Floating-point neural networks dominate modern machine learning but incur substantial inference costs, motivating emerging interest in Boolean networks for resource-constrained deployments. Since Boolean networks use only Boolean operations, they can achieve nanosecond-scale inference latency. However, learning Boolean networks that are both compact and accurate remains challenging because of thei
Automated molecular structure elucidation remains challenging, as existing approaches often depend on pre-compiled databases or restrict themselves to single spectroscopic modalities. Here we introduce SpectraLLM, a large language model that performs end-to-end structure prediction by reasoning over one or multiple spectra. Unlike conventional spectrum-to-structure pipelines, SpectraLLM represents
Modeling 3D dynamics is a fundamental problem in multi-body systems across scientific and engineering domains and has important practical implications in object trajectory prediction and simulation. While recent GNN-based approaches have achieved strong performance by enforcing geometric symmetries, encoding high-order features or incorporating neural-ODE mechanics, they typically depend on explic
Large Reasoning Models (LRMs) have recently achieved strong mathematical and code reasoning performance through Reinforcement Learning (RL) post-training. However, we show that modern reasoning post-training induces an unintended exploration collapse: temperature-based sampling no longer increases pass@$n$ accuracy. Empirically, the final-layer posterior of post-trained LRMs exhibit sharply reduce
Data-driven models are increasingly adopted in critical scientific fields like weather forecasting and fluid dynamics. These methods can fail on out-of-distribution (OOD) data, but detecting such failures in regression tasks is an open challenge. We propose a new OOD detection method based on estimating joint likelihoods using a score-based diffusion model. This approach considers not just the inp
Capital is concentrated at the top end, with one transaction far larger than the rest of the bundle. The rest of the desk reads as a sparse follow-on signal rather than a broad spread of new checks.
Capital is concentrated at the top end, with one transaction far larger than the rest of the bundle. The rest of the desk reads as a sparse follow-on signal rather than a broad spread of new checks.
Capital is concentrated at the top end, with one transaction far larger than the rest of the bundle. The rest of the desk reads as a sparse follow-on signal rather than a broad spread of new checks.
The talent desk is thin, but the named move still points to leadership churn at a major platform. Repeated entries suggest the underlying feed is duplicative rather than a wider hiring wave.
The talent desk is thin, but the named move still points to leadership churn at a major platform. Repeated entries suggest the underlying feed is duplicative rather than a wider hiring wave.
The talent desk is thin, but the named move still points to leadership churn at a major platform. Repeated entries suggest the underlying feed is duplicative rather than a wider hiring wave.
Benchmark activity is narrow but concrete: one system posts fresh scores across multiple difficulty tiers. The signal matters less for the absolute numbers than for showing where evaluation attention is currently landing.
Benchmark activity is narrow but concrete: one system posts fresh scores across multiple difficulty tiers. The signal matters less for the absolute numbers than for showing where evaluation attention is currently landing.
Benchmark activity is narrow but concrete: one system posts fresh scores across multiple difficulty tiers. The signal matters less for the absolute numbers than for showing where evaluation attention is currently landing.