Yan-Chao Li

Cell-Lens: Advancing LLM Reasoning at Single-Cell Resolution

Equivariant Neural Primal-Dual Assignment for Maximum Common Edge Subgraphs

This work introduces Equivariant Neural Primal-Dual Assignment (ENPDA), which learns a shared matching policy and applies it to new pairs without further training, answering queries roughly three orders of magnitude faster and recovering its training cost after a few dozen queries.

Disagree to Accelerate: Closing the Loop on Diffusion Feature Forecasts

This work introduces RACER, a training-free closed-loop controller that continuously shrinks uncertain forecasts toward the last computed feature and derives a deterministic error bound for the shrinkage and empirically evaluates its validity and tightness across acceleration regimes.

Reachability Is Not Realization: Tracing the Sources of LLM Benchmark Gains

This work establishes a question-level audit under fixed budgets, temperatures, and answer formats, and asks why reachable answers sometimes fail to appear, and test whether inference-time layer routing can expand reachability.