GPT4o-Receipt: A Dataset and Human Study for AI-Generated Document Forensics
arXiv:2603.11442v1 Announce Type: new Abstract: Can humans detect AI-generated financial documents better than machines? We present GPT4o-Receipt, a benchmark of 1,235 receipt images pairing GPT-4o-generated receipts with authentic ones from established datasets, evaluated by five state-of-the-art multimodal LLMs and a...
Counterweights and Complementarities: The Convergence of AI and Blockchain Powering a Decentralized Future
arXiv:2603.11299v1 Announce Type: new Abstract: This editorial addresses the critical intersection of artificial intelligence (AI) and blockchain technologies, highlighting their contrasting tendencies toward centralization and decentralization, respectively. While AI, particularly with the rise of large language models (LLMs), exhibits a...
AI Knows What's Wrong But Cannot Fix It: Helicoid Dynamics in Frontier LLMs Under High-Stakes Decisions
arXiv:2603.11559v1 Announce Type: new Abstract: Large language models perform reliably when their outputs can be checked: solving equations, writing code, retrieving facts. They perform differently when checking is impossible, as when a clinician chooses an irreversible treatment on incomplete data,...
Task-Conditioned Routing Signatures in Sparse Mixture-of-Experts Transformers
arXiv:2603.11114v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models through conditional computation, yet the routing mechanisms responsible for expert selection remain poorly understood. In this work, we introduce routing signatures, a vector representation...
A Learning-Based Superposition Operator for Non-Renewal Arrival Processes in Queueing Networks
arXiv:2603.11118v1 Announce Type: new Abstract: The superposition of arrival processes is a fundamental yet analytically intractable operation in queueing networks when inputs are general non-renewal streams. Classical methods either reduce merged flows to renewal surrogates, rely on computationally prohibitive Markovian...
Quantifying Hallucinations in Language Language Models on Medical Textbooks
arXiv:2603.09986v1 Announce Type: cross Abstract: Hallucinations, the tendency for large language models to provide responses with factually incorrect and unsupported claims, is a serious problem within natural language processing for which we do not yet have an effective solution to...
SiMPO: Measure Matching for Online Diffusion Reinforcement Learning
arXiv:2603.10250v1 Announce Type: new Abstract: A commonly used family of RL algorithms for diffusion policies conducts softmax reweighting over the behavior policy, which usually induces an over-greedy policy and fails to leverage feedback from negative samples. In this work, we...
GSVD for Geometry-Grounded Dataset Comparison: An Alignment Angle Is All You Need
arXiv:2603.10283v1 Announce Type: new Abstract: Geometry-grounded learning asks models to respect structure in the problem domain rather than treating observations as arbitrary vectors. Motivated by this view, we revisit a classical but underused primitive for comparing datasets: linear relations between...
What crackdown? Trump's EPA enforcement claims don't pass sniff test.
75% of the criminal cases closed last fiscal year originated before Trump took office.
United States v. Johnson
Drug detection dogs are critical tools in the fight against drug trafficking. However, law enforcement canines are imperfect: They sometimes incorrectly alert when performing...The post<em>United States v. Johnson</em>appeared first onHarvard Law Review.
Abundant Intelligence and Deficient Demand: A Macro-Financial Stress Test of Rapid AI Adoption
arXiv:2603.09209v1 Announce Type: new Abstract: We formalize a macro-financial stress test for rapid AI adoption. Rather than a productivity bust or existential risk, we identify a distribution-and-contract mismatch: AI-generated abundance coexists with demand deficiency because economic institutions are anchored to...
PrivPRISM: Automatically Detecting Discrepancies Between Google Play Data Safety Declarations and Developer Privacy Policies
arXiv:2603.09214v1 Announce Type: new Abstract: End-users seldom read verbose privacy policies, leading app stores like Google Play to mandate simplified data safety declarations as a user-friendly alternative. However, these self-declared disclosures often contradict the full privacy policies, deceiving users about...
Interpretable Markov-Based Spatiotemporal Risk Surfaces for Missing-Child Search Planning with Reinforcement Learning and LLM-Based Quality Assurance
arXiv:2603.08933v1 Announce Type: new Abstract: The first 72 hours of a missing-child investigation are critical for successful recovery. However, law enforcement agencies often face fragmented, unstructured data and a lack of dynamic, geospatial predictive tools. Our system, Guardian, provides an...
The $qs$ Inequality: Quantifying the Double Penalty of Mixture-of-Experts at Inference
arXiv:2603.08960v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models deliver high quality at low training FLOPs, but this efficiency often vanishes at inference. We identify a double penalty that structurally disadvantages MoE architectures during decoding: first, expert routing fragments microbatches and...
Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation
arXiv:2603.09053v1 Announce Type: new Abstract: Simulation-to-decision learning enables safe policy training in digital environments without risking real-world deployment, and has become essential in mission-critical domains such as supply chains and industrial systems. However, simulators learned from noisy or biased real-world...
The Radio-Frequency Transformer for Signal Separation
arXiv:2603.09201v1 Announce Type: new Abstract: We study a problem of signal separation: estimating a signal of interest (SOI) contaminated by an unknown non-Gaussian background/interference. Given the training data consisting of examples of SOI and interference, we show how to build...
Language Shapes Mental Health Evaluations in Large Language Models
arXiv:2603.06910v1 Announce Type: new Abstract: This study investigates whether large language models (LLMs) exhibit cross-linguistic differences in mental health evaluations. Focusing on Chinese and English, we examine two widely used models, GPT-4o and Qwen3, to assess whether prompt language systematically...
Dual-Metric Evaluation of Social Bias in Large Language Models: Evidence from an Underrepresented Nepali Cultural Context
arXiv:2603.07792v1 Announce Type: new Abstract: Large language models (LLMs) increasingly influence global digital ecosystems, yet their potential to perpetuate social and cultural biases remains poorly understood in underrepresented contexts. This study presents a systematic analysis of representational biases in seven...
In birthright citizenship case, Justice Department urges court to treat an old concept in a new way
Immigration Matters is a recurring series by César Cuauhtémoc García Hernández that analyzes the court’s immigration docket, highlighting emerging legal questions about new policy and enforcement practices. President Donald Trump’s […]The postIn birthright citizenship case, Justice Department urges court to...
SCOTUStoday for Monday, March 9
Just 22% of U.S. registered voters have “a great deal” (7%) or “quite a bit” (15%) of confidence in the Supreme Court, according to a new NBC News poll shared […]The postSCOTUStoday for Monday, March 9appeared first onSCOTUSblog.
ROSE: Reordered SparseGPT for More Accurate One-Shot Large Language Models Pruning
arXiv:2603.05878v1 Announce Type: new Abstract: Pruning is widely recognized as an effective method for reducing the parameters of large language models (LLMs), potentially leading to more efficient deployment and inference. One classic and prominent path of LLM one-shot pruning is...
Aligning the True Semantics: Constrained Decoupling and Distribution Sampling for Cross-Modal Alignment
arXiv:2603.05566v1 Announce Type: new Abstract: Cross-modal alignment is a crucial task in multimodal learning aimed at achieving semantic consistency between vision and language. This requires that image-text pairs exhibit similar semantics. Traditional algorithms pursue embedding consistency to achieve semantic consistency,...
Exempt but Not Immune: Why the Section 501(c)(3) Tax Exemption Amounts to Federal Financial Assistance and Demands that Private Schools Comply with Title IX lawreview - Minnesota Law Review
By ELLEN BART. Full Text. Title IX of the Education Amendments Act of 1972 (Title IX) prohibits discrimination on the basis of sex in education programs and activities that receive federal financial assistance and ensures that federal funds are not...
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Wisconsin Law Review’s 2025 Symposium
The Wisconsin Law Review presents: The Shadow Carceral State Registration available here.Date and Time Friday, September 26 9:00am – 5:30pm CDT Location Madison Museum of Contemporary Art 227 State Street Madison, WI 53703 CLE for this event is pending.Summary On...
Wisconsin Law Review’s 2022 Symposium
Schedule and information for Wisconsin Law Review Symposia.
Russian experience of using digital technologies and legal risks of AI
The aim of the present article is to analyze the Russian experience of using digital technologies in law and legal risks of artificial intelligence (AI). The result of the present research is the author’s conclusion on the necessity of the...