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A unified layer equation for GNNs reveals how over 200 architectures can be systematically compared and optimized for performance.
Personalization in AI co-scientists could be the key to unlocking novel research insights that generic systems overlook.
XBRIDGE reduces communication latency by 11x while ensuring that heterogeneous LLMs maintain entity identity and contextual relevance.
Abandoning biased offline critics leads to more efficient online reinforcement learning, achieving superior performance on challenging tasks.
A single checkpoint can now adapt to any model size, streamlining the deployment of elastic retrieval systems and achieving faster performance without sacrificing quality.
Positional bias in LLMs can drastically alter predictions, with accuracy and stability often at odds, challenging our understanding of model reliability in ordinal classification tasks.
