Illinois enacted comprehensive AI safety regulation, reported as passing with support from OpenAI and Anthropic. Specific bill number and statutory details are not provided in the record.
New York lawmakers passed a bill intended to protect minors from AI chatbots. The record does not provide the bill number or the enacted text details.
Let's Data Science reports that Saudi Arabia published details of a national data monetization policy. The record does not provide the primary legal instrument text or an official publication date.
A joint statement from the Bank of England, the Financial Conduct Authority, and HM Treasury on AI frontier models and cyber resilience. The record appears to be an official policy statement rather than a binding regulation.
A report attributes to the Cyberspace Administration of China (CAC) an action to strengthen online ecosystem governance and regulate/manage minors' internet use. The source provided is a news/RSS item without the underlying official instrument text.
Decentralized finance exposes supervisors to fast-moving, networked credit risks. General-purpose LLM agents fit this setting poorly: they over-read weak evidence and recommend high-stakes interventions, while existing evaluations offer no regulator-aligned way to measure the resulting false alarms. We introduce DeXposure-Claw, a forecast-grounded agentic supervision system that routes LLM decisio
While LLM-powered agents offer end-to-end automation for industrial asset lifecycles, real-world Industry 4.0 deployment is hindered by latency, concurrency instability, and safety risks. We present DynAMO (Dynamic Asset Management Orchestration), a deployment-ready engine using a Plan-then-Execute architecture to generate verifiable workflow graphs. DynAMO supports both SequentialWorkflow (topolo
The ``Pre-train, then fine-tune'' paradigm has revolutionized Natural Language Processing (NLP). In this context, transferable backdoors pose a severe threat to the Pre-trained Language Models (PLMs) supply chain, yet defensive research remains nascent, primarily relying on detecting anomalies in the output feature space. We identify a critical flaw that fine-tuning on downstream tasks inevitably
Instruction fine-tuning of large language models (LLMs) often involves selecting a subset of instruction training data from a large candidate pool, using a small query set from the target task. Despite growing interest, the literature on targeted instruction selection remains fragmented and opaque: methods vary widely in selection budgets, often omit zero-shot baselines, and frequently entangle th
When AI agents use language models to evaluate their own outputs in a feedback loop, systematic biases emerge. We show that Evaluator Preference Collapse (EPC) is dramatically amplified in multimodal settings. Using GPT-4o to evaluate DeepSeek-chat across text and visual tasks, we find that a single strategy (step_by_step) absorbs 48.4% of all weight -- 3.2x the collapse observed in text-only self
Capital remains highly concentrated in a small number of outsized transactions, with infrastructure and adjacent platform assets absorbing the largest checks. The pattern points to late-stage conviction and balance-sheet strength rather than broad-based seed activity.
Capital remains highly concentrated in a small number of outsized transactions, with infrastructure and adjacent platform assets absorbing the largest checks. The pattern points to late-stage conviction and balance-sheet strength rather than broad-based seed activity.
Capital remains highly concentrated in a small number of outsized transactions, with infrastructure and adjacent platform assets absorbing the largest checks. The pattern points to late-stage conviction and balance-sheet strength rather than broad-based seed activity.
Named moves continue to matter where they alter commercial reach or technical authority: one senior sales departure and return, plus a chief-scientist promotion, signal internal reshaping at major AI firms. The desk is about leverage, not churn.
Named moves continue to matter where they alter commercial reach or technical authority: one senior sales departure and return, plus a chief-scientist promotion, signal internal reshaping at major AI firms. The desk is about leverage, not churn.
Named moves continue to matter where they alter commercial reach or technical authority: one senior sales departure and return, plus a chief-scientist promotion, signal internal reshaping at major AI firms. The desk is about leverage, not churn.
Benchmark updates are sparse but still useful: the cycle adds fresh scores across core reasoning and knowledge tests, keeping attention on where open-weight systems are landing relative to established baselines. Even a small set of entries can move the comparison frame when they cluster around general-purpose capability.
Benchmark updates are sparse but still useful: the cycle adds fresh scores across core reasoning and knowledge tests, keeping attention on where open-weight systems are landing relative to established baselines. Even a small set of entries can move the comparison frame when they cluster around general-purpose capability.
Benchmark updates are sparse but still useful: the cycle adds fresh scores across core reasoning and knowledge tests, keeping attention on where open-weight systems are landing relative to established baselines. Even a small set of entries can move the comparison frame when they cluster around general-purpose capability.