Detection of Illicit Content on Online Marketplaces using Large Language Models
arXiv:2603.04707v1 Announce Type: new Abstract: Online marketplaces, while revolutionizing global commerce, have inadvertently facilitated the proliferation of illicit activities, including drug trafficking, counterfeit sales, and cybercrimes. Traditional content moderation methods such as manual reviews and rule-based automated systems struggle with...
Can LLMs Capture Expert Uncertainty? A Comparative Analysis of Value Alignment in Ethnographic Qualitative Research
arXiv:2603.04897v1 Announce Type: new Abstract: Qualitative analysis of open-ended interviews plays a central role in ethnographic and economic research by uncovering individuals' values, motivations, and culturally embedded financial behaviors. While large language models (LLMs) offer promising support for automating and...
Agent Memory Below the Prompt: Persistent Q4 KV Cache for Multi-Agent LLM Inference on Edge Devices
arXiv:2603.04428v1 Announce Type: new Abstract: Multi-agent LLM systems on edge devices face a memory management problem: device RAM is too small to hold every agent's KV cache simultaneously. On Apple M4 Pro with 10.2 GB of cache budget, only 3...
Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data
arXiv:2603.04683v1 Announce Type: new Abstract: Accurate estimation of forest biomass is crucial for monitoring carbon sequestration and informing climate change mitigation strategies. Existing methods often rely on allometric models, which estimate individual tree biomass by relating it to measurable biophysical...
Fine-Tuning and Evaluating Conversational AI for Agricultural Advisory
arXiv:2603.03294v1 Announce Type: cross Abstract: Large Language Models show promise for agricultural advisory, yet vanilla models exhibit unsupported recommendations, generic advice lacking specific, actionable detail, and communication styles misaligned with smallholder farmer needs. In high stakes agricultural contexts, where recommendation...
PlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents
arXiv:2603.03296v1 Announce Type: cross Abstract: Long-term memory is essential for large language model (LLM) agents operating in complex environments, yet existing memory designs are either task-specific and non-transferable, or task-agnostic but less effective due to low task-relevance and context explosion...
Old Habits Die Hard: How Conversational History Geometrically Traps LLMs
arXiv:2603.03308v1 Announce Type: cross Abstract: How does the conversational past of large language models (LLMs) influence their future performance? Recent work suggests that LLMs are affected by their conversational history in unexpected ways. For instance, hallucinations in prior interactions may...
Believe Your Model: Distribution-Guided Confidence Calibration
arXiv:2603.03872v1 Announce Type: new Abstract: Large Reasoning Models have demonstrated remarkable performance with the advancement of test-time scaling techniques, which enhances prediction accuracy by generating multiple candidate responses and selecting the most reliable answer. While prior work has analyzed that...
Thermodynamic Regulation of Finite-Time Gibbs Training in Energy-Based Models: A Restricted Boltzmann Machine Study
arXiv:2603.02525v1 Announce Type: new Abstract: Restricted Boltzmann Machines (RBMs) are typically trained using finite-length Gibbs chains under a fixed sampling temperature. This practice implicitly assumes that the stochastic regime remains valid as the energy landscape evolves during learning. We argue...
How Large Language Models Get Stuck: Early structure with persistent errors
arXiv:2603.00359v1 Announce Type: new Abstract: Linguistic insights may help make Large Language Model (LLM) training more efficient. We trained Meta's OPT model on the 100M word BabyLM dataset, and evaluated it on the BLiMP benchmark, which consists of 67 classes,...
CARE: Confounder-Aware Aggregation for Reliable LLM Evaluation
arXiv:2603.00039v1 Announce Type: new Abstract: LLM-as-a-judge ensembles are the standard paradigm for scalable evaluation, but their aggregation mechanisms suffer from a fundamental flaw: they implicitly assume that judges provide independent estimates of true quality. However, in practice, LLM judges exhibit...
MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
arXiv:2602.23798v1 Announce Type: new Abstract: Machine unlearning for large language models often faces a privacy dilemma in which strict constraints prohibit sharing either the server's parameters or the client's forget set. To address this dual non-disclosure constraint, we propose MPU,...
Court sides with parents in dispute over California policies on transgender students
The Supreme Court on Monday night granted a request from a group of California parents to reinstate a ruling by a federal district court that prohibits schools in that state […]The postCourt sides with parents in dispute over California policies...
Supreme Court skeptical of law banning drug users from possessing firearms
The Supreme Court on Monday was skeptical that the indictment of a Texas man on charges that he violated a federal law prohibiting the possession of a gun by the […]The postSupreme Court skeptical of law banning drug users from...
Trump FCC's equal-time crackdown doesn't apply equally—or at all—to talk radio
FCC Chairman Brendan Carr's unequal enforcement of the equal-time rule.
Right Diagnosis, Wrong Cure: Reconceptualizing the Commerce Clause Basis for the Federal Prohibition on Felon Firearm Possession
Introduction Jonathan Adler recently posted the provocative piece: “Is the Federal Prohibition on Felon Firearm Possession Constitutional?”[1] Although Second Amendment challenges are all the rage, Adler instead asks about Congress’s commerce power. This Essay takes up Adler’s challenge to reconceptualize...
Physics-based phenomenological characterization of cross-modal bias in multimodal models
arXiv:2602.20624v1 Announce Type: new Abstract: The term 'algorithmic fairness' is used to evaluate whether AI models operate fairly in both comparative (where fairness is understood as formal equality, such as "treat like cases as like") and non-comparative (where unfairness arises...
Language Models Exhibit Inconsistent Biases Towards Algorithmic Agents and Human Experts
arXiv:2602.22070v1 Announce Type: new Abstract: Large language models are increasingly used in decision-making tasks that require them to process information from a variety of sources, including both human experts and other algorithmic agents. How do LLMs weigh the information provided...
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.
How Do Latent Reasoning Methods Perform Under Weak and Strong Supervision?
arXiv:2602.22441v1 Announce Type: new Abstract: Latent reasoning has been recently proposed as a reasoning paradigm and performs multi-step reasoning through generating steps in the latent space instead of the textual space. This paradigm enables reasoning beyond discrete language tokens by...
The AI Research Assistant: Promise, Peril, and a Proof of Concept
arXiv:2602.22842v1 Announce Type: new Abstract: Can artificial intelligence truly contribute to creative mathematical research, or does it merely automate routine calculations while introducing risks of error? We provide empirical evidence through a detailed case study: the discovery of novel error...
Waging the Battle for Society’s Soul: The Constitutionality of Juvenile Transfer Legislation in the Wake of Jones v. Mississippi lawreview - Minnesota Law Review
By LOGAN KNUTSON. Full Text. Trying juvenile defendants as adults is a cruel, yet enduring practice in U.S. criminal law. If convicted, these youthful offenders face brutal conditions in adult prison and a lifelong stigma. Although these devastating consequences of...
The Crisis in U.S. Cancer Care: Law, Markets, and Privatization lawreview - Minnesota Law Review
By DANIEL G. AARON. Full Text. Cancer is surging among youth and young adults in the United States, yet, instead of public regulation addressing its root causes, we have outsourced the management of cancer to the private sector. A suite...
Regulatory History and Judicial Review lawreview - Minnesota Law Review
By TODD PHILLIPS & ANTHONY MOFFA. Full Text. The Administrative Procedure Act (APA) requires federal agencies to simply "incorporate in the rules adopted a concise general statement of their basis and purpose" after they receive comments from the public, and...
ESG Investing Under Scrutiny: Legal and Regulatory Developments in 2026
ESG investing faces both increased regulatory support in some jurisdictions and political backlash in others, creating a complex compliance landscape.
Deep Sequence Modeling with Quantum Dynamics: Language as a Wave Function
arXiv:2602.22255v1 Announce Type: new Abstract: We introduce a sequence modeling framework in which the latent state is a complex-valued wave function evolving on a finite-dimensional Hilbert space under a learned, time-dependent Hamiltonian. Unlike standard recurrent architectures that rely on gating...
AutoQRA: Joint Optimization of Mixed-Precision Quantization and Low-rank Adapters for Efficient LLM Fine-Tuning
arXiv:2602.22268v1 Announce Type: new Abstract: Quantization followed by parameter-efficient fine-tuning has emerged as a promising paradigm for downstream adaptation under tight GPU memory constraints. However, this sequential pipeline fails to leverage the intricate interaction between quantization bit-width and LoRA rank....
CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learning
arXiv:2602.22269v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training without sharing raw data. However, shared local model updates remain vulnerable to inference and poisoning attacks. Secure aggregation schemes have been proposed to mitigate these attacks. In this...
On the Structural Non-Preservation of Epistemic Behaviour under Policy Transformation
arXiv:2602.21424v1 Announce Type: new Abstract: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context. We formalise such information-conditioned interaction patterns as behavioural dependency: variation in action selection with...