Conditional generative models are increasingly used to predict biological and scientific responses under unseen experimental conditions. This paper investigates whether flow-matching models accurately recover non-additive interactions between factors or instead hallucinate them when extrapolating beyond the training support. The authors propose decomposing interaction errors into recovery and hallucinated components and find that aggregate fidelity metrics often conceal failures to learn interactions, while successful extrapolation depends critically on whether condition structure continues smoothly from observed to held-out regions.

ProtModernBERT tackles the high cost of training protein language models by porting modern NLP encoder improvements—RoPE, FlashAttention, GeGLU, and bias-free layers—into a BERT-style protein model. The authors pre-train on the UR100P corpus and show that a 140M-parameter model can match ProtBert and Amplify-120M on structural benchmarks while using roughly one-tenth the pre-training FLOPs of ProtBert. The work matters because it tests whether architectural modernization alone can lower the barrier to custom protein LMs for resource-constrained labs, and it uses random-initialization baselines to separate inductive biases from transfer-learned features.

This paper tackles the problem that agentic scientific workflows can finish without crashing yet still emit scientifically invalid results due to silent faults, stale inputs, or contaminated shared ancestors. The core idea is a directed acyclic graph harness that treats trust as a release decision: it enforces mandatory checks, contains failures to affected branches, and uses a fixed risk model plus optional validators to decide whether a deliverable may be released. Language-model agents operate inside this layer, choosing corrective actions and reviewing releases, but cannot override it. On two synthetic workflows with injected faults, the authors decompose which harness component supplies which property, finding that structure, not the LLM, drives reliability.

MorphoBranch tackles the problem of accurately quantifying fine, weakly labeled, and densely branched cellular structures—such as neuronal axons and microglial processes—from fluorescence microscopy images. The core idea is an integrated workbench that pairs a deterministic image-to-graph Morphometry Engine (multiscale ridge evidence, hysteresis segmentation, and evidence-constrained skeleton refinement) with an optional LLM-assisted Refinement Engine that translates natural-language instructions into editable, human-reviewable analysis plans. This separation of flexible interaction from deterministic computation aims to reduce operator-dependent bias while keeping morphometric workflows inspectable and reproducible.

Virtual cell modeling aims to simulate cellular responses to drug perturbations in silico, but existing flow-matching models optimize only pixel-level reconstruction and can produce biologically implausible outputs like nuclei outside cytoplasm. CellFluxRL addresses this by post-training the state-of-the-art CellFlux model with reinforcement learning, using seven manually designed reward functions spanning biological function (mode of action), structural validity (nuclear containment), and morphological statistics (size/count). The approach reveals a systematic framework for enforcing physical constraints through differentiable optimization, achieving consistent improvements across all biological metrics while maintaining image quality.