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Orca's unified world latent space enables superior performance in diverse tasks, outperforming specialized models with a single framework.
MLLMs struggle to convert visual evidence into context-specific actions, with performance plummeting by over 44% in certain scenarios.
RotMoLE's rotational gating unlocks more representational power from low-rank MoE architectures, even when expert diversity is limited.
Grounding RAG in historical texts demands more than semantic similarity; ChunQiuTR reveals the critical importance of temporal consistency, where even plausible evidence can be rendered invalid by subtle time-key mismatches.
