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A Mathematical Conflict Framework for Contextual Data Modulation

arXiv:2606.02381v1 Announce Type: new Abstract: In this study, a generalized operator-based mathematical conflict framework is presented to explicitly represent structural discrepancies between raw da

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A Methodological Framework for Explicit Control of the Speed-Accuracy Trade-off in Brain-Computer Interfaces

DGX agent

arXiv:2606.00106v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) are limited by low signal-to-noise ratio in modalities such as electroencephalography, which requires multiple trials

model-releasesarxiv-cs-ai

2 Jun 2026

A Minimalist Brain-Computer Musical Interface for Real-Time Emotion-Driven Sonification: System Design and Preliminary Evaluation

arXiv:2606.01473v1 Announce Type: new Abstract: This paper presents a minimalist brain-computer Musical Interface (BCMI) that functions as a real-time affective sonification system, translating prefro

A Modelling and Evaluation Framework for EuroCrops-Driven Sentinel-2 Crop Segmentation

arXiv:2606.00676v1 Announce Type: new Abstract: This work presents a configurable pipeline for generating semantic-segmentation-ready agricultural datasets from Sentinel-2 imagery and EuroCrops parcel

safetyarxiv-cs-cv

A Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Transformer-Based Language Models

arXiv:2601.17952v2 Announce Type: replace-cross Abstract: Interpretability remains a key challenge for deploying language models (LM) in clinical settings such as progression diagnosis of Alzheimer di

A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems

arXiv:2606.00138v1 Announce Type: new Abstract: Finite element analysis (FEA) is the most important numerical approach for solid mechanics. Challenges of FEA include a steep learning curve for entry-l

agentsarxiv-cs-ai

A Multi-Domain Red Teaming Framework for Safety, Robustness, and Fairness Evaluation of Medical Large Language Models

arXiv:2606.00027v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed across healthcare, yet existing benchmarks fail to capture model behavior under adversarial or

A multimodal dataset of photoplethysmography and continuous behavioral responses to ASMR and nature videos

arXiv:2606.00752v1 Announce Type: new Abstract: Autonomous Sensory Meridian Response (ASMR) is a somatosensory phenomenon characterized by pleasant tingling sensations and cardiovascular slowing. Howe

agentsarxiv-cs-lg

A Multiscale Network with Supervised Contrastive Learning for Real-Time Facial Emotion Recognition

arXiv:2606.01069v1 Announce Type: new Abstract: Real-time emotion recognition from facial expressions is a challenging task, particularly in video-based scenarios where multiple emotional states may o

researcharxiv-cs-cv

A Note on Stability for Orthogonalized Matrix Momentum with Client Sampling

arXiv:2606.01720v1 Announce Type: new Abstract: We study finite-sample generalization for a client-sampled distributed optimization scheme with matrix-valued parameters and orthogonalized momentum upd

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis

arXiv:2507.12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient asses

A passive universal grasping mechanism based on an everting shell

arXiv:2606.00470v1 Announce Type: new Abstract: A passive monolithic compliant grasping mechanism that works based on the eversion of an elastically deformable bistable shell is conceptualized. It com

researcharxiv-cs-ro

A Per-Component Diagnostic Protocol for Neural HJB-PIDE Solvers under Control-Dependent Levy Jumps

model-releasesarxiv-cs-lg

arXiv:2606.01122v1 Announce Type: new Abstract: We propose a five-step diagnostic protocol for residual-trained neural HJB-PIDE solvers with control-dependent Levy jumps, targeting a general failure m

A phenomenon of AI-conformity: how algorithms change human moral decision-making

arXiv:2606.00013v1 Announce Type: cross Abstract: Social conformity is a well-documented phenomenon in which individuals shift their opinions towards those of a social majority. As artificial intellig

A physics-informed foundation model for quantitative diffusion MRI

arXiv:2606.00156v1 Announce Type: cross Abstract: Understanding the human brain requires access to its microscopic tissue architecture. Diffusion magnetic resonance imaging (MRI) provides the only non

A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models

arXiv:2606.00563v1 Announce Type: cross Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models. When model

A Pre-Training Analogue of Grokking in Language Models: Tracing Delayed Grammatical Generalization

arXiv:2606.00230v1 Announce Type: new Abstract: Grokking, the phenomenon in which neural networks generalize long after fitting their training data, has been studied in supervised settings on many epo

A Predictive Control Strategy to Offset-Point Tracking for Agricultural Mobile Robots

arXiv:2603.28439v2 Announce Type: replace Abstract: Robots are increasingly being deployed in agriculture to support sustainable practices and improve productivity. They offer strong potential to enab

safetyarxiv-cs-ro

A Primer in Post-Training Reasoning Data: What We Know About How It Works

arXiv:2606.02113v1 Announce Type: cross Abstract: Post-training has become a primary driver of recent progress in large reasoning models, and reasoning data are often the key variable determining whet

A Protocol-Language Model for Network Intrusion (Without Deep Packet Inspection)

arXiv:2606.00155v1 Announce Type: cross Abstract: Modern network intrusion detection systems (NIDS) are caught in a structural contradiction: the protocols carrying the highest threat intelligence are

A Registry-Bound LLM Pipeline for Evidence-Grounded Trait Extraction across Tropical Plants, Aquatic Species, and Exotic Pets

arXiv:2606.00994v1 Announce Type: new Abstract: We describe a registry-bound large-language-model extraction pipeline producing evidence-grounded structured trait records at scale, on cultivated tropi

researcharxiv-cs-cl

A Shared Valence Axis Across Modern LLMs and Human EEG: The Saturation Regularity

arXiv:2606.00129v1 Announce Type: cross Abstract: Large language models (LLMs) have emerged as powerful representation learners whose internal features increasingly align with human cognition. We stud

Applications

A Simulation Platform for Flapping-Wing Vehicles

arXiv:2606.02370v1 Announce Type: new Abstract: Flapping-wing aerial vehicles (FWAVs) demonstrate remarkable agility but face substantial autonomy challenges due to their high sensitivity to aerodynam

applicationsarxiv-cs-ro

A Sonar-Visual Dataset for Cross-Modal Underwater Robot Perception

arXiv:2606.01398v1 Announce Type: new Abstract: Underwater robots typically use both cameras and sonar for perception to leverage the rich semantic details of vision and the robust range measurements

researcharxiv-cs-ro

A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision

arXiv:2606.01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task. Recent multimodal vision-language models have produced systems that admit textual

A Survey of 3D Reconstruction with Event Cameras

arXiv:2505.08438v4 Announce Type: replace-cross Abstract: Event cameras are rapidly emerging as powerful vision sensors for 3D reconstruction, uniquely capable of asynchronously capturing per-pixel br

agentsarxiv-cs-ai

A Systematic Benchmark of Intraoperative Ultrasound-to-MR Synthesis for Brain Tumour Surgery

model-releasesarxiv-cs-cv

arXiv:2606.00630v1 Announce Type: new Abstract: Intraoperative ultrasound (ioUS) is a versatile, cost-effective modality in brain tumour surgery, but its interpretation is difficult: acquisition plane

A Task-Centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula

arXiv:2602.10014v3 Announce Type: replace Abstract: Iterative self-improvement fine-tunes an autoregressive large language model (LLM) on reward-verified outputs generated by the LLM itself. In contra

A Theoretical Framework for Self-Play Theorem Proving Algorithms

arXiv:2606.01861v1 Announce Type: new Abstract: Self-play, a type of training algorithm that enables a model to self-improve, has recently shown promising empirical results in the context of formal th

A Theoretical Framework for Statistical Evaluability of Generative Models

arXiv:2604.05324v2 Announce Type: replace Abstract: Statistical evaluation aims to estimate the generalization performance of a model using held-out i.i.d. test data sampled from the ground-truth dist

A Unified Evaluation-Instructed Framework for Query-Dependent Prompt Optimization

arXiv:2511.19829v2 Announce Type: replace Abstract: Most prompt-optimization methods refine a single static template, making them ineffective in complex and dynamic user scenarios. Existing query-depe

A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures

arXiv:2506.10239v3 Announce Type: replace Abstract: Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned o

researcharxiv-cs-ro

A unifying Bayesian framework for adversarial robustness

arXiv:2510.09288v2 Announce Type: replace-cross Abstract: The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge. Traditional defenses, such

Ablating Archetypes: The Stability of Archetypal SAEs is an Artifact of Initialization and Metric Design

arXiv:2606.02061v1 Announce Type: new Abstract: Dictionary learning with sparse autoencoders (SAEs) produces overcomplete bases from neural network activations that are often interpretable and reduces

AblationBench: Evaluating Automated Planning of Ablations in Empirical AI Research

arXiv:2507.08038v3 Announce Type: replace-cross Abstract: Language model agents are increasingly used to automate scientific research, yet evaluating their scientific contributions remains a challenge

Absorbing Complexity: An Interaction-Native Knowledge Harness for Financial LLM Agents

arXiv:2606.01886v1 Announce Type: new Abstract: Financial AI agents often fail for a simple reason: they make users carry the complexity. A user must repeatedly restate goals, risk preferences, portfo

Accelerating Min-Max Optimization via Power-Law Stepsizes

arXiv:2606.01764v1 Announce Type: cross Abstract: We revisit the convergence guarantees of the Extragradient (EG) method for unconstrained biaffine min-max optimization. It is known that EG with a fix

Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture

model-releasesarxiv-cs-lg

arXiv:2606.01110v1 Announce Type: cross Abstract: Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informe

Accuracy, Stability, and Repeated-Run Reliability of Large Language Models on Deterministic Programming Tasks

arXiv:2606.00920v1 Announce Type: cross Abstract: Run-level pass rate overstates retry-free coverage by up to 17.8 percentage points -- and the gap is largest precisely for mid-performing systems. We

Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo

arXiv:2606.00293v1 Announce Type: new Abstract: Tuning algorithms such as stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD) for approximate sampling and uncertainty qu

ACON: Optimizing Context Compression for Long-horizon LLM Agents

arXiv:2510.00615v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as agents in dynamic real-world environments, where success depends on maintaining precise re

Acoustic and perceptual differences between standard and accented speech and their voice clones

arXiv:2604.01562v2 Announce Type: replace-cross Abstract: Voice cloning is often evaluated in terms of overall quality, but less is known about accent preservation and its perceptual consequences. We

Acting with AI: An Interaction-Based Framework for Agentic Tort Liability

arXiv:2606.00518v1 Announce Type: new Abstract: Agentic AI systems can plan over multiple steps, use tools, and execute tasks over time. When such systems cause harm, tort law struggles to allocate re

agentsarxiv-cs-ai

Active Exploring like a Pigeon: Reinforcing Spatial Reasoning via Agentic Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2606.02459v1 Announce Type: new Abstract: Enabling Vision-Language Models (VLMs) to perform spatial reasoning remains challenging. Existing approaches treat VLMs as passive observers, which is d

ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning

arXiv:2603.09692v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become the standard for aligning Large Language Models (LLMs), yet its efficacy is bottl

ActMVS: Active Scene Reconstruction with Monocular Multi-View Stereo

arXiv:2606.01367v1 Announce Type: cross Abstract: Active scene reconstruction enables robots/UAVs to autonomously plan trajectories and reconstruct environments without costly manual data acquisition.

researcharxiv-cs-cv

AdaCodec: A Predictive Visual Code for Video MLLMs

arXiv:2606.02569v1 Announce Type: cross Abstract: Video is temporally redundant: adjacent frames usually share most objects, background, and layout. Yet existing video multimodal large language models

Tutorials

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks

arXiv:2606.01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs). Traditional methods rely on distance-based

tutorialsarxiv-cs-lg