Ben Gao

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

ChemMLLM: Chemical Multimodal Large Language Model

This work designs five types of multimodal tasks across text, molecular SMILES strings and images, and curates the datasets, demonstrating the feasibility of unifying multiple cross-modal chemical tasks within a single foundation model and enabling more intuitive, visual human-AI interaction.

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.

Chem-R: Learning to Reason as a Chemist

The proposed Chem-R, a general Chemical Reasoning model designed to emulate the deliberative processes of chemists, achieves state-of-the-art performance on comprehensive benchmarks, surpassing leading LLMs, including Gemini-3-Pro and Kimi-k2.5.

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.

Resolving Chemically Inequivalent 11B NMR Sites via Interpretable Hybrid Machine Learning

A manually verified, solvent-annotated 11B NMR data set constructed via a large language model (LLM)-assisted workflow provides a form of virtual spectral resolution, enabling the discrimination of chemically inequivalent boron sites that are difficult to resolve experimentally.

Chem-R: Learning to Reason as a Chemist

The proposed Chem-R, a general Chemical Reasoning model designed to emulate the deliberative processes of chemists, achieves state-of-the-art performance on comprehensive benchmarks, surpassing leading LLMs, including Gemini-3-Pro and Kimi-k2.5.