AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchDeltaTTT: Layerwise Optimization for Nonlinear Recurrent Memory
arXiv:2610.08553v1 Announce Type: cross Abstract: Sequential test-time training adapts a memory network through successive updates, each computing an inner-loop gradient based on the network's previous state. Intuitively…
- arXiv cs.AIresearchSystemization of Knowledge (SoK): Human-Centered AI Safety for Youth
arXiv:2610.08554v1 Announce Type: cross Abstract: While HCI increasingly examines AI-safety for youth, the literature lacks a comprehensive view of what risks have been identified, how they are addressed, and whether pro…
- arXiv cs.AIresearchLatent space bias directions in LLMs capture confidence, not fairness
arXiv:2610.08559v1 Announce Type: cross Abstract: Activation steering has gained popularity as a lightweight inference-time debiasing technique for large language models. However, prior work reports that steering vectors…
- arXiv cs.AIresearchRAG-PIBench: A Leakage-Aware Benchmark for Prompt-Injection Detection in Trustworthy RAG Systems
arXiv:2610.08571v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to prompt-injection attacks embedded in retrieved content. We introduce RAG-PIBench, a benchmark for RAG-style…
- arXiv cs.AIresearchFedDermaSeg: Federated Learning for Dermatological Image Segmentation
arXiv:2610.08574v1 Announce Type: cross Abstract: Skin cancer is a major global health concern, and early detection and accurate lesion delineation are important for effective diagnosis and treatment planning. Automated…
- arXiv cs.AIresearchHow Learning Governs Unlearning across the Memorization-Generalization Spectrum
arXiv:2610.08577v1 Announce Type: cross Abstract: While unlearning seeks to negate undesired capabilities acquired through learning, little research has examined how the way models learn shapes their subsequent unlearnin…
- arXiv cs.AIresearchA Swarm-Coordinated Multi-Robot System for Early Stress Detection in Agricultural Rows Using Multimodal Leaf Sensing
arXiv:2610.08603v1 Announce Type: cross Abstract: Early stress detection in crops is a necessity today to improve efficiency and reduce waste of time, money, and effort. However, most modern techniques, such as hyperspec…
- arXiv cs.AIresearchEarly Memory Selection for Balanced Adam
arXiv:2610.08624v1 Announce Type: cross Abstract: We propose a method for choosing the shared memory parameter $\beta_1=\beta_2=\beta$ in Adam from a short pilot training. The selected $\beta$ remains fixed during the su…
- arXiv cs.AIresearchFeature Information Dynamics in Diffusion
arXiv:2610.08626v1 Announce Type: cross Abstract: Diffusion models generate data through a continuum of denoising problems, and are widely observed to reveal coarse structure before fine detail. Yet, this intuition is mo…
- arXiv cs.AIresearchHygieneRoboBench: Benchmarking Hygiene-Aware Planning for Household Robots
arXiv:2610.08642v1 Announce Type: cross Abstract: Contact with contaminated objects can spread hazards through a household robot's grippers, tools, and shared surfaces, while new contacts can make an existing plan unsafe…
- arXiv cs.AIresearchSelective Transfer of RL Updates for Visual Reasoning
arXiv:2610.08659v1 Announce Type: cross Abstract: Model merging provides a training-free way to transfer reasoning capabilities from language models to vision-language models (VLMs), but endpoint-based transfer can confl…
- arXiv cs.AIresearchMemFLoRA: Memory-Floor LoRA for CNN Adaptation at the Edge
arXiv:2610.08669v1 Announce Type: cross Abstract: On-device learning is necessary when the model encounters user-,sensor-, or environment-specific shifts after deployment. Although parameter-efficient fine-tuning (PEFT)…
- arXiv cs.AIresearchSecure Speculative Decoding for Large Language Models
arXiv:2610.08678v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates inference for a large language model (LLM), referred to as the \emph{target model}, by first using a smaller model, referred to a…
- arXiv cs.AIresearchEgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning
arXiv:2610.08726v1 Announce Type: cross Abstract: Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a poor supervisory ta…
- arXiv cs.AIresearchReinforcement Learning with Conformal Action Sets: An Application to Sequential Recommendation
arXiv:2610.08743v1 Announce Type: cross Abstract: Sequential recommenders typically use a fixed slate size even though the number of useful alternatives changes within a session. We propose Reinforcement Learning with Ca…
- arXiv cs.AIresearchWorldSonus: Bringing Sound to Worlds
arXiv:2610.08760v1 Announce Type: cross Abstract: Recent advances in world models have enabled increasingly realistic visual synthesis. However, these generated environments remain largely silent. Bringing sound to world…
- arXiv cs.AIresearchDepthWorld: 3D World Model for Robot Manipulation
arXiv:2610.08780v1 Announce Type: cross Abstract: World models offer a data-driven alternative to traditional simulators for robotics, with applications spanning policy evaluation, improvement, and planning. All of these…
- arXiv cs.AIresearchIdeaAnchor: Teaching LLMs to Turn Literature into Research Ideas
arXiv:2610.08781v1 Announce Type: cross Abstract: Scientific research often begins by synthesizing ideas from a set of related papers to identify gaps and formulate new directions. However, training language models to pe…
- arXiv cs.AIresearch4D-HOF: Hand-Object Flow Matching for Feed-Forward 4D Interaction Reconstruction
arXiv:2610.08782v1 Announce Type: cross Abstract: Existing methods for 4D hand-object reconstruction often rely on costly per-sequence optimization, while generative approaches typically synthesize interactions from rand…
- arXiv cs.AIresearchWhen Explanations Compete: Policy-Aware Selection Under Uncertainty
arXiv:2410.05479v2 Announce Type: replace Abstract: Uncertainty-aware explanation methods often produce several alternatives for the same prediction. Selecting among them requires a policy for balancing prediction confid…
- arXiv cs.AIresearchPuzzleJAX: A Benchmark for Reasoning and Learning
arXiv:2508.16821v2 Announce Type: replace Abstract: We introduce PuzzleJAX, a GPU-accelerated puzzle game engine and description language designed to support rapid benchmarking of tree search, reinforcement learning, and…
- arXiv cs.AIresearchUniverse of Thoughts: A Computational Framework for Creative Reasoning in Large Language Models
arXiv:2511.20471v3 Announce Type: replace Abstract: Recent advances in Large Language Model (LLM) reasoning have improved conventional problem solving, but creative reasoning remains comparatively underexplored. Inspired…
- arXiv cs.AIresearchAlgorithm Selection with Zero Domain Knowledge via Text Embeddings
arXiv:2604.19753v3 Announce Type: replace Abstract: We propose ZeroFolio, a feature-free approach to algorithm selection that uses pretrained text embeddings instead of hand-crafted instance features. It reads the raw in…
- arXiv cs.AIresearchSDFlow: Similarity-Driven Flow Matching for Time Series Generation
arXiv:2605.05736v3 Announce Type: replace Abstract: Vector quantization (VQ) with autoregressive (AR) token modeling is a widely adopted and highly competitive paradigm for time-series generation. However, such models ar…
- arXiv cs.AIresearchVon Neumann Networks
arXiv:2605.05780v2 Announce Type: replace Abstract: In the mid-twentieth century, mathematician and polymath John von Neumann created a computational system on an array of cells as a simple model of the human brain, wher…
- arXiv cs.AIresearchXDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction
arXiv:2605.05866v2 Announce Type: replace Abstract: Multiphase powder X-ray diffraction (PXRD) analysis remains a fundamental bottleneck in structure identification, as real-world synthesis often produces complex mixture…
- arXiv cs.AIresearchRethinking Adapter Placement: A Dominant Adaptation Module Perspective
arXiv:2605.06183v2 Announce Type: replace Abstract: Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method that places trainable low-rank adapters into frozen pre-trained models. Recent studie…
- arXiv cs.AIresearchReward on Path: Learning Intermediate Supervision Signals for Knowledge Graph Question Answering
arXiv:2605.10791v2 Announce Type: replace Abstract: Knowledge Graph Question Answering (KGQA) aims to answer user questions by reasoning over Knowledge Graphs (KGs). Recent methods use supervision derived from answer lab…
- arXiv cs.AIresearchWhen Attention Closes: How LLMs Lose the Thread in Multi-Turn Interaction
arXiv:2605.12922v2 Announce Type: replace Abstract: Large language models can follow complex instructions in a single turn, yet over long multi-turn interactions they often lose the thread of instructions, persona, and r…
- arXiv cs.AIresearchPalette: A Modular, Controllable, and Efficient Framework for On-demand Authorized Safety Alignment Relaxation in LLMs
arXiv:2605.24154v2 Announce Type: replace Abstract: Current safety alignment of foundation models largely follows a \emph{one-size-fits-all} paradigm, applying the same refusal policy across users and contexts. As a resu…
- arXiv cs.AIresearchFHRFormer: A Self-Supervised Masked Transformer Framework for Fetal Heart Rate Time-Series Inpainting and Forecasting
arXiv:2605.29695v2 Announce Type: replace Abstract: Approximately 10% of newborns require assistance to initiate breathing at birth, and around 5% need ventilation support. Fetal heart rate (FHR) monitoring plays a cruci…
- arXiv cs.AIresearchBenchmarking the Personalization Capabilities of Large Language Models
arXiv:2607.20471v2 Announce Type: replace Abstract: Personalization is classically a two-party problem: a sender chooses what to say, and a receiver with independent objectives decides whether to act. A salesperson pitch…
- arXiv cs.AIresearchRefusal-Gated Decoding: Preserving Refusal Behavior Under High-Temperature Sampling
arXiv:2607.20791v2 Announce Type: replace Abstract: Recent advances in truncation-based sampling have helped mitigate drawbacks of high-temperature sampling such as neural text degeneration, thereby enabling greater dive…
- arXiv cs.AIresearchRethinking Modality Reliability in Multimodal Sentiment Analysis with Incomplete Observations
arXiv:2608.03611v3 Announce Type: replace Abstract: Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplete. Existing meth…
- arXiv cs.AIresearchWebRider: Persona-Conditioned Intent Controllers for Live-Web Assistance
arXiv:2608.06704v2 Announce Type: replace Abstract: Delegating a web task involves more than asking a question; it requires transferring a policy: what to verify, how to handle uncertainty, which preferences matter, and…
- arXiv cs.AIresearchPost-Grokking Collapse at the Representation-Readout Interface in Muon-Trained Transformers
arXiv:2608.07436v2 Announce Type: replace Abstract: Muon-trained modular-arithmetic transformers can lose accuracy while retaining linearly decodable task information. Adjacent swaps localize five captured unnormalized f…
- arXiv cs.AIresearchFast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields
arXiv:2311.18029v2 Announce Type: replace-cross Abstract: The current trend in the literature on Time Series Classification is to develop increasingly accurate algorithms by combining multiple models in ensemble hybrids,…
- arXiv cs.AIresearchHypergraph-Enhanced Dual Convolutional Network for Bundle Recommendation
arXiv:2312.11018v4 Announce Type: replace-cross Abstract: Bundle recommendation ranks sets of related items rather than isolated items. Its central challenge is to connect user preferences, item interactions, and bundle…
- arXiv cs.AIresearchFreDF: Learning to Forecast in the Frequency Domain
arXiv:2402.02399v3 Announce Type: replace-cross Abstract: Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research predominantly addresse…
- arXiv cs.AIresearchDiffusion Model-Based Video Editing: A Survey
arXiv:2407.07111v2 Announce Type: replace-cross Abstract: The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making "what you want is what you see" a reality. Among t…
- arXiv cs.AIresearchModeling Time-Dependent Responses of Optical Compressors with Selective State Space Models
arXiv:2408.12549v4 Announce Type: replace-cross Abstract: This paper presents a method for modeling optical dynamic range compressors using deep neural networks with Selective State Space models. The proposed approach su…
- arXiv cs.AIresearchLarge Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification
arXiv:2410.21582v4 Announce Type: replace-cross Abstract: Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of maintaining the general per…
- arXiv cs.AIresearchBoosting Large Language Models with Mask Fine-Tuning
arXiv:2503.22764v3 Announce Type: replace-cross Abstract: The large language model (LLM) is typically integrated into the mainstream optimization protocol. However, it remains underexplored whether maintaining the model…
- arXiv cs.AIresearchKO: Kinetics-inspired Neural Optimizer with PDE Simulation Approaches
arXiv:2505.14777v2 Announce Type: replace-cross Abstract: The design of effective optimization algorithms for neural networks remains a fundamental challenge, and most existing methods rely on heuristic extensions of gra…
- arXiv cs.AIresearchTime-o1: Time-Series Forecasting Needs Transformed Label Alignment
arXiv:2505.17847v3 Announce Type: replace-cross Abstract: Training time-series forecasting models poses unique challenges in loss function design. Most existing approaches adopt temporal mean squared error, but this stud…
- arXiv cs.AIresearchToo Categorical to be Human: Emotion Concepts in LLMs and Humans
arXiv:2508.05880v3 Announce Type: replace-cross Abstract: Understanding human emotions is central to user-facing AI applications, safety alignment, and the simulation of human behavior. As emotional stimuli shape high-st…
- arXiv cs.AIresearchFedCoT: Communication-Efficient Federated Reasoning Enhancement for Large Language Models
arXiv:2508.10020v2 Announce Type: replace-cross Abstract: Enhancing LLM reasoning in federated settings is nontrivial due to stringent computational, communication, and privacy constraints, especially in healthcare, wher…
- arXiv cs.AIresearchCross-Modality Controlled Molecule Generation with Diffusion Language Model
arXiv:2508.14748v2 Announce Type: replace-cross Abstract: The increasing variety of molecular data creates a need for generative models that can flexibly incorporate heterogeneous constraints across modalities. However,…
- arXiv cs.AIresearchLHM-Humanoid: Long-Horizon Human Motion Control for Continuous Object Transport in Cluttered Scenes
arXiv:2508.16943v4 Announce Type: replace-cross Abstract: Physics-based human motion control can make a simulated character walk, sit, and manipulate objects with high physical realism. Almost always, though, this happen…
- arXiv cs.AIresearchPractical Feasibility of Gradient Inversion Attacks in Federated Learning
arXiv:2508.19819v3 Announce Type: replace-cross Abstract: Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions u…