Mingyu Li

The paper introduces FEDB, a novel confidential database design that eliminates cryptographic operations from the critical query path, significantly reducing performance overhead for secure querying over sensitive data.

The paper provides the first theoretical convergence analysis for machine learning training under fully homomorphic encryption combined with differential privacy, improving efficiency and scalability.

The paper introduces Expected Value Alignment (EVA), a novel reward modeling procedure that allows continuous scoring of intermediate reasoning steps in formal mathematics verification while maintaining the discrete, textual output format of generative models.

Soft-NBCE introduces soft entropy-weighted chunk fusion to overcome the semantic fragmentation caused by hard chunk selection in long-context LLMs, significantly improving performance on multi-hop benchmarks.