Governing artificial intelligence: ethical, legal and technical opportunities and challenges
This paper is the introduction to the special issue entitled: ‘Governing artificial intelligence: ethical, legal and technical opportunities and challenges'. Artificial intelligence (AI) increasingly permeates every aspect of our society, from the critical, like urban infrastructure, law enforcement, banking, healthcare...
Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions
Artificial intelligence (AI) research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal...
SLA2: Sparse-Linear Attention with Learnable Routing and QAT
arXiv:2602.12675v1 Announce Type: new Abstract: Sparse-Linear Attention (SLA) combines sparse and linear attention to accelerate diffusion models and has shown strong performance in video generation. However, (i) SLA relies on a heuristic split that assigns computations to the sparse or...
Episode 38: Non-intervention— past, present and future - EJIL: The Podcast!
Announcements Archives - AI Now Institute
BotzoneBench: Scalable LLM Evaluation via Graded AI Anchors
arXiv:2602.13214v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in interactive environments requiring strategic decision-making, yet systematic evaluation of these capabilities remains challenging. Existing benchmarks for LLMs primarily assess static reasoning through isolated tasks and fail to...
NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines
arXiv:2602.13473v1 Announce Type: new Abstract: Although foundation models have demonstrated remarkable success in general domains, the application of these models to electroencephalography (EEG) analysis is constrained by substantial data requirements and high parameterization. These factors incur prohibitive computational costs, thereby...
The Quantization Trap: Breaking Linear Scaling Laws in Multi-Hop Reasoning
arXiv:2602.13595v1 Announce Type: new Abstract: Neural scaling laws provide a predictable recipe for AI advancement: reducing numerical precision should linearly improve computational efficiency and energy profile (E proportional to bits). In this paper, we demonstrate that this scaling law breaks...
Ontology-Guided Neuro-Symbolic Inference: Grounding Language Models with Mathematical Domain Knowledge
arXiv:2602.17826v1 Announce Type: new Abstract: Language models exhibit fundamental limitations -- hallucination, brittleness, and lack of formal grounding -- that are particularly problematic in high-stakes specialist fields requiring verifiable reasoning. I investigate whether formal domain ontologies can enhance language model...
ScaleBITS: Scalable Bitwidth Search for Hardware-Aligned Mixed-Precision LLMs
arXiv:2602.17698v1 Announce Type: cross Abstract: Post-training weight quantization is crucial for reducing the memory and inference cost of large language models (LLMs), yet pushing the average precision below 4 bits remains challenging due to highly non-uniform weight sensitivity and the...
Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models
arXiv:2602.17750v1 Announce Type: cross Abstract: A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain and stress. Machine learning has lead to considerable advances in this field lately....
Modularity is the Bedrock of Natural and Artificial Intelligence
arXiv:2602.18960v1 Announce Type: new Abstract: The remarkable performance of modern AI systems has been driven by unprecedented scales of data, computation, and energy -- far exceeding the resources required by human intelligence. This disparity highlights the need for new guiding...
RUMAD: Reinforcement-Unifying Multi-Agent Debate
arXiv:2602.23864v1 Announce Type: new Abstract: Multi-agent debate (MAD) systems leverage collective intelligence to enhance reasoning capabilities, yet existing approaches struggle to simultaneously optimize accuracy, consensus formation, and computational efficiency. Static topology methods lack adaptability to task complexity variations, while external...
A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities
arXiv:2603.02540v1 Announce Type: new Abstract: Large language models (LLMs) exhibit a unified "general factor" of capability across 10 benchmarks, a finding confirmed by our factor analysis of 156 models, yet they still struggle with simple, trivial tasks for humans. This...
SpatialText: A Pure-Text Cognitive Benchmark for Spatial Understanding in Large Language Models
arXiv:2603.03002v1 Announce Type: new Abstract: Genuine spatial reasoning relies on the capacity to construct and manipulate coherent internal spatial representations, often conceptualized as mental models, rather than merely processing surface linguistic associations. While large language models exhibit advanced capabilities across...
AI4S-SDS: A Neuro-Symbolic Solvent Design System via Sparse MCTS and Differentiable Physics Alignment
arXiv:2603.03686v1 Announce Type: new Abstract: Automated design of chemical formulations is a cornerstone of materials science, yet it requires navigating a high-dimensional combinatorial space involving discrete compositional choices and continuous geometric constraints. Existing Large Language Model (LLM) agents face significant...
Solving an Open Problem in Theoretical Physics using AI-Assisted Discovery
arXiv:2603.04735v1 Announce Type: new Abstract: This paper demonstrates that artificial intelligence can accelerate mathematical discovery by autonomously solving an open problem in theoretical physics. We present a neuro-symbolic system, combining the Gemini Deep Think large language model with a systematic...
On Multi-Step Theorem Prediction via Non-Parametric Structural Priors
arXiv:2603.04852v1 Announce Type: new Abstract: Multi-step theorem prediction is a central challenge in automated reasoning. Existing neural-symbolic approaches rely heavily on supervised parametric models, which exhibit limited generalization to evolving theorem libraries. In this work, we explore training-free theorem prediction...
From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models
arXiv:2603.04828v1 Announce Type: new Abstract: Pre-training data detection for LLMs is essential for addressing copyright concerns and mitigating benchmark contamination. Existing methods mainly focus on the likelihood-based statistical features or heuristic signals before and after fine-tuning, but the former are...
Machine Learning for Complex Systems Dynamics: Detecting Bifurcations in Dynamical Systems with Deep Neural Networks
arXiv:2603.04420v1 Announce Type: new Abstract: Critical transitions are the abrupt shifts between qualitatively different states of a system, and they are crucial to understanding tipping points in complex dynamical systems across ecology, climate science, and biology. Detecting these shifts typically...
On Emergences of Non-Classical Statistical Characteristics in Classical Neural Networks
arXiv:2603.04451v1 Announce Type: new Abstract: Inspired by measurement incompatibility and Bell-family inequalities in quantum mechanics, we propose the Non-Classical Network (NCnet), a simple classical neural architecture that stably exhibits non-classical statistical behaviors under typical and interpretable experimental setups. We find...
Implicit Bias and Loss of Plasticity in Matrix Completion: Depth Promotes Low-Rankness
arXiv:2603.04703v1 Announce Type: new Abstract: We study matrix completion via deep matrix factorization (a.k.a. deep linear neural networks) as a simplified testbed to examine how network depth influences training dynamics. Despite the simplicity and importance of the problem, prior theory...
From Exact Hits to Close Enough: Semantic Caching for LLM Embeddings
arXiv:2603.03301v1 Announce Type: cross Abstract: The rapid adoption of large language models (LLMs) has created demand for faster responses and lower costs. Semantic caching, reusing semantically similar requests via their embeddings, addresses this need but breaks classic cache assumptions and...
Solving adversarial examples requires solving exponential misalignment
arXiv:2603.03507v1 Announce Type: new Abstract: Adversarial attacks - input perturbations imperceptible to humans that fool neural networks - remain both a persistent failure mode in machine learning, and a phenomenon with mysterious origins. To shed light, we define and analyze...
Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective
arXiv:2603.03830v1 Announce Type: new Abstract: Overparameterized machine learning (ML) methods such as neural networks may be prohibitively resource intensive for devices with limited computational capabilities. Hyperdimensional computing (HDC) is an emerging resource efficient and low-complexity ML method that allows hardware...
Neural Paging: Learning Context Management Policies for Turing-Complete Agents
arXiv:2603.02228v1 Announce Type: new Abstract: The proof that Large Language Models (LLMs) augmented with external read-write memory constitute a computationally universal system has established the theoretical foundation for general-purpose agents. However, existing implementations face a critical bottleneck: the finite and...
Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
arXiv:2603.02231v1 Announce Type: new Abstract: Large-scale wave field reconstruction requires precise solutions but faces challenges with computational efficiency and accuracy. The physics-based numerical methods like Finite Element Method (FEM) provide high accuracy but struggle with large-scale or high-frequency problems due...
Attn-QAT: 4-Bit Attention With Quantization-Aware Training
arXiv:2603.00040v1 Announce Type: new Abstract: Achieving reliable 4-bit attention is a prerequisite for end-to-end FP4 computation on emerging FP4-capable GPUs, yet attention remains the main obstacle due to FP4's tiny dynamic range and attention's heavy-tailed activations. This paper presents the...
Wideband Power Amplifier Behavioral Modeling Using an Amplitude Conditioned LSTM
arXiv:2603.00101v1 Announce Type: new Abstract: Wideband power amplifiers exhibit complex nonlinear and memory effects that challenge traditional behavioral modeling approaches. This paper proposes a novel amplitude conditioned long short-term memory (AC-LSTM) network that introduces explicit amplitude-dependent gating to enhance the...
Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare
arXiv:2603.00192v1 Announce Type: new Abstract: In healthcare, predictive models increasingly inform patient-level decisions, yet little attention is paid to the variability in individual risk estimates and its impact on treatment decisions. For overparameterized models, now standard in machine learning, a...