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Bridging the NL2Pipeline gap, DataFlow-Harness enables LLMs to create reliable, editable data workflows at a fraction of the cost and time of traditional methods.
Even state-of-the-art models only achieve pass rates below 60% on a new benchmark that spans 1,431 diverse tasks, exposing critical weaknesses in general AI capabilities.
Physically aligned video models can boost robotic manipulation success rates by over 50% compared to traditional methods.
Beyond a certain threshold, queue peaks grow logarithmically rather than quadratically, fundamentally altering our understanding of scheduling efficiency in stochastic networks.
Ditching text-based chain-of-thought unlocks better audio-visual reasoning by interleaving textual steps with a unified latent space that preserves dense sensory information.
Stop wrestling with finicky evaluation codebases: One-Eval lets you specify LLM evaluation tasks in natural language and automatically executes them end-to-end.
