prefill costs dramatically, challenging the notion that KV states are strictly model-local.
A striking task-dependent robustness gap reveals that while ASR thrives on direct audio retrieval, AQA falters due to bottlenecks in mediated information access.
Entity type information can dramatically boost SciNER performance, enabling LLMs to match fully supervised models without extensive human input.
Compact models like Athena-Brain-8B can outperform larger counterparts in embodied tasks while maintaining strong general intelligence.
