The EDPB issued a view on how personal data may be used to train and deploy AI models under EU data protection rules. The record is sourced via a secondary news item (Osborne Clarke).
The Illinois House passed a bill to accelerate lead service line replacement and sent it to Governor Pritzker. This is a state legislative action affecting lead service line replacement requirements.
Illinois lawmakers are advancing a proposed bill intended to regulate powerful AI models. The record does not indicate enactment or an official bill number/effective date.
The Singapore Ministry of Law issued a guide for using generative AI in the legal sector. The record is sourced from a Hogan Lovells news item and does not provide the guide’s text or dates.
Illinois proposes specific notice requirements for employers that use AI in hiring and employment decisions. The record is a news repost and does not provide the bill number or official 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 is clustering around a small number of outsized deals rather than spreading across the stack. The mix of acquisition, undisclosed financing, and debt shows late-stage and strategic money setting the tone.
Capital is clustering around a small number of outsized deals rather than spreading across the stack. The mix of acquisition, undisclosed financing, and debt shows late-stage and strategic money setting the tone.
Capital is clustering around a small number of outsized deals rather than spreading across the stack. The mix of acquisition, undisclosed financing, and debt shows late-stage and strategic money setting the tone.
Named moves are sparse but directional: one senior sales departure at a frontier lab and a scientific promotion at a major platform both signal internal rebalancing. The desk matters today because leadership changes are landing at companies already under pressure to scale.
Named moves are sparse but directional: one senior sales departure at a frontier lab and a scientific promotion at a major platform both signal internal rebalancing. The desk matters today because leadership changes are landing at companies already under pressure to scale.
Named moves are sparse but directional: one senior sales departure at a frontier lab and a scientific promotion at a major platform both signal internal rebalancing. The desk matters today because leadership changes are landing at companies already under pressure to scale.
Benchmark activity is limited to a narrow slice of the leaderboard, but it still matters because it shows where performance is being recorded and compared. The current entries keep attention on core reasoning and math surfaces rather than broad capability shifts.
Benchmark activity is limited to a narrow slice of the leaderboard, but it still matters because it shows where performance is being recorded and compared. The current entries keep attention on core reasoning and math surfaces rather than broad capability shifts.
Benchmark activity is limited to a narrow slice of the leaderboard, but it still matters because it shows where performance is being recorded and compared. The current entries keep attention on core reasoning and math surfaces rather than broad capability shifts.