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On Friday, Feb. 20, we will be live blogging as the court releases opinions in one or more argued cases from the current term. Click here for a list of FAQs about […]The postAnnouncement of opinions for Friday, February 20appeared...

Information and updates about emergent situations at Stanford University.

The International Association for Artificial Intelligence and Law (IAAIL) invites initial bids (expressions of interest) to host the 22nd International…

Dear Community, The deadline for submission of workshop and tutorial proposals for ICAIL 2026 has been moved to December 12, 2025 To submit a workshop or a…

Last week the White House released a National Security Memorandum concerning AI governance and risk management. The NSM issues guidance […]

36th International Conference on Legal Knowledge and Information Systems

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Big tech companies tend to make a lot of enemies — but there are none more powerful than the US government. Apple, Google, Amazon, and Meta are regularly called in front of Congress to fend off monopoly accusations — and...

Once a modest online seller of books, Amazon is now one of the largest companies in the world, and its former CEO, Jeff Bezos, is the world’s most wealthy person. We track developments, both of Bezos and Amazon, its growth...

The Verge is about technology and how it makes us feel. Founded in 2011, we offer our audience everything from breaking news to reviews to award-winning features and investigations, on our site, in video, and in podcasts.

The Verge is about technology and how it makes us feel. Founded in 2011, we offer our audience everything from breaking news to reviews to award-winning features and investigations, on our site, in video, and in podcasts.

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Artificial intelligence is more a part of our lives than ever before. While some might call it hype and compare it to NFTs or 3D TVs, AI is causing a sea change in nearly every part of the technology industry....

arXiv:2602.13452v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for crisis preparedness and response, particularly for multilingual communication. However, their suitability for high-stakes crisis contexts remains insufficiently evaluated. This work examines the performance of state-of-the-art LLMs and...

arXiv:2602.17910v1 Announce Type: new Abstract: Traditional AI alignment primarily focuses on individual model outputs; however, autonomous agents in long-horizon workflows require sustained reliability across entire interaction trajectories. We introduce APEMO (Affect-aware Peak-End Modulation for Orchestration), a runtime scheduling layer that...

arXiv:2602.17686v1 Announce Type: cross Abstract: Distilling Chain-of-Thought (CoT) reasoning from large language models into compact student models presents a fundamental challenge: teacher rationales are often too verbose for smaller models to faithfully reproduce. Existing approaches either compress reasoning into single-step,...

arXiv:2602.18918v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used as scientific copilots, but evidence on their role in research-level mathematics remains limited, especially for workflows accessible to individual researchers. We present early evidence for vibe-proving with a...

arXiv:2602.19416v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) enables powerful LLM alignment but can introduce reward hacking - models exploit spurious correlations in proxy rewards without genuine alignment. Compounding this, the objectives internalized during RLHF remain opaque,...

arXiv:2602.18922v1 Announce Type: new Abstract: Personal AI agents incur substantial cost via repeated LLM calls. We show existing caching methods fail: GPTCache achieves 37.9% accuracy on real benchmarks; APC achieves 0-12%. The root cause is optimizing for the wrong property...

arXiv:2602.24037v1 Announce Type: new Abstract: Market regime shifts induce distribution shifts that can degrade the performance of portfolio rebalancing policies. We propose macro-conditioned scenario-context rollout (SCR) that generates plausible next-day multivariate return scenarios under stress events. However, doing so faces...

arXiv:2602.23447v1 Announce Type: cross Abstract: Detection of rare lesions in whole-body CT is fundamentally limited by extreme class imbalance and low target-to-volume ratios, producing precision collapse despite high AUROC. Synthetic augmentation with diffusion models offers promise, yet pixel-space diffusion is...

arXiv:2603.00028v1 Announce Type: new Abstract: Effective communication in health care is critical for treatment outcomes and adherence. With patient-provider exchanges shifting to secure messaging, analyzing electronic patient-communication (EPPC) data is both essential and challenging. We introduce EPPCMinerBen, a benchmark for...

arXiv:2603.02874v1 Announce Type: new Abstract: Transformers excel at in-context retrieval but suffer from quadratic complexity with sequence length, while State Space Models (SSMs) offer efficient linear-time processing but have limited retrieval capabilities. We investigate whether hybrid architectures combining Transformers and...

arXiv:2603.03119v1 Announce Type: new Abstract: Agentic AI deployments increasingly behave as persistent institutions rather than one-shot inference endpoints: they accumulate state, invoke external tools, coordinate multiple runtimes, and modify their future authority surface over time. Existing governance language typically specifies...

arXiv:2603.02258v1 Announce Type: new Abstract: Do neural machine translation models learn language-universal conceptual representations, or do they merely cluster languages by surface similarity? We investigate this question by probing the representation geometry of Meta's NLLB-200, a 200-language encoder-decoder Transformer, through...