Deals
Only the best deals on Verge-approved gadgets get the Verge Deals stamp of approval, so if you’re looking for a deal on your next gadget or gift from major retailers like Amazon, Walmart, Best Buy, Target, and more, this is...
Understanding the Regulation of the Use of Artificial Intelligence Under International Law
The development of artificial intelligence (AI) has revolutionized various aspects of human life, from the economic sector to the government system. While it brings significant benefits, AI also poses legal and ethical risks that have not been fully addressed in...
When to Think Fast and Slow? AMOR: Entropy-Based Metacognitive Gate for Dynamic SSM-Attention Switching
arXiv:2602.13215v1 Announce Type: new Abstract: Transformers allocate uniform computation to every position, regardless of difficulty. State Space Models (SSMs) offer efficient alternatives but struggle with precise information retrieval over a long horizon. Inspired by dual-process theories of cognition (Kahneman, 2011),...
VeRA: Verified Reasoning Data Augmentation at Scale
arXiv:2602.13217v1 Announce Type: new Abstract: The main issue with most evaluation schemes today is their "static" nature: the same problems are reused repeatedly, allowing for memorization, format exploitation, and eventual saturation. To measure genuine AI progress, we need evaluation that...
X-Blocks: Linguistic Building Blocks of Natural Language Explanations for Automated Vehicles
arXiv:2602.13248v1 Announce Type: new Abstract: Natural language explanations play a critical role in establishing trust and acceptance of automated vehicles (AVs), yet existing approaches lack systematic frameworks for analysing how humans linguistically construct driving rationales across diverse scenarios. This paper...
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...
Multimodal Consistency-Guided Reference-Free Data Selection for ASR Accent Adaptation
arXiv:2602.13263v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems often degrade on accented speech because acoustic-phonetic and prosodic shifts induce a mismatch to training data, making labeled accent adaptation costly. However, common pseudo-label selection heuristics are largely text-centric (e.g.,...
Chain-of-Thought Reasoning with Large Language Models for Clinical Alzheimer's Disease Assessment and Diagnosis
arXiv:2602.13979v1 Announce Type: new Abstract: Alzheimer's disease (AD) has become a prevalent neurodegenerative disease worldwide. Traditional diagnosis still relies heavily on medical imaging and clinical assessment by physicians, which is often time-consuming and resource-intensive in terms of both human expertise...
Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis
arXiv:2602.15067v1 Announce Type: new Abstract: Gliomas, among the most common primary brain tumors, vary widely in aggressiveness, prognosis, and histology, making treatment challenging due to complex and time-intensive surgical interventions. This study presents an Attention-Gated Recurrent Residual U-Net (R2U-Net) based...
SOMtime the World Ain$'$t Fair: Violating Fairness Using Self-Organizing Maps
arXiv:2602.18201v1 Announce Type: new Abstract: Unsupervised representations are widely assumed to be neutral with respect to sensitive attributes when those attributes are withheld from training. We show that this assumption is false. Using SOMtime, a topology-preserving representation method based on...
AsynDBT: Asynchronous Distributed Bilevel Tuning for efficient In-Context Learning with Large Language Models
arXiv:2602.17694v1 Announce Type: cross Abstract: With the rapid development of large language models (LLMs), an increasing number of applications leverage cloud-based LLM APIs to reduce usage costs. However, since cloud-based models' parameters and gradients are agnostic, users have to manually...
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...
Detection and Classification of Cetacean Echolocation Clicks using Image-based Object Detection Methods applied to Advanced Wavelet-based Transformations
arXiv:2602.17749v1 Announce Type: cross Abstract: A challenge in marine bioacoustic analysis is the detection of animal signals, like calls, whistles and clicks, for behavioral studies. Manual labeling is too time-consuming to process sufficient data to get reasonable results. Thus, an...
Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring
arXiv:2602.17751v1 Announce Type: cross Abstract: Biodiversity loss poses a significant threat to humanity, making wildlife monitoring essential for assessing ecosystem health. Avian species are ideal subjects for this due to their popularity and the ease of identifying them through their...
InfEngine: A Self-Verifying and Self-Optimizing Intelligent Engine for Infrared Radiation Computing
arXiv:2602.18985v1 Announce Type: new Abstract: Infrared radiation computing underpins advances in climate science, remote sensing and spectroscopy but remains constrained by manual workflows. We introduce InfEngine, an autonomous intelligent computational engine designed to drive a paradigm shift from human-led orchestration...
ReHear: Iterative Pseudo-Label Refinement for Semi-Supervised Speech Recognition via Audio Large Language Models
arXiv:2602.18721v1 Announce Type: new Abstract: Semi-supervised learning in automatic speech recognition (ASR) typically relies on pseudo-labeling, which often suffers from confirmation bias and error accumulation due to noisy supervision. To address this limitation, we propose ReHear, a framework for iterative...
PseudoAct: Leveraging Pseudocode Synthesis for Flexible Planning and Action Control in Large Language Model Agents
arXiv:2602.23668v1 Announce Type: new Abstract: Large language model (LLM) agents typically rely on reactive decision-making paradigms such as ReAct, selecting actions conditioned on growing execution histories. While effective for short tasks, these approaches often lead to redundant tool usage, unstable...
ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference
arXiv:2602.23681v1 Announce Type: new Abstract: The paradigm of large language model (LLM) reasoning is shifting from parameter scaling to test-time compute scaling, yet many existing approaches still rely on uniform brute-force sampling (for example, fixed best-of-N or self-consistency) that is...
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...
Bi-level RL-Heuristic Optimization for Real-world Winter Road Maintenance
arXiv:2602.24097v1 Announce Type: new Abstract: Winter road maintenance is critical for ensuring public safety and reducing environmental impacts, yet existing methods struggle to manage large-scale routing problems effectively and mostly reply on human decision. This study presents a novel, scalable...
Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG
arXiv:2602.23410v1 Announce Type: cross Abstract: Brain foundation models have achieved remarkable advances across a wide range of neuroscience tasks. However, most existing models are limited to a single functional modality, restricting their ability to exploit complementary spatiotemporal dynamics and the...
BiKA: Kolmogorov-Arnold-Network-inspired Ultra Lightweight Neural Network Hardware Accelerator
arXiv:2602.23455v1 Announce Type: cross Abstract: Lightweight neural network accelerators are essential for edge devices with limited resources and power constraints. While quantization and binarization can efficiently reduce hardware cost, they still rely on the conventional Artificial Neural Network (ANN) computation...
Truncated Step-Level Sampling with Process Rewards for Retrieval-Augmented Reasoning
arXiv:2602.23440v1 Announce Type: new Abstract: Training large language models to reason with search engines via reinforcement learning is hindered by a fundamental credit assignment problem: existing methods such as Search-R1 provide only a sparse outcome reward after an entire multi-step...
BioProAgent: Neuro-Symbolic Grounding for Constrained Scientific Planning
arXiv:2603.00876v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical execution in wet-labs. In these irreversible environments, probabilistic hallucinations are not merely incorrect, but also...
Alien Science: Sampling Coherent but Cognitively Unavailable Research Directions from Idea Atoms
arXiv:2603.01092v1 Announce Type: new Abstract: Large language models are adept at synthesizing and recombining familiar material, yet they often fail at a specific kind of creativity that matters most in research: producing ideas that are both coherent and non-obvious to...
FCN-LLM: Empower LLM for Brain Functional Connectivity Network Understanding via Graph-level Multi-task Instruction Tuning
arXiv:2603.01135v1 Announce Type: new Abstract: Large Language Models have achieved remarkable success in language understanding and reasoning, and their multimodal extensions enable comprehension of images, video, and audio. Inspired by this, foundation models for brain functional connectivity networks derived from...
AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
arXiv:2603.01145v1 Announce Type: new Abstract: In practical LLM applications, users repeatedly express stable preferences and requirements, such as reducing hallucinations, following institutional writing conventions, or avoiding overly technical wording, yet such interaction experience is seldom consolidated into reusable knowledge. Consequently,...
Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization
arXiv:2603.00086v1 Announce Type: new Abstract: Automatic speech recognition for French medical conversations remains challenging, with word error rates often exceeding 30% in spontaneous clinical speech. This study proposes a multi-pass LLM post-processing architecture alternating between Speaker Recognition and Word Recognition...
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...
Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity
arXiv:2603.03190v1 Announce Type: new Abstract: During music listening, cortical activity encodes both acoustic and expectation-related information. Prior work has shown that ANN representations resemble cortical representations and can serve as supervisory signals for EEG recognition. Here we show that distinguishing...