IRDAI plans to develop an AI governance framework via an industry working group. The record does not indicate a finalized or published framework yet.
Colorado repealed and replaced its AI Act, updating the state’s AI regulatory framework. The change indicates a material revision rather than a new standalone publication.
Announcement of a ceremony to sign an AI bill in Connecticut, indicating final legislative action has occurred.
Asia Insurance Review reports that India’s insurance regulator has constituted a working group to support AI governance in the insurance sector. The record does not specify any binding rule, consultation, or formal guidance 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 concentrated in a few large AI-adjacent transactions, with acquisition, debt, and late-stage financing all appearing in the same cycle. The pattern points to continued funding depth around infrastructure and high-value platform assets.
Capital remains concentrated in a few large AI-adjacent transactions, with acquisition, debt, and late-stage financing all appearing in the same cycle. The pattern points to continued funding depth around infrastructure and high-value platform assets.
Capital remains concentrated in a few large AI-adjacent transactions, with acquisition, debt, and late-stage financing all appearing in the same cycle. The pattern points to continued funding depth around infrastructure and high-value platform assets.
Named moves are concentrated at the top of the stack: one senior departure from a frontier lab and one promotion inside a major Chinese technology group. The signal is less churn than reallocation of authority in sales and research leadership.
Named moves are concentrated at the top of the stack: one senior departure from a frontier lab and one promotion inside a major Chinese technology group. The signal is less churn than reallocation of authority in sales and research leadership.
Named moves are concentrated at the top of the stack: one senior departure from a frontier lab and one promotion inside a major Chinese technology group. The signal is less churn than reallocation of authority in sales and research leadership.
Benchmark updates center on a single model family across reasoning and math tasks, which keeps attention on where general-purpose performance is still being tracked. The desk matters here because even a narrow score set can reset comparison baselines across adjacent evaluations.
Benchmark updates center on a single model family across reasoning and math tasks, which keeps attention on where general-purpose performance is still being tracked. The desk matters here because even a narrow score set can reset comparison baselines across adjacent evaluations.
Benchmark updates center on a single model family across reasoning and math tasks, which keeps attention on where general-purpose performance is still being tracked. The desk matters here because even a narrow score set can reset comparison baselines across adjacent evaluations.