EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors
arXiv:2602.21218v1 Announce Type: cross Abstract: High-quality data is essential for modern machine learning, yet many valuable corpora are sensitive and cannot be freely shared. Synthetic data offers a practical substitute for downstream development, and large language models (LLMs) have emerged...
Field-Theoretic Memory for AI Agents: Continuous Dynamics for Context Preservation
arXiv:2602.21220v1 Announce Type: cross Abstract: We present a memory system for AI agents that treats stored information as continuous fields governed by partial differential equations rather than discrete entries in a database. The approach draws from classical field theory: memories...
Task-Aware LoRA Adapter Composition via Similarity Retrieval in Vector Databases
arXiv:2602.21222v1 Announce Type: cross Abstract: Parameter efficient fine tuning methods like LoRA have enabled task specific adaptation of large language models, but efficiently composing multiple specialized adapters for unseen tasks remains challenging. We present a novel framework for dynamic LoRA...
Architecture-Agnostic Curriculum Learning for Document Understanding: Empirical Evidence from Text-Only and Multimodal
arXiv:2602.21225v1 Announce Type: cross Abstract: We investigate whether progressive data scheduling -- a curriculum learning strategy that incrementally increases training data exposure (33\%$\rightarrow$67\%$\rightarrow$100\%) -- yields consistent efficiency gains across architecturally distinct document understanding models. By evaluating BERT (text-only, 110M parameters)...
IslamicLegalBench: Evaluating LLMs Knowledge and Reasoning of Islamic Law Across 1,200 Years of Islamic Pluralist Legal Traditions
arXiv:2602.21226v1 Announce Type: cross Abstract: As millions of Muslims turn to LLMs like GPT, Claude, and DeepSeek for religious guidance, a critical question arises: Can these AI systems reliably reason about Islamic law? We introduce IslamicLegalBench, the first benchmark evaluating...
The Fundamental Right to Education
ARTICLE The Fundamental Right to Education Derek W. Black* New litigation has revived one of the most important questions of constitutional law: Is education a fundamental right? The Court’s previous answers have been disappointing. While the Court has hinted that...
The Discrimination Presumption
ARTICLE The Discrimination Presumption Joseph A. Seiner* Employment discrimination is a fact in our society. Scientific studies continue to show that employer misconduct in the workplace is pervasive. This social science research is further supported by governmental data and litigation...
The New Oral Argument: Justices as Advocates
ARTICLE The New Oral Argument: Justices as Advocates Tonja Jacobi* & Matthew Sag** This Article conducts a comprehensive empirical inquiry of fifty-five years of Supreme Court oral argument, showing that judicial activity has increased dramatically, in terms of words used,...
Transborder Speech
ARTICLE Transborder Speech Ronald J. Krotoszynski, Jr.* In an increasingly globalized marketplace of ideas, First Amendment law and theory must recognize that the freedom of speech does not end at the water’s edge. Simply put, the locus of expressive activity...
Gains, Losses, and Judges: Framing and the Judiciary
ARTICLE Gains, Losses, and Judges: Framing and the Judiciary Jeffrey J. Rachlinski* & Andrew J. Wistrich** Losses hurt more than foregone gains—an asymmetry that psychologists call “loss aversion.” Losses cause more regret than foregone gains, and people struggle harder to...
Corporate Governance in the Age of AI: Board Responsibilities and Best Practices
As AI transforms business operations, corporate boards face new governance challenges requiring updated oversight frameworks and expertise.
Fintech Regulation 2026: Navigating the New Compliance Landscape
The regulatory environment for fintech has evolved dramatically, with new frameworks addressing digital assets, open banking, and AI-driven financial services.
Autonomous Vehicles and Liability: Who Is Responsible When AI Drives?
As autonomous vehicles approach widespread deployment, legal frameworks for determining liability in accidents involving self-driving cars remain uncertain.
Budget-Aware Agentic Routing via Boundary-Guided Training
arXiv:2602.21227v1 Announce Type: cross Abstract: As large language models (LLMs) evolve into autonomous agents that execute long-horizon workflows, invoking a high-capability model at every step becomes economically unsustainable. While model routing is effective for single-turn queries, agentic routing is a...
ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following
arXiv:2602.21228v1 Announce Type: cross Abstract: As applications of large language models (LLMs) become increasingly complex, the demand for robust complex instruction following capabilities is growing accordingly. We argue that a thorough understanding of the instruction itself, especially the latent reasoning...
ACAR: Adaptive Complexity Routing for Multi-Model Ensembles with Auditable Decision Traces
arXiv:2602.21231v1 Announce Type: cross Abstract: We present ACAR (Adaptive Complexity and Attribution Routing), a measurement framework for studying multi-model orchestration under auditable conditions. ACAR uses self-consistency variance (sigma) computed from N=3 probe samples to route tasks across single-model, two-model, and...
AngelSlim: A more accessible, comprehensive, and efficient toolkit for large model compression
arXiv:2602.21233v1 Announce Type: cross Abstract: This technical report introduces AngelSlim, a comprehensive and versatile toolkit for large model compression developed by the Tencent Hunyuan team. By consolidating cutting-edge algorithms, including quantization, speculative decoding, token pruning, and distillation. AngelSlim provides a...
AgenticTyper: Automated Typing of Legacy Software Projects Using Agentic AI
arXiv:2602.21251v1 Announce Type: cross Abstract: Legacy JavaScript systems lack type safety, making maintenance risky. While TypeScript can help, manually adding types is expensive. Previous automated typing research focuses on type inference but rarely addresses type checking setup, definition generation, bug...
A Systematic Review of Algorithmic Red Teaming Methodologies for Assurance and Security of AI Applications
arXiv:2602.21267v1 Announce Type: cross Abstract: Cybersecurity threats are becoming increasingly sophisticated, making traditional defense mechanisms and manual red teaming approaches insufficient for modern organizations. While red teaming has long been recognized as an effective method to identify vulnerabilities by simulating...
Group Orthogonalized Policy Optimization:Group Policy Optimization as Orthogonal Projection in Hilbert Space
arXiv:2602.21269v1 Announce Type: cross Abstract: We present Group Orthogonalized Policy Optimization (GOPO), a new alignment algorithm for large language models derived from the geometry of Hilbert function spaces. Instead of optimizing on the probability simplex and inheriting the exponential curvature...
Scaling View Synthesis Transformers
arXiv:2602.21341v1 Announce Type: cross Abstract: Geometry-free view synthesis transformers have recently achieved state-of-the-art performance in Novel View Synthesis (NVS), outperforming traditional approaches that rely on explicit geometry modeling. Yet the factors governing their scaling with compute remain unclear. We present...
Alignment-Weighted DPO: A principled reasoning approach to improve safety alignment
arXiv:2602.21346v1 Announce Type: cross Abstract: Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) have improved the safety of large language models (LLMs). However, these LLMs remain vulnerable...
Towards Controllable Video Synthesis of Routine and Rare OR Events
arXiv:2602.21365v1 Announce Type: cross Abstract: Purpose: Curating large-scale datasets of operating room (OR) workflow, encompassing rare, safety-critical, or atypical events, remains operationally and ethically challenging. This data bottleneck complicates the development of ambient intelligence for detecting, understanding, and mitigating rare...
Black-Box Reliability Certification for AI Agents via Self-Consistency Sampling and Conformal Calibration
arXiv:2602.21368v1 Announce Type: cross Abstract: Given a black-box AI system and a task, at what confidence level can a practitioner trust the system's output? We answer with a reliability level -- a single number per system-task pair, derived from self-consistency...
MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation
arXiv:2602.21379v1 Announce Type: cross Abstract: We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and code. Through targeted adaptation, this model family achieves state-of-the-art results on Catalan- and Spanish-specific tasks,...
FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning
arXiv:2602.21399v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across multiple clients without sharing their private data. However, data heterogeneity across clients leads to client drift, which degrades the overall generalization performance of the model. This effect...
Graph Your Way to Inspiration: Integrating Co-Author Graphs with Retrieval-Augmented Generation for Large Language Model Based Scientific Idea Generation
arXiv:2602.22215v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate potential in the field of scientific idea generation. However, the generated results often lack controllable academic context and traceable inspiration pathways. To bridge this gap, this paper proposes a scientific...
FIRE: A Comprehensive Benchmark for Financial Intelligence and Reasoning Evaluation
arXiv:2602.22273v1 Announce Type: new Abstract: We introduce FIRE, a comprehensive benchmark designed to evaluate both the theoretical financial knowledge of LLMs and their ability to handle practical business scenarios. For theoretical assessment, we curate a diverse set of examination questions...
Vibe Researching as Wolf Coming: Can AI Agents with Skills Replace or Augment Social Scientists?
arXiv:2602.22401v1 Announce Type: new Abstract: AI agents -- systems that execute multi-step reasoning workflows with persistent state, tool access, and specialist skills -- represent a qualitative shift from prior automation technologies in social science. Unlike chatbots that respond to isolated...
Towards Autonomous Memory Agents
arXiv:2602.22406v1 Announce Type: new Abstract: Recent memory agents improve LLMs by extracting experiences and conversation history into an external storage. This enables low-overhead context assembly and online memory update without expensive LLM training. However, existing solutions remain passive and reactive;...