AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchLater Is Better: Token Reduction for ViTs Under Distribution Shift
arXiv:2610.07758v1 Announce Type: cross Abstract: Training-free token reduction accelerates vision transformers by removing redundant tokens across layers, recovering most of the original accuracy at a fraction of the co…
- arXiv cs.AIresearchContrastive Learning for Aspect Representation towards Explainable Recommendation
arXiv:2610.07761v1 Announce Type: cross Abstract: In this work, we propose a novel recommendation model, CLARER (Contrastive Learning for Aspect Representation towards Explainable Recommendation) that integrates aspect f…
- arXiv cs.AIresearchNo Transformer Beats Six Covariates: Long-Horizon Prediction of Depressive Symptoms from Childhood Essays
arXiv:2610.07764v1 Announce Type: cross Abstract: Natural language processing (NLP) models can detect depression-related language in text written near the time symptoms are measured, but whether pretrained transformers c…
- arXiv cs.AIresearchTowards One-for-All Foundation Model for Attributed Graph Clustering
arXiv:2610.07778v1 Announce Type: cross Abstract: Attributed graph clustering aims to discover node groups by jointly exploiting node attributes and graph topology, yet its unsupervised nature makes model selection and a…
- arXiv cs.AIresearchNovice Reliance Calibration in AI-Assisted Decision Making: The Role of Explanations and Self-Assessment
arXiv:2610.07800v1 Announce Type: cross Abstract: Artificial Intelligence (AI) tools are widely used to support decision making in tasks and domains where no immediate performance feedback is available. In these settings…
- arXiv cs.AIresearchDynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models
arXiv:2610.07848v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks. However, most existing PEFT methods rely on…
- arXiv cs.AIresearchLabel-Efficient Deep Learning for ECG Delineation: A Multi-Dataset Benchmark against Widely Used Delineation Tools
arXiv:2610.07885v1 Announce Type: cross Abstract: Electrocardiogram (ECG) delineation, the identification of waveform boundaries, is a foundational step that translates raw ECG signals into clinically interpretable measu…
- arXiv cs.AIresearchVisual Abstention in Unified Multimodal Models
arXiv:2610.07887v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) integrate understanding and generation, yet their generative behavior is rarely governed by what they understand about the task. We forma…
- arXiv cs.AIresearchVariance-Averse $n$-Step Offline Reinforcement Learning for Sparse Long-Horizon Environments
arXiv:2610.07899v1 Announce Type: cross Abstract: Generative actors are transforming offline reinforcement learning (RL) by enabling expressive policy classes that model complex action distributions. However, this expres…
- arXiv cs.AIresearchIEEE 802.11bx - WLAN Intelligent Networking (WIN): Toward an AI-Ready Wi-Fi 9
arXiv:2610.07900v1 Announce Type: cross Abstract: Wi-Fi 9 is expected to go beyond mere communication and provide new services such as sensing or computation. At this juncture, Artificial Intelligence (AI) is taking a le…
- arXiv cs.AIresearchDiverse Motion Customization via Control-based Dynamic Optimization
arXiv:2610.07911v2 Announce Type: replace-cross Abstract: Despite recent advances in video generation, motion customization remains challenging due to content leakage, where appearance attributes from the reference video…
- arXiv cs.AIresearchMultimodal Knowledge Distillation for Gastric Adenocarcinoma Classification from Whole-Slide Images
arXiv:2610.07913v1 Announce Type: cross Abstract: Gastric adenocarcinoma (GA) is a leading cause of cancer-related mortality worldwide, and accurate histopathological subtype classification from whole-slide images (WSIs)…
- arXiv cs.AIresearchDynamic Alignment and Calibration for Multimodal Learning
arXiv:2610.07928v1 Announce Type: cross Abstract: Dynamic multimodal learning aims to learn robust representations by adaptively modeling information discrepancies across modalities. However, existing methods still suffe…
- arXiv cs.AIresearchHybrid Latent Attention for Looped Language Models
arXiv:2610.07940v1 Announce Type: cross Abstract: Looped language models apply the same stack of layers T times to each token, which deepens the model without adding parameters but multiplies its key-value (KV) cache by…
- arXiv cs.AIresearchAdapting Vision-Language-Action Models to Unknown Visual Disruptions During Execution
arXiv:2610.07946v1 Announce Type: cross Abstract: Visual disruptions can arise while a robot is executing a task, leaving a vision-language-action (VLA) policy to respond without knowing the disruption type or timing. We…
- arXiv cs.AIresearchReGraph: A Computational Account of Emergent Generalization in the "what" and "where" Dual Visual Streams
arXiv:2610.07962v1 Announce Type: cross Abstract: Where generalization capacity--the ability to extract context-invariant relational structures--first emerges remains a central question in AI and neuroscience. The founda…
- arXiv cs.AIresearchVisionWeave: Weaving Elastic Visual Representations as a Native Capability of MLLMs
arXiv:2610.07987v1 Announce Type: cross Abstract: Multimodal large language models have become the dominant paradigm for visual understanding, but incur substantial costs by encoding inputs into dense, fixed-size patch t…
- arXiv cs.AIresearchTICDA: Tabular In-Context Data Attribution
arXiv:2610.07996v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) achieve strong predictive performance by conditioning on labeled demonstrations provided in context, without any parameter update. Yet ho…
- arXiv cs.AIresearchWhen Plans Change Answers: Formalizing Cost-Accuracy Optimization for Semantic Queries
arXiv:2610.08089v2 Announce Type: replace-cross Abstract: In semantic query engines, predicates are evaluated by machine-learned models, and the choice of a query plan affects not only the cost of a query but also its re…
- arXiv cs.AIresearchSAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis
arXiv:2610.08093v1 Announce Type: cross Abstract: Developing reliable models for clinical tasks, such as Medical Question Answering (QA), is severely constrained by the limited availability of high-quality, expert-annota…
- arXiv cs.AIresearchExploiting Acoustic and Content-Oriented Speaker Verification Attacks Against Multilingual Voice Anonymization
arXiv:2610.08107v1 Announce Type: cross Abstract: Attacker ASV systems for voice anonymization have been studied primarily in English, leaving their behavior in multilingual settings largely unexplored. Conventional ASV…
- arXiv cs.AIresearchBeyond Waypoint Regression: Query-Based Cost Learning over Reachable Ego Futures for End-to-End Driving
arXiv:2610.08123v1 Announce Type: cross Abstract: End-to-end planners based on waypoint regression achieve strong open-loop accuracy, but they primarily learn to mimic expert geometry and remain difficult to adapt to dep…
- arXiv cs.AIresearchSupermarket Product Detection and Recognition: Utilizing Deep Learning with Rectified Imagery
arXiv:2610.08126v1 Announce Type: cross Abstract: Product Identification has sprung up to become one of the most challenging problems in the automation of the retail industry. With the new industry 5.0 standards, automat…
- arXiv cs.AIresearchNavier-Stokes lost in translation: Why Lean verification of AI autoformalisation does not guarantee correct natural language proofs
arXiv:2610.08144v1 Announce Type: cross Abstract: Autoformalisation is increasingly used to verify mathematical texts, including those generated by AI, as in OpenAI's announced proof of blow-up of solutions to the Navier…
- arXiv cs.AIresearchPenalty-Framed No-Valid-Option MCQA: Analyzing LLM Abstention under Invalid Choices
arXiv:2610.08153v1 Announce Type: cross Abstract: Multiple-choice question answering (MCQA) is commonly used to evaluate large language models under the assumption that one of the provided options is correct, typically u…
- arXiv cs.AIresearchSymphony for Text Generation: Benchmarking Clinical Note Generation
arXiv:2610.08161v1 Announce Type: cross Abstract: Ambient documentation systems are rapidly gaining adoption, yet their impact on clinical note quality remains poorly characterized. We introduce MedConv, a multilingual d…
- arXiv cs.AIresearchCompact Robot Policies Need Fine-Grained Visual Representations
arXiv:2610.08183v1 Announce Type: cross Abstract: Multi-task manipulation policies differ in architecture, scale, and pretrained priors all at once, so published comparisons cannot attribute performance to any single com…
- arXiv cs.AIresearchSTRUCTURALCOST: A controlled reading time dataset for modeling human sentence processing difficulty
arXiv:2610.08208v1 Announce Type: cross Abstract: We introduce STRUCTURALCOST, a self-paced reading dataset of 475 participants and 40,800 observations isolating the processing cost of long-distance subject-verb dependen…
- arXiv cs.AIresearchVOMMI: Collecting and Leveraging Portable Demonstrations for Mobile Manipulation
arXiv:2610.08220v1 Announce Type: cross Abstract: Portable mobile-manipulation demonstrations can help alleviate data scarcity for embodied intelligence, but obtaining reliable, low-cost, and robot-free motion supervisio…
- arXiv cs.AIresearchDySCo: Dynamic Sharding for Collaborative Edge-Cloud LLM Inference with Depth-Synchronized Batching
arXiv:2610.08268v1 Announce Type: cross Abstract: Pervasive intelligent applications are increasingly deployed on mobile and Internet of Things (IoT) edge devices. Consequently, Large Language Models (LLMs) are increasin…
- arXiv cs.AIresearchMARCO: The Radioactive Watermark for Protein Generative Models
arXiv:2610.08316v1 Announce Type: cross Abstract: Protein Generative Models (PGMs) have revolutionized structural biology by enabling the design of complex 3D protein structures from sequence data. However, this breakthr…
- arXiv cs.AIresearchHow Much Planning Is Enough? Reducing Search and Computation in World-Model Planning
arXiv:2610.08350v1 Announce Type: cross Abstract: Visual world models enable goal-directed control through decision-time action search, but their deployment efficiency is often limited by conservatively large planning bu…
- arXiv cs.AIresearchSensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals
arXiv:2610.08355v1 Announce Type: cross Abstract: Flow-matching models start from an isotropic Gaussian source, the standard choice when the correlation structure of the data is unknown in advance. For multi-channel brai…
- arXiv cs.AIresearchTest-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration
arXiv:2610.08358v1 Announce Type: cross Abstract: Post-training quantization is a standard route to fitting vision transformers (ViTs) into edge compute and memory budgets, yet quantized models become especially brittle…
- arXiv cs.AIresearchAccelerating the Development of PLGA In Situ Forming Depots Through AI-Driven Multi-Objective Optimization
arXiv:2610.08368v1 Announce Type: cross Abstract: Developing long-acting injectable formulations requires the simultaneous optimization of drug loading, release kinetics, viscosity, injectability, stability and other obj…
- arXiv cs.AIresearchForesight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering
arXiv:2610.08388v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from hallucinations on knowledge-intensive task…
- arXiv cs.AIresearchAtom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems
arXiv:2610.08400v1 Announce Type: cross Abstract: Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to generalize across downs…
- arXiv cs.AIresearchGeoPID: Decomposing and Steering Visual Information in Vision-Language Models
arXiv:2610.08401v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) have shown outstanding performance across diverse applications, they tend to under-use visual information and over-rely on text…
- arXiv cs.AIresearchLearning from Failures: A Failure-Driven Prompt Refinement for LLM-Based Vulnerability Analysis
arXiv:2610.08405v1 Announce Type: cross Abstract: Large Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depends heavily on prompt design. Existing research pri…
- arXiv cs.AIresearchKnowing When Not to Answer: Cross-Domain and Multi-Turn Generalization of Latent Underspecification Signals
arXiv:2610.08413v1 Announce Type: cross Abstract: Large language models routinely answer questions that cannot be answered from the information given, and in dialogue they answer before enough has been said. Unanswerabil…
- arXiv cs.AIresearchRethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability
arXiv:2610.08448v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) trains a student on its own generations using teacher feedback. With different tokenizers, comparing teacher and student predictions requires…
- arXiv cs.AIresearchMetaLearnNCA: Few-Shot Offline Meta-Learning via Interacting Neural Cellular Automata
arXiv:2610.08479v1 Announce Type: cross Abstract: Few-shot meta-learning traditionally formulates task adaptation either as analytical gradient descent through unrolled computational graphs or as metric-based distance co…
- arXiv cs.AIresearchKnee3DVLM: Dual-Sequence Full-Volume Vision-Language Modeling for Comprehensive Knee MRI Assessment
arXiv:2610.08482v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) are increasingly being applied to three-dimensional medical imaging, but their application to knee MRI remains limited, particularly…
- arXiv cs.AIresearchLanguage-model ratings of depression reflect the rater more than the patient
arXiv:2610.08501v1 Announce Type: cross Abstract: Depression has no diagnostic blood test. Language models promise tireless, consistent assessment, but can accurate raters disagree about individuals? We pre-registered 88…
- arXiv cs.AIresearchX-OPM: Explainable Automatic Digital On-Chip Power Modeling for Enhanced Robustness
arXiv:2610.08502v1 Announce Type: cross Abstract: Proactive power management systems reduce processor dynamic power through runtime power prediction and power-aware scheduling. Accurate, stable and low-overhead digital o…
- arXiv cs.AIresearchMedCORE: Criteria-Grounded Clinical Reasoning for Interpretable Medical Image Diagnosis
arXiv:2610.08528v1 Announce Type: cross Abstract: Clinical diagnosis is inherently a structured reasoning process, yet existing deep learning models often bypass this structure by mapping image features directly to disea…
- arXiv cs.AIresearchFlowCF: Sparse Counterfactual Explanations for Mixed-Type Tabular Data using Flow Matching
arXiv:2610.08537v1 Announce Type: cross Abstract: In the field of Explainable AI (XAI), counterfactual (CF) explanations interpret a model's decision by suggesting the changes to the input that would lead to a more favou…
- arXiv cs.AIresearchFrom Shared Demand Patterns to Local Uncertainty: Probabilistic Load Forecasting by Mixing Compact Adaptations
arXiv:2610.08538v1 Announce Type: cross Abstract: Probabilistic load forecasting has been widely studied for power-system operation and planning, but customer- and transformer-level forecasting introduces a distinct scal…
- arXiv cs.AIresearchMicro Neural Policies for Safe Real-Time Robotic Control
arXiv:2610.08541v1 Announce Type: cross Abstract: In this paper, we investigate the synthesis of Micro Neural Policies (MNP) to enable safe and robust real-time robotic control on computationally constrained embedded dev…
- arXiv cs.AIresearchHow High Is 0.6? Floors, Ceilings, and Headroom in Interpretability Probing
arXiv:2610.08544v1 Announce Type: cross Abstract: Probes are the workhorse of interpretability. If a model's hidden states predict a variable, the model is said to represent it. But a probe score has no fixed meaning. An…