Bipartisan lawmakers released a draft “Great American AI Act” and are seeking industry input. This indicates proposed federal legislation rather than an enacted rule.
Congressman Gabe Amo announced committee passage of a bipartisan bill intended to improve AI literacy for K-12 students. The record does not indicate enactment or an official bill number/status beyond committee passage.
The New York Legislature has passed a bill requiring disclosure of AI-generated news. The record does not provide the bill number or effective date.
The New York Legislature is considering a proposed one-year freeze on data centers, gaining momentum in the state legislative process. The record does not specify bill number or formal enactment.
A report discusses Colorado’s newly enacted AI bias law and its potential impact on trade secret management. The underlying legal change is a state AI bias requirement affecting covered uses.
While specialized Medical Vision-Language Models (VLMs) have achieved remarkable success in interpreting 2D and 3D medical modalities, their deployment for 3D volumetric data remains constrained by significant computational inefficiencies. Current architectures typically suffer from massive anatomical redundancy due to the direct concatenation of consecutive 2D slices and lack the flexibility to h
Meta-learning aims to uniformly sample homogeneous support-query pairs, characterized by the same categories and similar attributes, and extract useful inductive biases through identical network architectures. However, this identical network design results in over-semantic homogenization. To address this, we propose a novel homologous but heterogeneous network. By treating support-query pairs as d
Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture. Normalized Difference Vegetation Index (NDVI) forecasting from satellite observations, however, remains challenging due to sparse and irregular sampling caused by cloud masking, as well as the heterogeneous climatic conditions under which crops evolve. In this work, we propose
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann Generators (BGs), which allow rapid generation of uncorrelated equilibrium samples by combining a generative model with exact likelihoods and an importance sampling correction. However, modern BGs predominantly rely on normalizi
Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring. However, progress remains stifled by the lack of realistic benchmarks. Existing transaction-graph datasets suffer from two pervasive limitations: (i) they provide sparse node-level semantics beyond anonymized identifiers, and (ii) they rely on template-dr
Capital is concentrating in large, later-stage checks rather than scattered early bets. The cycle is led by public-market and undisclosed financing activity in hardware-adjacent names, signaling continued appetite for the AI supply chain.
Capital is concentrating in large, later-stage checks rather than scattered early bets. The cycle is led by public-market and undisclosed financing activity in hardware-adjacent names, signaling continued appetite for the AI supply chain.
Capital is concentrating in large, later-stage checks rather than scattered early bets. The cycle is led by public-market and undisclosed financing activity in hardware-adjacent names, signaling continued appetite for the AI supply chain.
Named moves are centered on formation rather than poaching: a new company is assembling its founding layer, and one junior hire adds capacity at an early-stage team. The signal is organizational buildout, not senior reshuffling.
Named moves are centered on formation rather than poaching: a new company is assembling its founding layer, and one junior hire adds capacity at an early-stage team. The signal is organizational buildout, not senior reshuffling.
Named moves are centered on formation rather than poaching: a new company is assembling its founding layer, and one junior hire adds capacity at an early-stage team. The signal is organizational buildout, not senior reshuffling.
Benchmark activity is limited to a small set of scores, but it still matters because it anchors current performance on core reasoning and math tasks. With no visible leaderboard churn, the main signal is the baseline itself.
Benchmark activity is limited to a small set of scores, but it still matters because it anchors current performance on core reasoning and math tasks. With no visible leaderboard churn, the main signal is the baseline itself.
Benchmark activity is limited to a small set of scores, but it still matters because it anchors current performance on core reasoning and math tasks. With no visible leaderboard churn, the main signal is the baseline itself.