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ASPIRE achieves a staggering 31% success rate on unseen long-horizon tasks, compared to just 4% for prior methods, highlighting its superior adaptability and efficiency.
SkillMigrator slashes LLM action counts by up to 10% by leveraging layout structure for skill reuse, transforming web agent efficiency.
Static model parallelism can outperform dynamic approaches for load balancing in distributed multimodal LLM training by shifting the profiling paradigm to macroscopic batches.
Traditional research papers are costing AI agents reproducibility and understanding, but a new "Agent-Native" format that captures the full messy research process boosts performance by up to 20%.
Cornserve unlocks significantly faster and more efficient serving of complex multimodal AI models by intelligently distributing computation and data flow.
LLM task choice can swing inference energy by 25x, and video chews through 100x more power than images, revealing massive optimization potential in generative AI.
