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...
Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning
arXiv:2603.05696v1 Announce Type: cross Abstract: Ptychography is a computational imaging technique widely used for high-resolution materials characterization, but high-quality reconstructions often require the use of regularization functions that largely remain manually designed. We introduce Ptychi-Evolve, an autonomous framework that uses...
The Rise of AI in Weather and Climate Information and its Impact on Global Inequality
arXiv:2603.05710v1 Announce Type: cross Abstract: The rapid adoption of AI in Earth system science promises unprecedented speed and fidelity in the generation of climate information. However, this technological prowess rests on a fragile and unequal foundation: the current trajectory of...
Attention Meets Reachability: Structural Equivalence and Efficiency in Grammar-Constrained LLM Decoding
arXiv:2603.05540v1 Announce Type: new Abstract: We study grammar-constrained decoding (GCD) as a coupling between an autoregressive next-token distribution and a reachability oracle over a pushdown system compiled from a context-free grammar (CFG). We prove an oracle invariance theorem: language-equivalent grammars...
PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models
arXiv:2603.05776v1 Announce Type: new Abstract: Motivation: Patient-generated text contains critical information about patients' lived experiences, social circumstances, and engagement in care, including factors that strongly influence adherence, care coordination, and health equity. However, these patient voice signals are rarely available...
Learning Next Action Predictors from Human-Computer Interaction
arXiv:2603.05923v1 Announce Type: new Abstract: Truly proactive AI systems must anticipate what we will do next. This foresight demands far richer information than the sparse signals we type into our prompts -- it demands reasoning over the entire context of...
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...
LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation
arXiv:2603.06198v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is a framework in which a Generator, such as a Large Language Model (LLM), produces answers by retrieving documents from an external collection using a Retriever. In practice, Generators must integrate evidence...
Transparent AI for Mathematics: Transformer-Based Large Language Models for Mathematical Entity Relationship Extraction with XAI
arXiv:2603.06348v1 Announce Type: new Abstract: Mathematical text understanding is a challenging task due to the presence of specialized entities and complex relationships between them. This study formulates mathematical problem interpretation as a Mathematical Entity Relation Extraction (MERE) task, where operands...
Autocorrelation effects in a stochastic-process model for decision making via time series
arXiv:2603.05559v1 Announce Type: new Abstract: Decision makers exploiting photonic chaotic dynamics obtained by semiconductor lasers provide an ultrafast approach to solving multi-armed bandit problems by using a temporal optical signal as the driving source for sequential decisions. In such systems,...
Improved Scaling Laws via Weak-to-Strong Generalization in Random Feature Ridge Regression
arXiv:2603.05691v1 Announce Type: new Abstract: It is increasingly common in machine learning to use learned models to label data and then employ such data to train more capable models. The phenomenon of weak-to-strong generalization exemplifies the advantage of this two-stage...
First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints
arXiv:2603.05774v1 Announce Type: new Abstract: This paper addresses the distributed stochastic minimax optimization problem subject to stochastic constraints. We propose a novel first-order Softmax-Weighted Switching Gradient method tailored for federated learning. Under full client participation, our algorithm achieves the standard...
Sparse Crosscoders for diffing MoEs and Dense models
arXiv:2603.05805v1 Announce Type: new Abstract: Mixture of Experts (MoE) achieve parameter-efficient scaling through sparse expert routing, yet their internal representations remain poorly understood compared to dense models. We present a systematic comparison of MoE and dense model internals using crosscoders,...
Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning
arXiv:2603.05900v1 Announce Type: new Abstract: Large language models (LLMs) benefit substantially from supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) in reasoning tasks. However, these recipes perform poorly in instruction-based molecular optimization, where each data point typically provides...
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...
Agnostic learning in (almost) optimal time via Gaussian surface area
arXiv:2603.06027v1 Announce Type: new Abstract: The complexity of learning a concept class under Gaussian marginals in the difficult agnostic model is closely related to its $L_1$-approximability by low-degree polynomials. For any concept class with Gaussian surface area at most $\Gamma$,...
Dynamic Momentum Recalibration in Online Gradient Learning
arXiv:2603.06120v1 Announce Type: new Abstract: Stochastic Gradient Descent (SGD) and its momentum variants form the backbone of deep learning optimization, yet the underlying dynamics of their gradient behavior remain insufficiently understood. In this work, we reinterpret gradient updates through the...
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...
DC-Merge: Improving Model Merging with Directional Consistency
arXiv:2603.06242v1 Announce Type: new Abstract: Model merging aims to integrate multiple task-adapted models into a unified model that preserves the knowledge of each task. In this paper, we identify that the key to this knowledge retention lies in maintaining the...
Surveillance Intrusiveness in a Pandemic
Government surveillance capabilities have always been a matter of public concern, but the current pandemic makes the issue especially salient. We set out to discover what Americans think of government surveillance during this crisis. Americans have been inundated with media...
Securitising AI: routine exceptionality and digital governance in the Gulf
Abstract This article examines how Gulf Cooperation Council (GCC) states securitise artificial intelligence (AI) through discourses and infrastructures that fuse modernisation with regime resilience. Drawing on securitisation theory (Buzan et al., 1998; Balzacq, 2011) and critical security studies, it analyses...
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News from the Vanderbilt Law School
A Law and Political Economy of Intellectual Property
Introduction Theories of intellectual property (IP) tend to come in two varieties: “normative” theories that concern themselves with evaluating the extent to which IP rights are (or are not) justified, and “positive” theories that focus on explaining the origins or...
Exacerbating Algorithmic Bias through Fairness Attacks
Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms. Despite this interest, the robustness of those fairness measures with respect to an intentional adversarial attack has...
Academic Calendar
2025-26 Academic Calendar Please note: All times in U.S. Central. EventDate / Time First Registration Appointment Window (all 3Ls)June 16 (YES opens at 12:35 PM) thru June 22 (YES closes at 11:59 PM) Second Registration Appointment Window (all 2Ls/3Ls)June 23...
Online Courts and the Future of Justice
In Online Courts and the Future of Justice, Richard Susskind, the world’s most cited author on the future of legal services, shows how litigation will be transformed by technology and proposes a solution to the global access-to-justice problem. In most...
Practical Consequences in Statutory Interpretation
Modern textualism has long criticized the use of practical, or consequentialist, reasoning when construing statutes. And yet in practice, textualist jurists long have invoked practical consequences arguments to help justify their statutory constructions.The postPractical Consequences in Statutory Interpretationappeared first onHarvard...
The Unpropertied Internet
It has often been said that the internet lacks public property. Unlike the offline world, denizens of cyberspace cannot gather in the digital equivalent of public parks, cannot shame websites by picketing on adjacent cyber-sidewalks, and cannot loiter in online...
The intellectual property road to the knowledge economy: remarks on the readiness of the UAE Copyright Act to drive AI innovation
Copyright law in the United Arab Emirates (UAE) has the capacity to address the challenges associated with artificial intelligence (AI)-generated literary, artistic and scientific works. Under UAE copyright law, AI-generated works may qualify as copyright subject matter despite the non-human...