Generating Expressive and Customizable Evals for Timeseries Data Analysis Agents with AgentFuel
arXiv:2603.12483v1 Announce Type: new Abstract: Across many domains (e.g., IoT, observability, telecommunications, cybersecurity), there is an emerging adoption of conversational data analysis agents that enable users to "talk to your data" to extract insights. Such data analysis agents operate on...
Maximum Entropy Exploration Without the Rollouts
arXiv:2603.12325v1 Announce Type: cross Abstract: Efficient exploration remains a central challenge in reinforcement learning, serving as a useful pretraining objective for data collection, particularly when an external reward function is unavailable. A principled formulation of the exploration problem is to...
Long-form RewardBench: Evaluating Reward Models for Long-form Generation
arXiv:2603.12963v1 Announce Type: new Abstract: The widespread adoption of reinforcement learning-based alignment highlights the growing importance of reward models. Various benchmarks have been built to evaluate reward models in various domains and scenarios. However, a significant gap remains in assessing...
Generalist Large Language Models for Molecular Property Prediction: Distilling Knowledge from Specialist Models
arXiv:2603.12344v1 Announce Type: new Abstract: Molecular Property Prediction (MPP) is a central task in drug discovery. While Large Language Models (LLMs) show promise as generalist models for MPP, their current performance remains below the threshold for practical adoption. We propose...
Modal Logical Neural Networks for Financial AI
arXiv:2603.12487v1 Announce Type: new Abstract: The financial industry faces a critical dichotomy in AI adoption: deep learning often delivers strong empirical performance, while symbolic logic offers interpretability and rule adherence expected in regulated settings. We use Modal Logical Neural Networks...
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...
The Unlearning Mirage: A Dynamic Framework for Evaluating LLM Unlearning
arXiv:2603.11266v1 Announce Type: new Abstract: Unlearning in Large Language Models (LLMs) aims to enhance safety, mitigate biases, and comply with legal mandates, such as the right to be forgotten. However, existing unlearning methods are brittle: minor query modifications, such as...
Examining Users' Behavioural Intention to Use OpenClaw Through the Cognition--Affect--Conation Framework
arXiv:2603.11455v1 Announce Type: new Abstract: This study examines users' behavioural intention to use OpenClaw through the Cognition--Affect--Conation (CAC) framework. The research investigates how cognitive perceptions of the system influence affective responses and subsequently shape behavioural intention. Enabling factors include perceived...
LLM-Augmented Digital Twin for Policy Evaluation in Short-Video Platforms
arXiv:2603.11333v1 Announce Type: new Abstract: Short-video platforms are closed-loop, human-in-the-loop ecosystems where platform policy, creator incentives, and user behavior co-evolve. This feedback structure makes counterfactual policy evaluation difficult in production, especially for long-horizon and distributional outcomes. The challenge is amplified...
DT-BEHRT: Disease Trajectory-aware Transformer for Interpretable Patient Representation Learning
arXiv:2603.10180v1 Announce Type: new Abstract: The growing adoption of electronic health record (EHR) systems has provided unprecedented opportunities for predictive modeling to guide clinical decision making. Structured EHRs contain longitudinal observations of patients across hospital visits, where each visit is...
GaLoRA: Parameter-Efficient Graph-Aware LLMs for Node Classification
arXiv:2603.10298v1 Announce Type: new Abstract: The rapid rise of large language models (LLMs) and their ability to capture semantic relationships has led to their adoption in a wide range of applications. Text-attributed graphs (TAGs) are a notable example where LLMs...
Abundant Intelligence and Deficient Demand: A Macro-Financial Stress Test of Rapid AI Adoption
arXiv:2603.09209v1 Announce Type: new Abstract: We formalize a macro-financial stress test for rapid AI adoption. Rather than a productivity bust or existential risk, we identify a distribution-and-contract mismatch: AI-generated abundance coexists with demand deficiency because economic institutions are anchored to...
MASEval: Extending Multi-Agent Evaluation from Models to Systems
arXiv:2603.08835v1 Announce Type: new Abstract: The rapid adoption of LLM-based agentic systems has produced a rich ecosystem of frameworks (smolagents, LangGraph, AutoGen, CAMEL, LlamaIndex, i.a.). Yet existing benchmarks are model-centric: they fix the agentic setup and do not compare other...
Probabilistic Hysteresis Factor Prediction for Electric Vehicle Batteries with Graphite Anodes Containing Silicon
arXiv:2603.09103v1 Announce Type: new Abstract: Batteries with silicon-graphite-based anodes, which offer higher energy density and improved charging performance, introduce pronounced voltage hysteresis, making state-of-charge (SoC) estimation particularly challenging. Existing approaches to modeling hysteresis rely on exhaustive high-fidelity tests or focus...
AI Now Co-ED Amba Kak Gives Remarks Before the UN General Assembly on AI Governance - AI Now Institute
Validation of a Small Language Model for DSM-5 Substance Category Classification in Child Welfare Records
arXiv:2603.06836v1 Announce Type: new Abstract: Background: Recent studies have demonstrated that large language models (LLMs) can perform binary classification tasks on child welfare narratives, detecting the presence or absence of constructs such as substance-related problems, domestic violence, and firearms involvement....
Khatri-Rao Clustering for Data Summarization
arXiv:2603.06602v1 Announce Type: new Abstract: As datasets continue to grow in size and complexity, finding succinct yet accurate data summaries poses a key challenge. Centroid-based clustering, a widely adopted approach to address this challenge, finds informative summaries of datasets in...
One step further with Monte-Carlo sampler to guide diffusion better
arXiv:2603.06685v1 Announce Type: new Abstract: Stochastic differential equation (SDE)-based generative models have achieved substantial progress in conditional generation via training-free differentiable loss-guided approaches. However, existing methodologies utilizing posterior sam- pling typically confront a substantial estimation error, which results in inaccu-...
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...
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...
Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments
arXiv:2603.06009v1 Announce Type: new Abstract: Plateaus, where an agent's performance stagnates at a suboptimal level, are a common problem in deep on-policy RL. Focusing on PPO due to its widespread adoption, we show that plateaus in certain regimes arise not...
Shaping the future of AI in healthcare through ethics and governance
Abstract The purpose of this research is to identify and evaluate the technical, ethical and regulatory challenges related to the use of Artificial Intelligence (AI) in healthcare. The potential applications of AI in healthcare seem limitless and vary in their...
Major-Questions Lenity lawreview - Minnesota Law Review
By JOEL S. JOHNSON. Full Text. Both the historic rule of lenity and the new major questions doctrine rest on a fundamental commitment to the separation of powers for important policy questions. In light of that shared justification, the logic...
A Legal Stimulus
We need a legal stimulus. Not just a stimulus that is legal, but one that provides legal aid. That is why any further congressional stimulus should allocate additional funds specifically for legal services to individuals who, as a result of...
Curbing Gun Violence Under PLCAA and Bruen: State Attorney General–Driven Solutions to the Surging Epidemic lawreview - Minnesota Law Review
By David Lamb. Full Text. At the same time that the deadly toll of gun violence continues to grow in the U.S., now taking nearly 50,000 lives per year, federal lawmakers and courts have increasingly constrained government authorities’ tools for...
Insurers as Contract Influencers lawreview - Minnesota Law Review
By DAVID A. HOFFMAN & RICK SWEDLOFF. Full Text. Contract boilerplate degrading consumers' litigation options is omnipresent, but a little mysterious. And that's not just because no one reads it. We know that terms mandating arbitration, exculpating liability, requiring individualized...
Volume 2025, No. 4
How Not to Democratize Algorithms by Ngozi Okidegbe; Missing Children Discrimination by Itay Ravid & Tanisha Brown; Justifications for Fair Uses by Pamela Samuelson; Section Three of the Fourteenth Amendment from the Perspective of Section Two of the Fourteenth Amendment...
Artificial Intelligence and Sui Generis Right: A Perspective for Copyright of Ukraine?
This note explores the current state of and perspectives on the legal qualification of artificial intelligence (AI) outputs in Ukrainian copyright. The possible legal protection for AI-generated objects by granting sui generis intellectual property rights will be examined. As will...
The intersection of AI and legal expertise: Transforming knowledge work in the legal profession
This article explores the transformative impact of artificial intelligence on legal knowledge work, examining the evolution from traditional document-centric processes to sophisticated AI-augmented workflows. The article shows the technological foundations of legal AI systems, highlighting the capabilities and limitations of...
Addressing Legal and Contractual Matters in Construction Using Natural Language Processing: A Critical Review
Claims, disputes, and litigations are major legal issues in construction projects, which often result in cost overruns, delays, and adverse working relationships among the contracting parties. Recent advances in natural language processing (NLP) techniques offer great potentials that can process...