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.
Nintendo sues to prevent Trump from dodging full tariff refunds
Nintendo may face pressure to share refunds with gamers who helped pay tariffs.
Talk Freely, Execute Strictly: Schema-Gated Agentic AI for Flexible and Reproducible Scientific Workflows
arXiv:2603.06394v1 Announce Type: new Abstract: Large language models (LLMs) can now translate a researcher's plain-language goal into executable computation, yet scientific workflows demand determinism, provenance, and governance that are difficult to guarantee when an LLM decides what runs. Semi-structured interviews...
Agentic LLM Planning via Step-Wise PDDL Simulation: An Empirical Characterisation
arXiv:2603.06064v1 Announce Type: new Abstract: Task planning, the problem of sequencing actions to reach a goal from an initial state, is a core capability requirement for autonomous robotic systems. Whether large language models (LLMs) can serve as viable planners alongside...
Omni-C: Compressing Heterogeneous Modalities into a Single Dense Encoder
arXiv:2603.05528v1 Announce Type: cross Abstract: Recent multimodal systems often rely on separate expert modality encoders which cause linearly scaling complexity and computational overhead with added modalities. While unified Omni-models address this via Mixture-of-Expert (MoE) architectures with specialized experts and routing,...
SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detection
arXiv:2603.05689v1 Announce Type: cross Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing tasks, yet their application in hardware security verification remains limited due to scarcity of publicly available hardware description language (HDL) datasets. This knowledge...
Longitudinal Lesion Inpainting in Brain MRI via 3D Region Aware Diffusion
arXiv:2603.05693v1 Announce Type: cross Abstract: Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines. While deep generative models have shown promise in inpainting these lesions, most existing methods operate cross-sectionally or lack...
FreeTxt-Vi: A Benchmarked Vietnamese-English Toolkit for Segmentation, Sentiment, and Summarisation
arXiv:2603.05690v1 Announce Type: new Abstract: FreeTxt-Vi is a free and open source web based toolkit for creating and analysing bilingual Vietnamese English text collections. Positioned at the intersection of corpus linguistics and natural language processing NLP it enables users to...
Let's Talk, Not Type: An Oral-First Multi-Agent Architecture for Guaran\'i
arXiv:2603.05743v1 Announce Type: new Abstract: Although artificial intelligence (AI) and Human-Computer Interaction (HCI) systems are often presented as universal solutions, their design remains predominantly text-first, underserving primarily oral languages and indigenous communities. This position paper uses Guaran\'i, an official and...
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...
Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation
arXiv:2603.05881v1 Announce Type: new Abstract: Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing methods are predominantly answer-first, producing confidence only after generating an answer, which measure the correctness of a specific response and limits practical usability....
ViewFusion: Structured Spatial Thinking Chains for Multi-View Reasoning
arXiv:2603.06024v1 Announce Type: new Abstract: Multi-view spatial reasoning remains difficult for current vision-language models. Even when multiple viewpoints are available, models often underutilize cross-view relations and instead rely on single-image shortcuts, leading to fragile performance on viewpoint transformation and occlusion-sensitive...
CRIMSON: A Clinically-Grounded LLM-Based Metric for Generative Radiology Report Evaluation
arXiv:2603.06183v1 Announce Type: new Abstract: We introduce CRIMSON, a clinically grounded evaluation framework for chest X-ray report generation that assesses reports based on diagnostic correctness, contextual relevance, and patient safety. Unlike prior metrics, CRIMSON incorporates full clinical context, including patient...
From Prompting to Preference Optimization: A Comparative Study of LLM-based Automated Essay Scoring
arXiv:2603.06424v1 Announce Type: new Abstract: Large language models (LLMs) have recently reshaped Automated Essay Scoring (AES), yet prior studies typically examine individual techniques in isolation, limiting understanding of their relative merits for English as a Second Language (L2) writing. To...
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,...
Identifying Adversary Characteristics from an Observed Attack
arXiv:2603.05625v1 Announce Type: new Abstract: When used in automated decision-making systems, machine learning (ML) models are vulnerable to data-manipulation attacks. Some defense mechanisms (e.g., adversarial regularization) directly affect the ML models while others (e.g., anomaly detection) act within the broader...
Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy
arXiv:2603.05719v1 Announce Type: new Abstract: Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this...
Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis
arXiv:2603.05917v1 Announce Type: new Abstract: Stock market prediction presents considerable challenges for investors, financial institutions, and policymakers operating in complex market environments characterized by noise, non-stationarity, and behavioral dynamics. Traditional forecasting methods often fail to capture the intricate patterns and...
Design Experiments to Compare Multi-armed Bandit Algorithms
arXiv:2603.05919v1 Announce Type: new Abstract: Online platforms routinely compare multi-armed bandit algorithms, such as UCB and Thompson Sampling, to select the best-performing policy. Unlike standard A/B tests for static treatments, each run of a bandit algorithm over $T$ users produces...
Weak-SIGReg: Covariance Regularization for Stable Deep Learning
arXiv:2603.05924v1 Announce Type: new Abstract: Modern neural network optimization relies heavily on architectural priorssuch as Batch Normalization and Residual connectionsto stabilize training dynamics. Without these, or in low-data regimes with aggressive augmentation, low-bias architectures like Vision Transformers (ViTs) often suffer...
FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring
arXiv:2603.06224v1 Announce Type: new Abstract: Wearable sensors with local data processing can detect health threats early, enhance documentation, and support personalized therapy. In the context of spinal cord injury (SCI), which involves risks such as pressure injuries and blood pressure...
Student Organizations
Vanderbilt law students are active, public-minded, and come from a variety of backgrounds - all qualities reflected by a wide variety of thriving student organizations at the law school. Even with little free time, most students find it worthwhile to...
About the Annual Review of Criminal Procedure
Ethics Guidelines for Trustworthy AI
Artificial intelligence (AI) is one of many digital technologies currently under development.1 In recent years, it is having increasing repercussions in the field of law. These repercussions go beyond the traditional effect of an economic and industrial evolution. Indeed, the...
Volume 110 - Issue 2 - Minnesota Law Review
Undue Computational Experimentation: Can In Silico Experiments Allows Genus Claims to Survive?
U.S. courts have, time and again, struck down genus claims for undue experimentation. The most recent blow came last year in Amgen v. Sanofi, when the Supreme Court affirmed the lower court’s ruling that Amgen’s patent on antibodies with a...
Undergraduate Research at Vanderbilt
Upcoming Events MORE » Recent News Louisiana v. Callais and the Future of the Voting Rights Act Vanderbilt Kennedy Center announces 2025–26 Nicholas Hobbs Discovery Award recipients Vanderbilt engineers debut breakthrough wearable that reduces body armor burden Innovative drug delivery...
Implementing User Rights for Research in the Field of Artificial Intelligence: A Call for International Action
BETTING ON THE FUTURE: DISCUSSING PATHS FORWARD FOR MINNESOTA TO LEGALIZE SPORTS BETTING - Minnesota Law Review
By Benjamin Albert Halevy, Volume 108 Staff Member From pull-tab vending machines at bars to tribe-owned casinos sporting slot machines and blackjack tables, Minnesota is no stranger to gambling within its borders. Yet, sports gambling, the fastest growing sector of...
Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance
The utilization of artificial intelligence (AI) applications has experienced tremendous growth in recent years, bringing forth numerous benefits and conveniences. However, this expansion has also provoked ethical concerns, such as privacy breaches, algorithmic discrimination, security and reliability issues, transparency, and...