preprint

This article is an academic paper by Tristan Buckmaster on the Navier-Stokes equations, likely exploring mathematical solutions or properties.

A research paper titled 'Long Gaps' by Professor Jared Duker Lichtman of Stanford is hosted on OpenAI's platform, suggesting a focus on mathematical aspects in AI research.

This paper introduces causal foundation models (CFMs), which are pretrained neural networks that estimate causal quantities on new datasets using in-context learning without requiring fine-tuning.

Scientists have found the most convincing evidence yet of a dark matter particle from an underground experiment, though it's a single unexplained event and not yet confirmed.

This paper introduces a graph neural regression framework for non-invasive estimation of body composition metrics such as body fat percentage and bone mineral density, demonstrating improved accuracy over previous methods using clinical data.

An LLM-agent framework is presented for large-scale analysis of cross-tissue protein co-abundance networks, identifying conserved clusters and generating mechanistic hypotheses for disease mechanisms.

This paper investigates the capability of automated alignment researchers to mitigate alignment failures such as deception and sycophancy, demonstrating that they can outperform human researchers and generalize to larger models while preserving capabilities.

Google Research's WikiSkill preprint demonstrates that compiling execution history into skill files allows a 9B AI model to outperform a 27B model in benchmarks, highlighting the role of procedural memory and skill transfer in agent performance.

Entropic Scree is a new diagnostic tool for assessing signal strength and structure in dirty tabular data using mutual information, with a preprint and upcoming Python and R packages.

This paper formulates the collaboration tax in LLM multi-agent systems, measuring it across tasks and models to reveal predictable mechanisms and practical interventions.

The paper introduces Coherentist Probabilistic Compositionalism (CPC), a framework for interpreting transformer computation through four operator roles, and validates it across 15 models from five architecture families.

Wazobia Eval is a benchmark designed to evaluate AI models on emotion understanding, sarcasm detection, and cultural reasoning in Nigerian Pidgin, aiming to advance NLP capabilities for this language.

The AQuA v2 preprint introduces a memory system for research agents that uses persistent evidence from accepted and rejected experiments to guide future proposals, emphasizing the importance of evidence lifecycle management.

This paper provides a theoretical analysis of the edge-of-stability phenomenon for the Adam optimizer on a one-dimensional quadratic function, proving that Adam exhibits a restoring mechanism that pushes sharpness toward a stability threshold.

RIBOSPAN is a large bidirectional RNA foundation model pretrained on up to 10,240 nucleotides, enabling high-resolution full-transcript modeling and mRNA generation through discrete diffusion.

This preprint demonstrates that evaluation resolution significantly affects the identification of brain-like learning rules in the visual cortex (V1), using CNNs with various learning rules and fMRI data.

Researchers discovered that radiation damage to the Hubble Space Telescope's CCD detectors is out of phase with the Solar cycle and developed empirical fits to correct over 99.5% of the damage in images.

The Entropic Scree is an information-theoretic upgrade to PCA for robust rank estimation and dimensionality reduction on messy tabular data, with open-sourced code and a preprint released.

The article presents a new ML primitive called the Spectral Neuron, offering a simple, scalable, and interpretable model with a mathematical foundation for training and initialization.

This research paper compares various machine learning classifiers for heart disease prediction, finding that Support Vector Machine and Simple Cart achieve the best performance on UCI and Kaggle datasets respectively, highlighting ML's potential to aid in early clinical diagnosis.