MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution
arXiv:2603.18718v1 Announce Type: new Abstract: Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction, yet most existing systems treat construction, retrieval, and utilization as isolated subroutines. This creates two coupled challenges: strategic blindness on the forward path of...
How Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence
arXiv:2603.18203v1 Announce Type: new Abstract: The dominant paradigms of artificial intelligence were shaped by learning theories from psychology: behaviorism inspired reinforcement learning, cognitivism gave rise to deep learning and memory-augmented architectures, and constructivism influenced curriculum learning and compositional approaches. This...
Engineering Verifiable Modularity in Transformers via Per-Layer Supervision
arXiv:2603.18029v1 Announce Type: new Abstract: Transformers resist surgical control. Ablating an attention head identified as critical for capitalization produces minimal behavioral change because distributed redundancy compensates for damage. This Hydra effect renders interpretability illusory: we may identify components through correlation,...
MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models
arXiv:2603.18256v1 Announce Type: new Abstract: Recent advances in reasoning-based large language models (LLMs) have demonstrated substantial improvements in complex problem-solving tasks. Motivated by these advances, several works have explored the application of reasoning LLMs to drug discovery and molecular design....
Justices to consider rules pardoning omissions by bankrupt debtors
Next week’s argument in Keathley v. Buddy Ayers Construction involves a technical question about bankruptcy procedure – the standards for overlooking the failure of a debtor in bankruptcy to mention […]The postJustices to consider rules pardoning omissions by bankrupt debtorsappeared...
Volume 2026, No. 1 – Wisconsin Law Review – UW–Madison
Contract Law and Civil Justice in Local Courts by Cathy Hwang & Justin Weinstein-Tull; Preempting Drug Price Reform by Shweta Kumar; Lessons Learned? COVID’s Continued Impact on Remote Work Disability Accommodations by D’Andra Millsap Shu; Unbundling AI Openness by Parth...
Transformers Can Learn Rules They've Never Seen: Proof of Computation Beyond Interpolation
arXiv:2603.17019v1 Announce Type: new Abstract: A central question in the LLM debate is whether transformers can infer rules absent from training, or whether apparent generalisation reduces to similarity-based interpolation over observed examples. We test a strong interpolation-only hypothesis in two...
On the Cone Effect and Modality Gap in Medical Vision-Language Embeddings
arXiv:2603.17246v1 Announce Type: new Abstract: Vision-Language Models (VLMs) exhibit a characteristic "cone effect" in which nonlinear encoders map embeddings into highly concentrated regions of the representation space, contributing to cross-modal separation known as the modality gap. While this phenomenon has...
SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction
arXiv:2603.17380v1 Announce Type: new Abstract: Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks:...
Optimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation
arXiv:2603.15960v1 Announce Type: new Abstract: The COVID-19 pandemic has placed immense strain on hospital systems worldwide, leading to critical capacity challenges. This research proposes a two-part framework to optimize hospital capacity through patient relocation strategies. The first component involves developing...
DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learning
arXiv:2603.16459v1 Announce Type: new Abstract: Diffusion large language models (D-LLMs) have emerged as a promising alternative to auto-regressive models due to their iterative refinement capabilities. However, hallucinations remain a critical issue that hinders their reliability. To detect hallucination responses from...
Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning
arXiv:2603.15842v1 Announce Type: new Abstract: Modern machine learning systems increasingly rely on sensitive data, creating significant privacy, security, and regulatory risks that existing privacy-preserving machine learning (ppML) techniques, such as Differential Privacy (DP) and Homomorphic Encryption (HE), address only at...
Auto Researching, not hyperparameter tuning: Convergence Analysis of 10,000 Experiments
arXiv:2603.15916v1 Announce Type: new Abstract: When LLM agents autonomously design ML experiments, do they perform genuine architecture search -- or do they default to hyperparameter tuning within a narrow region of the design space? We answer this question by analyzing...
Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systems
arXiv:2603.15927v1 Announce Type: new Abstract: We propose a data-driven framework to learn interaction kernels in stochastic multi-agent systems. Our approach aims at identifying the functional form of nonlocal interaction and diffusion terms directly from trajectory data, without any a priori...
Collaborative Temporal Feature Generation via Critic-Free Reinforcement Learning for Cross-User Sensor-Based Activity Recognition
arXiv:2603.16043v1 Announce Type: new Abstract: Human Activity Recognition using wearable inertial sensors is foundational to healthcare monitoring, fitness analytics, and context-aware computing, yet its deployment is hindered by cross-user variability arising from heterogeneous physiological traits, motor habits, and sensor placements....
The AI Fiction Paradox
arXiv:2603.13545v1 Announce Type: new Abstract: AI development has a fiction dependency problem: models are built on massive corpora of modern fiction and desperately need more of it, yet they struggle to generate it. I term this the AI-Fiction Paradox and...
TheraAgent: Multi-Agent Framework with Self-Evolving Memory and Evidence-Calibrated Reasoning for PET Theranostics
arXiv:2603.13676v1 Announce Type: new Abstract: PET theranostics is transforming precision oncology, yet treatment response varies substantially; many patients receiving 177Lu-PSMA radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC) fail to respond, demanding reliable pre-therapy prediction. While LLM-based agents have...
How Transformers Reject Wrong Answers: Rotational Dynamics of Factual Constraint Processing
arXiv:2603.13259v1 Announce Type: new Abstract: When a language model is fed a wrong answer, what happens inside the network? Current understanding treats truthfulness as a static property of individual-layer representations-a direction to be probed, a feature to be extracted. Less...
Benchmarking Large Language Models on Reference Extraction and Parsing in the Social Sciences and Humanities
arXiv:2603.13651v1 Announce Type: new Abstract: Bibliographic reference extraction and parsing are foundational for citation indexing, linking, and downstream scholarly knowledge-graph construction. However, most established evaluations focus on clean, English, end-of-document bibliographies, and therefore underrepresent the Social Sciences and Humanities (SSH),...
A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning
arXiv:2603.13293v1 Announce Type: new Abstract: Accurate cardiovascular risk prediction is crucial for preventive healthcare; however, the development of robust Artificial Intelligence (AI) models is hindered by the fragmentation of clinical data across institutions due to stringent privacy regulations. This paper...
Feature-level Interaction Explanations in Multimodal Transformers
arXiv:2603.13326v1 Announce Type: new Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision. Most existing multimodal explainable AI (MXAI) methods extend unimodal saliency to multimodal backbones, highlighting important tokens or patches within each modality,...
SCOTUStoday: Trump v. the Fed
Six years ago today, the court announced that it was postponing its March argument session in response to the COVID-19 pandemic. The press release noted that its “postponement of argument […]The postSCOTUStoday: Trump v. the Fedappeared first onSCOTUSblog.
Announcement of opinions for Friday, March 20
On Friday, March 20, we will be live blogging as the court potentially releases opinions in one or more argued cases from the current term. Click here for a list […]The postAnnouncement of opinions for Friday, March 20appeared first onSCOTUSblog.
Training Is Everything: Artificial Intelligence, Copyright, and Fair Training
To learn how to behave, the current revolutionary generation of AIs must be trained on vast quantities of published images, written works, and sounds, many of which fall within the core subject matter of copyright law. To some, the use...
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...
Measuring AI Agents' Progress on Multi-Step Cyber Attack Scenarios
arXiv:2603.11214v1 Announce Type: new Abstract: We evaluate the autonomous cyber-attack capabilities of frontier AI models on two purpose-built cyber ranges-a 32-step corporate network attack and a 7-step industrial control system attack-that require chaining heterogeneous capabilities across extended action sequences. By...
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...
Heavy-Tailed Principle Component Analysis
arXiv:2603.11308v1 Announce Type: new Abstract: Principal Component Analysis (PCA) is a cornerstone of dimensionality reduction, yet its classical formulation relies critically on second-order moments and is therefore fragile in the presence of heavy-tailed data and impulsive noise. While numerous robust...
Deep Learning Network-Temporal Models For Traffic Prediction
arXiv:2603.11475v1 Announce Type: new Abstract: Time series analysis is critical for emerging net- work intelligent control and management functions. However, existing statistical-based and shallow machine learning models have shown limited prediction capabilities on multivariate time series. The intricate topological interdependency...
Nurture-First Agent Development: Building Domain-Expert AI Agents Through Conversational Knowledge Crystallization
arXiv:2603.10808v1 Announce Type: new Abstract: The emergence of large language model (LLM)-based agent frameworks has shifted the primary challenge in building domain-expert AI agents from raw capability to effective encoding of domain expertise. Two dominant paradigms -- code-first development, which...