New York has passed a bill requiring AI crawlers to identify themselves to news sites. The measure is intended to increase transparency for automated scraping and crawling.
The U.S. Department of Commerce reportedly tightened export controls and blocked shipments of advanced AI chips to China tied to offshore units. The record is news-only and does not specify the exact rule or enforcement instrument.
Telecompaper reports that Ofcom has published an approach for AI adoption for the coming year. This appears to be an advisory/policy approach rather than a binding rule.
The Economic Times reports that China has issued guidelines related to financial services data as part of a broader cybersecurity push. The specific issuing agency and the document details are not provided in the record.
Effective exploration is a key challenge in reinforcement learning for large language models: discovering high-quality trajectories within a limited sampling budget from the vast natural language sequence space. Existing methods face notable limitations: GRPO samples exclusively from the root, saturating high-probability trajectories while leaving deep, error-prone states under-explored. Tree-base
Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune. Standard fine-tuning paradigms, including Reinforcement Learning (RL), fundamentally rely on backpropagation and continuous weights to compute gradients. Thus they cannot be used on quantized models, where the parameter spa
Evaluating new large language models typically requires costly human annotation campaigns at scale. LLM-as-a-judge offers a cheaper alternative, but judge scores carry systematic errors - such as position bias, self-preference, or intransitivity - that can strongly miscalibrate the resulting rankings. We quantify the resulting judge-human disagreement at two complementary levels. At the local leve
Reinforcement learning (RL) for large-scale Vision-Language-Action (VLA) models is severely bottlenecked by synchronization barriers and the high cost of environment data acquisition. To overcome these challenges, we propose AcceRL, a distributed asynchronous RL framework that physically isolates environment rollouts, model inference, and gradient updates. By eliminating the cascading long-tail id
Despite advances in information extraction driven by deep learning and large language models, performance gaps remain in highly specialized biomedical fields, where domainspecific complexity poses challenges for generalist models. In this work, we focus on the domain of autoimmunity, where the main entities of interest are autoimmune diseases, autoantibodies (i.e., molecules that may mark or cause
Capital is concentrating at the very top of the stack, with IPO-scale financing dwarfing everything else in the bundle. The cycle reads as infrastructure-heavy rather than broad-based, with one company absorbing outsized attention.
Capital is concentrating at the very top of the stack, with IPO-scale financing dwarfing everything else in the bundle. The cycle reads as infrastructure-heavy rather than broad-based, with one company absorbing outsized attention.
Capital is concentrating at the very top of the stack, with IPO-scale financing dwarfing everything else in the bundle. The cycle reads as infrastructure-heavy rather than broad-based, with one company absorbing outsized attention.
Named moves are sparse but directional: a CEO departure and two founder launches suggest reorganization at the edges of established platforms and new entrants. The signal is about team formation and continuity, not churn for its own sake.
Named moves are sparse but directional: a CEO departure and two founder launches suggest reorganization at the edges of established platforms and new entrants. The signal is about team formation and continuity, not churn for its own sake.
Named moves are sparse but directional: a CEO departure and two founder launches suggest reorganization at the edges of established platforms and new entrants. The signal is about team formation and continuity, not churn for its own sake.
Evaluation activity is centered on a single benchmark surface, with a wide spread between the strongest and weakest entries. The main takeaway is how uneven performance remains when agents are measured on the same task.
Evaluation activity is centered on a single benchmark surface, with a wide spread between the strongest and weakest entries. The main takeaway is how uneven performance remains when agents are measured on the same task.
Evaluation activity is centered on a single benchmark surface, with a wide spread between the strongest and weakest entries. The main takeaway is how uneven performance remains when agents are measured on the same task.