Tax Law

arXiv:2602.12449v1 Announce Type: new Abstract: Learning Gibbs distributions using only sufficient statistics has long been recognized as a computationally hard problem. On the other hand, computationally efficient algorithms for learning Gibbs distributions rely on access to full sample configurations generated...

arXiv:2602.12468v1 Announce Type: new Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their global, non-causal generation process, but their continuous latent dynamics make discrete constraints -- e.g., the output should be a JSON file that...

arXiv:2602.12482v1 Announce Type: new Abstract: We give a geometric construction of neural networks that separate disjoint compact subsets of $\Bbb R^n$, and use it to obtain a constructive universal approximation theorem. Specifically, we show that networks with two hidden layers...

arXiv:2602.12506v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision language models (VLMs). While RL-tuned VLMs improve on visual reasoning benchmarks, they...

arXiv:2602.12517v1 Announce Type: new Abstract: The intersection of Mean Field Games (MFGs) and Reinforcement Learning (RL) has fostered a growing family of algorithms designed to solve large-scale multi-agent systems. However, the field currently lacks a standardized evaluation protocol, forcing researchers...

arXiv:2602.12527v1 Announce Type: new Abstract: The Hierarchical Dirichlet Process (HDP) provides a flexible Bayesian nonparametric framework for modeling grouped data with a shared yet unbounded collection of mixture components. While existing applications of the HDP predominantly focus on the Dirichlet-multinomial...

arXiv:2602.12529v1 Announce Type: new Abstract: Reinforcement learning has emerged as a promising paradigm for aligning diffusion and flow-matching models with human preferences, yet practitioners face fragmented codebases, model-specific implementations, and engineering complexity. We introduce Flow-Factory, a unified framework that decouples...

arXiv:2602.12579v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a dominant paradigm for enhancing Large Language Models (LLMs) reasoning, yet its reliance on external verifiers limits its scalability. Recent findings suggest that RLVR primarily functions...

arXiv:2602.12613v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) are pivotal in processing dynamic graphs. However, existing TGNNs primarily target one-time predictions for a given temporal span, whereas many practical applications require continuous predictions, that predictions are issued frequently...

arXiv:2602.12683v1 Announce Type: new Abstract: We reformulate Optimal Transport Conditional Flow Matching (OT-CFM), a class of dynamical generative models, showing that it admits an exact proximal formulation via an extended Brenier potential, without assuming that the target distribution has a...

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This journal seeks papers that address the development of formal or computational models of legal knowledge, reasoning, and decision making. It also includes ...

Osborn Design Works (ODW) designs and develops high-performance websites and apps, leveraging product design, UI/UX design, and marketing design to create business value.

The Center for AI Safety is offering grants for philosophers to pursue research in conceptual AI safety.

The AI Safety, Ethics, and Society Course would be held virtually and be completely free. It would be part-time and we expect that it will require a time commitment of 3-5 hours per week for 10 weeks.

In the aftermath of the “AI boom,” this report examines how the push to integrate AI products everywhere grants AI companies - and the tech oligarchs that run them - power that goes far beyond their deep pockets.

The AI-facilitated intelligence revolution is claimed by some to be setting humanity on a glidepath into utopian futures of nearly effortless satisfaction and frictionless choice. We should beware.

The fourth of our 'AI Safety Breakfasts' event series, featuring Dr. Rumman Chowdhury on algorithmic auditing, "right to repair" AI systems, and the AI Safety and Action Summits.

Over 140 countries have agreed to the introduction of a Global Minimum Tax (GMT), widely regarded as the most significant reform of the international business tax system in a century. While acknowledging that the agreement constitutes a remarkable political and...

FTC claims Apple News suppresses conservatives, cites study by pro-Trump group.