Regulation

arXiv:2607.22006v1 Announce Type: new Abstract: Buildings account for roughly 34% of global final energy use and 37% of energy- and process-related CO$_2$ emissions. Stranding regulation now being enacted (New York City Local Law 97, the EU Energy Performance of Buildings Directive recast) presupposes that a building portfolio's carbon intensity can be measured per square metre and compared against a science-based pathway. Whether corporate disclosure is actually fit for that comparison has not,

arXiv:2607.21608v1 Announce Type: cross Abstract: With the EU AI Act entering into force, organizations developing or operating AI systems face new obligations on transparency, risk management, and traceability. For Requirements Engineering (RE), these obligations must be translated into testable, auditable requirements and verifiable evidence. However, many organizations currently lack systematic processes to achieve this. We hypothesize that LLM-based agentic validation tools can support this

arXiv:2607.21799v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly perform commercial tasks that involve retrieving external content such as images and, where appropriate, reproducing that content. LLM agents should comply with the law, including copyright law. Presently, however, we lack adequate frameworks to assess whether they do so in practice. To that end, we introduce \textbf{Copyright-Bench}, a benchmark designed to evaluate \textit{LLM agents' compliance wi

arXiv:2510.01467v2 Announce Type: replace Abstract: The rapid emergence of generative artificial intelligence (AI) and related technologies has the potential to dramatically influence higher education, raising questions about the roles of institutions, educators, and students in a technology-rich future. While existing discourse often emphasizes either the promise and peril of AI or its immediate implementation, this paper advances a third path: a principled framework for guiding the use of AI i

arXiv:2602.09678v3 Announce Type: replace Abstract: Since 1887, administrative law has confronted a problem of institutional cognition. Expert agencies are needed to govern technologically complex systems, but expertise makes agency decisions difficult for courts, Congress, and the public to understand and oversee. Administrative law has responded to this "capability-accountability trap" by requiring records, reason-giving, and transparency, drawn together through procedural review. These device

On Saturday, China’s State Administration for Market Regulation (SAMR) imposed administrative penalties on Ctrip Group for abusing its dominant market position in violation of the country’s Anti-Monopoly Law. The total fine and confiscated illegal gains amounted to RMB 5.179 billion ($760 million). In a statement released the same day, Ctrip said it had received the […]

In this document, the Federal Communications Commission (Commission or FCC) adopted a Second Report and Order that promotes the deployment of submarine cables while strengthening national security interests in connection with their modern capabilities. The Second Report and Order establishes a regulatory regime for licensing owners and/or operators of submarine line terminal equipment (SLTE), including granting a blanket license to certain current and future SLTE owners and/or operators that are

Background : Deep neural networks increasingly power language, vision, and decision systems, yet many deployments require explanations that are faithful, compositional, and governance-ready. Symbolic techniques promise these properties, but the literature mixes post-hoc extraction, knowledge injection, and intrinsically hybrid designs without a unifying view. Objectives : We provide a systematic review and synthesis of symbolic explainable AI (XAI) for deep learning (January 2017– June 2025), or

Forecasting corporate earnings growth is fundamental to investment, credit, and regulatory decision-making. Existing forecasting approaches either rely on restrictive linear assumptions or provide limited interpretability, making them less suitable for high-stakes financial applications. This study proposes a transparent and causally informed framework for predicting future corporate earnings growth from financial statement data. We present TRuE-XAI (Transparent, Rule-based, and Explainable Arti

Corporate boards increasingly bear formal fiduciary responsibility for overseeing AI-driven strategic and sustainability decisions, yet their practical capacity to exercise such oversight substantively remains poorly documented. This study investigates whether a measurable AI governance capability gap exists between board-level and executive-level AI competence and examines the implications of this gap for the integrity of AI-driven ESG reporting. A pilot study (n = 26) of board members and seni

Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear. Existing models are trained on uncontrollable and opaque Internet data, making it difficult to identify how planning ability is acquired, shaped, and integrated. To address this challenge, we introduce a unified and controlled multi-turn environment that enables precise control. It allows systematically study long-horizon planning across three stages. (1) Planning abilit

On-policy distillation (OPD) adapts diffusion models by querying a teacher along trajectories generated by the current student, but how it should behave under classifier-free guidance (CFG), a default component of modern diffusion systems, remains poorly understood. Existing OPD methods naturally extend velocity matching to the CFG-composed prediction, directly matching teacher and student guided velocities. We show that this objective is under-identified at the branch level: positive- and negat

The alignment of Small Language Models (SLMs) in the 70--500M parameter range using reinforcement learning is often considered unstable, though the underlying failure mechanisms have not been systematically investigated. In the State-of-the-Art (SOTA) research, fifteen (model, corpus) configurations were trained using Proximal Policy Optimization (PPO). The experiments included Pythia-70M, 160M, 410M and SmolLM2-135M, 360M on the TinyStories, CNN/DailyMail, and Wikitext-103 corpora. Three reprod

Biomedical image analysis spans diverse modalities and tasks, yet real-world deployment is hindered by severe distribution shifts across scanners, protocols, and patient populations. High-performing models consequently require repeated domain-specific fine-tuning, which is a costly cycle that becomes impractical when labels are scarce or privacy constraints limit data sharing. We propose OPERA (Offline Policy-guided Expert Routing and Adaptation), a multi-agent ensemble framework that addresses

The study, released after months of delays and mounting criticism, was ordered two years ago amid growing concerns about resources for data centers.

On-policy distillation (OPD) trains a student on its own trajectories while a teacher supplies dense token-level likelihoods at student-visited prefixes. These likelihoods are often read locally: agreement appears safe to imitate, whereas disagreement appears to identify an error. We show that both readings are confounded by the outcome of the completed trajectory. We introduce an outcome-resolved diagnostic that crosses pointwise teacher-student divergence with final-answer correctness, separat

Designing effective reward functions for model-free reinforcement learning under non-holonomic constraints remains a persistent challenge, often resulting in severe local minima such as policy paralysis or over-conservative hazard avoidance. In this work, we present a parameterized reward shaping framework featuring coverage-gated alignment feedback, drive-direction switch regularization, and an aligned episode termination mechanism evaluated on an autonomous parallel parking task. Crucially, we

The Nuclear Regulatory Commission is proposing to eliminate a foundational safety principle that has for 50 years minimized the radiation people in the United States are exposed to, and that has been ...

The Trump administration is planning targeted bans on Chinese AI models rather than a blanket ban. After public pressure, OpenAI and Google DeepMind signed an open letter opposing regulation of open-weight models, yet OpenAI and Anthropic continue to lobby privately for those same restrictions amid security concerns and powerful business interests. The article US reportedly favors selective bans over blanket restrictions on Chinese open weight models citing security concerns appeared first on Th

In its judgment of 11 November 2025 (42 O 14139/24), the Munich I Regional Court (Germany) issued a widely noted precedent on the copyright assessment of AI training and outputs under German and EU law. According to its press release1, the ... (https://incidentdatabase.ai/cite/1278#7586)

OpenAI has been ordered to pay damages after a court in Germany ruled that its chatbot ChatGPT violated German copyright laws. The Munich regional court ruled in favour of the German music performance rights organization GEMA which manages ... (https://incidentdatabase.ai/cite/1278#7587)

"And the survey says… policy will likely stay on hold for an additional meeting. Hawkish momentum is building, however. We expect two hawkish dissents."

Gene regulatory network modeling often requires balancing predictive accuracy and mechanistic interpretability. In this work, we compare continuous surrogate models and a discrete mechanistic model on the same \textit{Arabidopsis thaliana} induced systemic resistance (ISR) dataset, using both the raw continuous gene-expression measurements and their sign-binarized representation. The study considers eight defense-related genes measured over nine time points and evaluates two continuous predictor

Distillation has long been a topic for AI wonks, but it's become a hot-button issue of late as techies and lawmakers debate how it should be regulated.

China’s market regulator fined Trip.com Group $765 million on Saturday after concluding that the country’s largest online travel platform abused its dominant market position. The State Administration for Market Regulation said Trip.com used its traffic allocation algorithms, platform rules and technology to restrict hotel operators from listing on competing services and to control the prices […] This story continues at The Next Web

Publication date: September 2026 Source: Technology in Society, Volume 88 Author(s): Francesco Cappellano, Cristian Matti, Matjaz Vidmar

Publication date: September 2026 Source: Technology in Society, Volume 88 Author(s): Asad Mahmood, Tian Yixiang, Muhammad Adeel Khan

Publication date: October 2026 Source: Telecommunications Policy, Volume 50, Issue 9 Author(s): Pratik Tarafdar, S.S. Swathysree

Publication date: October 2026 Source: Telecommunications Policy, Volume 50, Issue 9 Author(s): Wecka Imam Yudhistyra, Chalita Srinuan

Publication date: October 2026 Source: Telecommunications Policy, Volume 50, Issue 9 Author(s): Angel Valarezo-Unda, Javier Capilla, Teodosio Pérez-Amaral, Alfredo García-Hiernaux, Rafael López

Publication date: September 2026 Source: Telecommunications Policy, Volume 50, Issue 8 Author(s): Yuran Shi, Qixuan Wu

Publication date: November 2026 Source: Technological Forecasting and Social Change, Volume 232 Author(s): Fang Fang, Yikun Dai, Knut Blind, Biaoan Shan

Publication date: October 2026 Source: Research Policy, Volume 55, Issue 8 Author(s): Wanxiang Cai, Haneul Choi, Tianshu Zhao, Max Munday

Publication date: October 2026 Source: Research Policy, Volume 55, Issue 8 Author(s): Liecheng Qiao, Xiaofang Dong

Publication date: October 2026 Source: Research Policy, Volume 55, Issue 8 Author(s): Susanna Mansikkamäki, Antti Sihvonen, Mirva Peltoniemi, Kalle Pajunen

How can organisations innovate with AI while meeting growing governance and regulatory expectations?

China’s market regulator has imposed a 5.2 billion yuan (US$765 million) penalty on Trip.com Group, the country’s largest online travel services provider, for “monopolistic conduct”. Trip.com – operator of its namesake international platform, China-focused siblings Ctrip and Qunar, and global site Skyscanner – had “abused its dominant market position”, the State Administration for Market Regulation (SAMR) said on Saturday. The market regulator confiscated 1.658 billion yuan in illegal gains and.

The justice threw her liberal colleagues under the bus... for nothing. The post Elena Kagan Thinks The Supreme Court Gets ‘A Bad Rap, Which Is Either A Lie Or Dangerously Naive appeared first on Above the Law .