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LLMs can seamlessly generate formal proofs with a new system that mimics natural mathematical language, bridging the gap between human intuition and formal verification.
Achieving differential privacy in multi-objective submodular maximization opens new avenues for handling sensitive data without sacrificing performance.
Recurrent memory can be added to transformers at scale with minimal parameter overhead and no performance penalty by reusing existing hidden states and training with interleaved parallel updates.
