AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchLocalized time-frequency representation learning for bioacoustic classification in complex soundscapes
arXiv:2502.13440v2 Announce Type: replace-cross Abstract: Prevailing bioacoustic classifiers assign species labels to fixed time-frequency windows rather than to individual vocalizations. When multiple vocalizations occu…
- arXiv cs.AIresearchLearning Where and What to Restore for Composite Image Restoration
arXiv:2503.17915v2 Announce Type: replace-cross Abstract: Real-world degraded images often contain multiple co-occurring degradation types, making composite image restoration fundamentally different from the single-degra…
- arXiv cs.AIresearchEmergence of psychopathological computations in large language models
arXiv:2504.08016v3 Announce Type: replace-cross Abstract: Can large language models (LLMs) instantiate computations of psychopathology? In this work, we establish a computational-theoretical framework to provide an accou…
- arXiv cs.AIresearchFocus on Likely Classes for Test-Time Prediction
arXiv:2505.03819v4 Announce Type: replace-cross Abstract: We ask: Can focusing on likely classes of a single, in-domain sample improve accuracy? Prior work argued "no", we answer "yes" on average. Standard entropy minimi…
- arXiv cs.AIresearchTags for DAGs: Graph Refinement with Meta-Informed Relations
arXiv:2506.19459v2 Announce Type: replace-cross Abstract: Causal discovery has shifted from data-centric methods to hybrid strategies that integrate semantic knowledge from experts or large language models (LLMs). Such e…
- arXiv cs.AIresearchAudio-JEPA: Joint-Embedding Predictive Architecture for Audio Representation Learning
arXiv:2507.02915v2 Announce Type: replace-cross Abstract: Building on the Joint-Embedding Predictive Architecture (JEPA) paradigm, a recent self-supervised learning framework that predicts latent representations of maske…
- arXiv cs.AIresearchLLM Bidders Preserve the Mechanism-Level Orderings of Human Bidders
arXiv:2507.09083v2 Announce Type: replace-cross Abstract: Training on vast amounts of human-generated data has motivated growing interest in using large language models (LLMs) to simulate human behavior. We ask which fea…
- arXiv cs.AIresearchBridging MOOCs, Smart Teaching, and AI-Assisted Learning: A Unified Quantitative Model
arXiv:2507.14266v4 Announce Type: replace-cross Abstract: MOOCs, Smart Teaching (ST), and AI-assisted learning support different stages of higher education, yet a unified quantitative basis for analyzing their contributi…
- arXiv cs.AIresearchOn the Interaction of Compressibility and Adversarial Robustness
arXiv:2507.17725v3 Announce Type: replace-cross Abstract: As demands for resource efficiency and safety in modern neural networks intensify, substantial research effort has gone into model compression and adversarial rob…
- arXiv cs.AIresearchRooseBERT: A New Deal For Political Language Modelling
arXiv:2508.03250v5 Announce Type: replace-cross Abstract: The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such…
- arXiv cs.AIresearchGraph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning
arXiv:2508.14859v2 Announce Type: replace-cross Abstract: Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive representatio…
- arXiv cs.AIresearchLearning Domain- and Class-Disentangled Prototypes for Domain-Generalized EEG Emotion Recognition
arXiv:2509.01135v3 Announce Type: replace-cross Abstract: Electroencephalography (EEG)-based emotion recognition plays a critical role in affective Brain-Computer Interfaces (aBCIs), yet its practical deployment remains…
- arXiv cs.AIresearchOne Model, Many Morals: Uncovering Cross-Linguistic Misalignments in Computational Moral Reasoning
arXiv:2509.21443v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed across multilingual and multicultural settings, yet it remains unclear whether changing language leads mode…
- arXiv cs.AIresearchPushing Toward the Simplex Vertices: A Simple Remedy for Code Collapse in Smoothed Vector Quantization
arXiv:2509.22161v4 Announce Type: replace-cross Abstract: Vector quantization, which discretizes a continuous vector space into a finite set of representative vectors (a codebook), has been widely adopted in modern machi…
- arXiv cs.AIresearchDynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking
arXiv:2509.22828v2 Announce Type: replace-cross Abstract: Rearranging objects in cluttered tabletop environments remains a long-standing challenge in robotics. Classical planners often generate inefficient, high-cost pla…
- arXiv cs.AIresearchPatch Rebirth: Fast and Transferable Model Inversion of Vision Transformers
arXiv:2509.23235v3 Announce Type: replace-cross Abstract: Model inversion is a widely adopted technique in data-free learning that reconstructs synthetic inputs from a pretrained model through iterative optimization, wit…
- arXiv cs.AIresearchGraph Your Own Prompt
arXiv:2509.23373v3 Announce Type: replace-cross Abstract: We propose Graph Consistency Regularization (GCR), a novel framework that injects relational graph structures, derived from model predictions, into the learning p…
- arXiv cs.AIresearchQuantifying How Training Gradient Sparsity Affect Spiking Neural Network Accuracy And Robustness
arXiv:2509.23762v4 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) have recently received increasing attention in both computational neuroscience and artificial intelligence owing to their potential…
- arXiv cs.AIresearchAttentionViG: Cross-Attention-Based Dynamic Neighbor Aggregation in Vision GNNs
arXiv:2509.25570v2 Announce Type: replace-cross Abstract: Vision Graph Neural Networks (ViGs) have demonstrated promising performance in image recognition tasks against Convolutional Neural Networks (CNNs) and Vision Tra…
- arXiv cs.AIresearchLearning to Generate Rigid Body Interactions with Video Diffusion Models
arXiv:2510.02284v4 Announce Type: replace-cross Abstract: Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond their creative pot…
- arXiv cs.AIresearchInvestigating The Smells of LLM Generated Code
arXiv:2510.03029v2 Announce Type: replace-cross Abstract: Context: Large Language Models (LLMs) are increasingly being used to generate program code. Much research has been reported on the functional correctness of gener…
- arXiv cs.AIresearchAI Adoption Across Mission-Driven Organizations
arXiv:2510.03868v2 Announce Type: replace-cross Abstract: Despite AI's promise, little is known about how mission-driven organizations (MDOs) adopt AI. We conducted semi-structured interviews with 15 experts and leaders…
- arXiv cs.AIresearchNOSA: Native and Offloadable Sparse Attention
arXiv:2510.13602v3 Announce Type: replace-cross Abstract: Decoding throughput improvements from larger inference batches are limited by GPU memory, which is largely consumed by the key-value (KV) cache. Prior training-fr…
- arXiv cs.AIresearchScaling Vision Transformers for Functional MRI with Flat Maps
arXiv:2510.13768v3 Announce Type: replace-cross Abstract: We study the problem of training self-supervised foundation models for functional MRI. Our main contributions are: (1) we introduce a new model family (CortexMAE)…
- arXiv cs.AIresearchAlignBeat: A Latent Variable Model for Multi-Class Beat Tracking from Partially Labeled Data
arXiv:2510.14391v3 Announce Type: replace-cross Abstract: Recent neural beat trackers predict beats and downbeats with two independent frame-wise binary classification heads, a convention adopted because a single multi-c…
- arXiv cs.AIagentsMA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization
arXiv:2510.16635v3 Announce Type: replace-cross Abstract: Prompt optimization has become a practical way to improve the performance of Large Language Models (LLMs) without retraining. However, most existing frameworks tr…
- arXiv cs.AIagentsReinforcement Learning and Consumption-Savings Behavior
arXiv:2510.20748v3 Announce Type: replace-cross Abstract: This paper demonstrates how reinforcement learning can explain two puzzling empirical patterns in household consumption behavior during economic downturns. I deve…
- arXiv cs.AIagentsMitigating Hallucination in Large Language Models: A Capability-Oriented Survey on RAG, Reasoning, and Agentic Systems
arXiv:2510.24476v2 Announce Type: replace-cross Abstract: Hallucination remains one of the key obstacles to the reliable deployment of large language models (LLMs). Although various mitigation approaches have been propos…
- arXiv cs.AIresearchA Survey on Efficient Vision-Language-Action Models
arXiv:2510.24795v3 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) represent a significant frontier in embodied intelligence, aiming to bridge digital knowledge with physical-world interaction…
- arXiv cs.AIresearchSparse, self-organizing ensembles of local kernels detect rare statistical anomalies
arXiv:2511.03095v2 Announce Type: replace-cross Abstract: Modern artificial intelligence has revolutionized how we extract representations from scientific data, yet the statistical properties of these representations rem…
- arXiv cs.AIresearchCOGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing
arXiv:2511.06894v4 Announce Type: replace-cross Abstract: Reconstruction-based methods are a dominant paradigm in time series anomaly detection (TSAD), however, their near-universal reliance on Mean Squared Error (MSE) l…
- arXiv cs.AIresearchD-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Frequency and Pixel Spaces
arXiv:2511.11286v4 Announce Type: replace-cross Abstract: Out-of-domain (OOD) robustness is challenging to achieve in real-world computer vision, especially in unsupervised domain adaptation scenarios, where shifts in im…
- arXiv cs.AIresearchServer-Enforced Watermarking in U-Shaped Split Federated Learning
arXiv:2511.14422v2 Announce Type: replace-cross Abstract: U-shaped split federated learning (U-SFL) enables resource-constrained Internet of Things devices to collaboratively train models with an edge server while retain…
- arXiv cs.AIagentsMASTEST: A LLM-Based Multi-Agent System For Testing RESTful APIs
arXiv:2511.18038v2 Announce Type: replace-cross Abstract: Testing RESTful API is increasingly complicated but indispensable to quality assurance of cloud-native applications. This paper reports a multi-agent system calle…
- arXiv cs.AIresearchThe Devil in the Details: Emergent Misalignment, Format and Coherence in Open-Weights LLMs
arXiv:2511.20104v2 Announce Type: replace-cross Abstract: Prior work has shown that fine-tuning models on a narrow domain with misaligned data can lead to broad misalignment - a phenomenon termed "emergent misalignment"…
- arXiv cs.AIresearchWhy They Disagree: Decoding Differences in Opinions about AI Risk
arXiv:2512.06350v2 Announce Type: replace-cross Abstract: Identifying the reasons for disagreements between influential points of view on issues that affect the public can help produce informed policy responses, even if…
- arXiv cs.AIresearchSoft Geometric Inductive Bias for Object Centric Dynamics
arXiv:2512.15493v2 Announce Type: replace-cross Abstract: Physical systems are naturally parameterized in terms of geometric entities and their transformations. While exact equivariance can be a powerful inductive bias,…
- arXiv cs.AIresearchFasterPy: An LLM-based Code Execution Efficiency Optimization Framework
arXiv:2512.22827v3 Announce Type: replace-cross Abstract: Code often suffers from performance bugs. These bugs necessitate the research and practice of code optimization. Traditional rule-based methods rely on manually d…
- arXiv cs.AIresearchSB-TRPO: Towards Safe Reinforcement Learning with Hard Constraints
arXiv:2512.23770v4 Announce Type: replace-cross Abstract: In safety-critical domains, reinforcement learning (RL) systems must satisfy strict, zero-cost safety constraints while achieving meaningful task performance. Exi…
- arXiv cs.AIresearchVulcan: Instance-specialized, Verifiable Systems Heuristics Through LLM-driven Search
arXiv:2512.25065v3 Announce Type: replace-cross Abstract: Systems resource management tasks rely primarily on hand-designed heuristics. However, growing hardware heterogeneity and workload diversity require heuristics sp…
- arXiv cs.AIresearchDeep Delta Learning
arXiv:2601.00417v5 Announce Type: replace-cross Abstract: Transformer residual streams are updated by addition. A sufficiently expressive residual block can represent content replacement, but the residual update itself h…
- arXiv cs.AIresearchNC-Bench: An LLM Benchmark for Evaluating Conversational Competence
arXiv:2601.06426v3 Announce Type: replace-cross Abstract: Existing LLM benchmarks evaluate what models say, such as whether answers are correct, faithful, or helpful, but they do not test whether models produce the right…
- arXiv cs.AIresearchUntangling Input Language from Reasoning Language: A Diagnostic Framework for Cross-Lingual Moral Alignment in LLMs
arXiv:2601.10257v2 Announce Type: replace-cross Abstract: When LLMs judge moral dilemmas, do they reach different conclusions in different languages, and if so, why? Two factors could drive such differences: the language…
- arXiv cs.AIresearchContextual Distributionally Robust Optimization with Causal and Continuous Structure
arXiv:2601.11016v3 Announce Type: replace-cross Abstract: We propose a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution…
- arXiv cs.AIresearchA Scalable Entity-Based Framework for Auditing Bias in Large Language Models
arXiv:2601.12374v3 Announce Type: replace-cross Abstract: Existing approaches to bias evaluation in large language models (LLMs) trade ecological validity for statistical control, relying either on artificial prompts tha…
- arXiv cs.AIagentsOP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agents
arXiv:2601.13722v2 Announce Type: replace-cross Abstract: Memory-augmented conversational agents enable personalized interactions using long-term user memory and have gained substantial traction. However, existing benchm…
- arXiv cs.AIagentsJust-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates
arXiv:2601.18510v4 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights after deployment. Con…
- arXiv cs.AIresearchMGSM-Pro: A Simple Strategy for Robust Multilingual Mathematical Reasoning Evaluation
arXiv:2601.21225v5 Announce Type: replace-cross Abstract: Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation has lagged behind Engli…
- arXiv cs.AIresearchHypersolid: Emergent Vision Representations via Short-Range Repulsion
arXiv:2601.21255v3 Announce Type: replace-cross Abstract: A central problem in self-supervised learning is preventing representation collapse. Most methods avoid it through global mechanisms, such as contrastive expansio…
- arXiv cs.AIresearchDenoising Time Matters:Diverse Generation in Diffusion Language Models
arXiv:2601.22629v3 Announce Type: replace-cross Abstract: Diffusion language models (Diffusion-LMs) generate text through iterative denoising, exposing a temporal structure that is largely absent from autoregressive deco…