6 papers
LLM-Guided Retrieval for Prediction of Molecular Perturbation Responses
Betty Xiong, Jan-Christian Huetter, Gabriele Scalia +2
Predicting transcriptomic responses to small-molecule perturbations across cell lines is central to drug discovery, but exhaustive profiling of drug-cell combinations is infeasible…
scCBGM: Interpretable Single-Cell Counterfactual Editing
Alma Andersson, Aya Abdelsalam Ismail, Edward De Brouwer +6
Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design. Single-cell RNA sequencing enables characterization…
Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation
Hugues Van Assel, Edward De Brouwer, Saeed Saremi +2
Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distributional fidelity, while SSL rew…
E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing
Shuvom Sadhuka, Drew Prinster, Clara Fannjiang +4
Agentic AI systems execute a sequence of actions, such as reasoning steps or tool calls, in response to a user prompt. To evaluate the success of their trajectories, researchers ha…
AssayBench: An Assay-Level Virtual Cell Benchmark for LLMs and Agents
Edward De Brouwer, Carl Edwards, Alexander Wu +9
Recent advances in machine learning and large-scale biological data collections have revived the prospect of building a virtual cell, a computational model of cellular behavior tha…
DC-W2S: Dual-Consensus Weak-to-Strong Training for Reliable Process Reward Modeling in Biological Reasoning
Chi-Min Chan, Ehsan Hajiramezanali, Xiner Li +6
In scientific reasoning tasks, the veracity of the reasoning process is as critical as the final outcome. While Process Reward Models (PRMs) offer a solution to the coarse-grained…