AI for Science

A new arXiv paper introduces MOJO, a joint training framework that combines masked autoencoding with supervised decoding for spike-tokenizing neural models. The approach aims to make better use of unlabelled neural recordings, especially when behavioural labels are scarce.

A new arXiv paper tests whether frozen Evo 2 representations contain linearly accessible biosecurity-relevant information. Lightweight probes show strong performance on antimicrobial resistance detection, while virulence and generated-sequence labels remain more limited.

A new arXiv paper examines feature selection in renewable power prediction and proposes Cluster-based Sequential Feature Selection. CSFS matches the predictive performance of standard sequential feature selection while cutting computational cost by 21% on average.

A new arXiv paper extends empirical Bayes variational autoencoders to jointly model tumor growth, time to dropout, and genomic covariates. The work combines neural dynamics with semi-mechanistic pharmacometric structure.

A new arXiv paper introduces PlumeQuant, a diagnostic framework for checking whether methane plume masks, IME, plume length, emission rates and uncertainty estimates are internally consistent. The study shows that published scalar quantities often do not uniquely determine the plume boundary.

A new arXiv study proposes a two-step unsupervised clustering strategy for multimodal cardiac PET/MRI data. The method generates automated text and bullseye reports that align reasonably well with cardiac imaging specialists’ observations.

A new arXiv paper applies Quantum Kitchen Sinks to RF spectrogram anomaly detection using measured cellular signals and validation on IBM quantum hardware. The strongest results come from DCT representations paired with moderate-depth entangled QKS circuits.

A new arXiv paper introduces OCP-CT, a CT vision-language pretraining framework that aligns volumes and reports through organ-conditioned radiological pattern tokens. The work targets a key weakness of global scan-report contrastive learning: its tendency to blur heterogeneous organ-level evidence.

A new arXiv paper presents an adaptive digital twin framework for predicting brain tumor evolution and scheduling treatment. The approach combines a mechanistic reaction–diffusion model, 3D residual learning, online patient-specific updating, and model predictive control.

A new arXiv paper introduces Quantum Topological Data Encoding (QTDE), a framework for encoding geometric and topological structure into quantum states through topology-driven quantum evolution. Early experiments on clique-complex classification suggest that these quantum representations can outperform a baseline based on direct Laplacian comparisons.

A new arXiv paper presents a fully automated multimodal framework for assessing pancreatic ductal adenocarcinoma resectability. The system combines 3D contrast-enhanced CT with structured clinical variables to classify patients into NCCN resectability categories.

The arXiv paper “CAS I: A Geometric Coding Theorem” proposes a group-theoretic analogue of the classical Coding Theorem by measuring how likely a symmetry is to isolate a binary string as its unique fixed point.

A new arXiv paper reframes regularity through a communication-complexity lens: split an input word between Alice and Bob, then ask whether a constant amount of communication is enough to compute the function. The goal is to unify Nerode-style characterizations across different output domains.

A new arXiv paper applies Neural Spline Flows to a mono-Z dark matter search using CMS Run 2015D open data. The study reports no evidence for dark matter, but highlights how likelihood-ratio scoring in high-dimensional phase space could reshape particle-physics analyses.

A new arXiv paper introduces MxGPS, a multiplex graph transformer designed to reduce topology overfitting in power-grid GNNs. By jointly training on static state estimation and AC power flow, the model aims to generalize better to unseen grid topologies.

A new arXiv paper compares a variational quantum circuit with a parameter-matched classical message-passing model for molecular property prediction. Its main message is that topology-aligned inductive bias, not the quantum label alone, appears to drive parameter efficiency.

A new arXiv paper explores whether Quantum Generative Adversarial Networks can support the security assessment of post-quantum cryptography. Rather than claiming a practical break, the work frames QGANs as an early building block for quantum-assisted cryptanalysis workflows.