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Discover Fast Power Allocation Solution for Multi-Target Tracking via AlphaEvolve Evolution

arXiv:2605.01794v1 Announce Type: cross Abstract: Efficient radar resource allocation is a fundamental yet computationally challenging problem, as optimal solutions typically require iterative optimiz

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Disease Is a Spectral Perturbation

DGX agent

arXiv:2605.02949v1 Announce Type: new Abstract: We propose a novel method of understanding disease transformation from a healthy baseline with biomarker-level explainability. By modeling the biomarker

researcharxiv-cs-lg

6 May 2026

Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding

arXiv:2605.02290v1 Announce Type: new Abstract: Distilling large reasoning models is essential for making Long-CoT reasoning practical, as full-scale inference remains computationally prohibitive. Exi

Distributed Learning with Adversarial Gradient Perturbations

arXiv:2605.03313v1 Announce Type: new Abstract: Privacy concerns in distributed learning often lead clients to return intentionally altered gradient information. We consider the problem of learning co

Distribution-Free Pretraining of Classification Losses via Evolutionary Dynamics

arXiv:2605.03722v1 Announce Type: new Abstract: We propose Evolutionary Dynamic Loss (EDL), a framework that learns a transferable classification loss in the probability space using unlimited syntheti

Do LLMs have core beliefs?

arXiv:2605.03255v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has sparked debate about whether these systems exhibit human-level cognition. In this debate, little attention

Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scaling

arXiv:2601.21684v2 Announce Type: replace Abstract: Test-Time Scaling enhances the reasoning capabilities of Large Language Models by allocating additional inference compute to broaden the exploration

Double Rectified Linear Unit-based Modular Semantics for Quantitative Bipolar Argumentation Framework

arXiv:2605.02551v1 Announce Type: new Abstract: Quantitative Bipolar Argumentation Frameworks (QBAFs) provide an alternative approach to computing argument acceptability in Bipolar Argumentation Frame

Dynamic Distillation and Gradient Consistency for Robust Long-Tailed Incremental Learning

arXiv:2605.03364v1 Announce Type: new Abstract: The task of Long-tailed Class Incremental Learning (LT-CIL) addresses the sequential learning of new classes from datasets with imbalanced class distrib

E-MIA: Exam-Style Black-Box Membership Inference Attacks against RAG Systems

arXiv:2605.00955v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) equips large language models (LLMs) with external evidence by retrieving documents at inference time, but it also

Ecologically-Constrained Task Arithmetic for Multi-Taxa Bioacoustic Classifiers Without Shared Data

arXiv:2605.03914v1 Announce Type: cross Abstract: Training data for bioacoustics is scattered across taxa, regions, and institutions. Centralizing it all is often infeasible. We show that independentl

Effect-Transparent Governance for AI Workflow Architectures: Semantic Preservation, Expressive Minimality, and Decidability Boundaries

arXiv:2605.01030v2 Announce Type: new Abstract: We present a machine-checked formalization of structurally governed AI workflow architectures and prove that effect-level governance can be imposed with

EFGPP: Exploratory framework for genotype-phenotype prediction

arXiv:2605.02954v1 Announce Type: cross Abstract: Predicting complex human traits from genetic data is challenging because different genetic, clinical, and molecular data sources often contain differe

EMOVIS: Emotion-Optimized Image Processing

arXiv:2605.03131v1 Announce Type: cross Abstract: In cinematography, visual attributes such as color grading, contrast, and brightness are manipulated to reinforce the emotional narrative of a scene.

Empowering LLM Agents with Geospatial Awareness: Toward Grounded Reasoning for Wildfire Response

arXiv:2510.12061v2 Announce Type: replace Abstract: Effective disaster response is essential for safeguarding lives and property. Existing statistical approaches often lack semantic context, generaliz

Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training

arXiv:2605.04008v1 Announce Type: new Abstract: A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of non-essential cells close to o

Enhancing AI-Based ECG Delineation with Deep Learning Denoising Techniques

arXiv:2605.03183v1 Announce Type: new Abstract: Evaluating canine electrocardiograms (ECGs) is challenging due to noise that can obscure clinically relevant cardiac electrical activity. Common sources

Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework

arXiv:2605.03390v1 Announce Type: new Abstract: Supervised talking head forgery detection faces severe generalization challenges due to the continuous evolution of generators. By reducing reliance on

Entanglement is Half the Story: Post-Selection vs. Partial Traces

arXiv:2605.02385v1 Announce Type: cross Abstract: While tensor networks have their traditional application in simulating quantum systems, in the recent decade they have gathered interest as machine le

Epistemic Reject Option Prediction

arXiv:2511.04855v2 Announce Type: replace Abstract: In high-stakes applications, predictive models must not only produce accurate predictions but also quantify and communicate their uncertainty. Rejec

Evaluating Generative Models as Interactive Emergent Representations of Human-Like Collaborative Behavior

arXiv:2605.03855v1 Announce Type: new Abstract: Human-AI collaboration requires AI agents to understand human behavior for effective coordination. While advances in foundation models show promising ca

researcharxiv-cs-ro

Evaluating Reasoning Models for Queries with Presuppositions

arXiv:2605.03050v1 Announce Type: new Abstract: Millions of users turn to AI models for their information needs. It is conceivable that a large number of user queries contain assumptions that may be f

Every time Republicans take power, they increase the deficit. Every time Democrats take power, they reduce it.

Every time Republicans take power, they increase the deficit. Every time Democrats take power, they reduce it. CBO says the Senate Republicans' new reconciliation bill will increase deficits by $71.7B

researchyann-lecun--x

Exact and Approximate Algorithms for Polytree Learning

arXiv:2605.03622v1 Announce Type: cross Abstract: Polytrees are a subclass of Bayesian networks that seek to capture the conditional dependencies between a set of n variables as a directed forest and

Exact ReLU realization of tensor-product refinement iterates

arXiv:2605.03917v1 Announce Type: cross Abstract: We study scalar dyadic refinement operators on R^2 of the form (Vf)(x,y) = sum_{(j,k) in Z^2} c_{j,k} f(2x-j, 2y-k), where only finitely many mask coe

ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability

arXiv:2502.11336v2 Announce Type: replace Abstract: Detecting texts generated by Large Language Models (LLMs) could cause grave mistakes due to incorrect decisions, such as undermining students' acade

Exploring Pass-Rate Reward in Reinforcement Learning for Code Generation

arXiv:2605.02944v1 Announce Type: new Abstract: Reinforcement learning (RL) from unit-test feedback has become a standard post-training recipe for improving large language models (LLMs) on code genera

Feasibility-aware Hybrid Control for Motion Planning under Signal Temporal Logics

arXiv:2605.03662v1 Announce Type: new Abstract: In this work, a novel method for planar task and motion planning based on hybrid modeling is proposed. By virtue of a discrete variable which models loc

researcharxiv-cs-ro

Finite-Size Gradient Transport in Large Language Model Pretraining: From Cascade Size to Intensive Transport Efficiency

arXiv:2605.02968v1 Announce Type: new Abstract: We introduce a finite-size gradient-transport framework for real language-model training, based on five observables (D,z,eta,elta,v_{rel}) that separate

First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint

arXiv:2605.02827v1 Announce Type: new Abstract: Probabilistic values, including Shapley values and semivalues, provide a model-agnostic framework to attribute the behavior of a black-box model to data

Flow Matching on Symmetric Spaces

arXiv:2605.03588v1 Announce Type: new Abstract: We introduce a general framework for training flow matching models on Riemannian symmetric spaces, a large class of manifolds that includes the sphere,

FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles

arXiv:2605.03337v1 Announce Type: new Abstract: The recent surge in 4D Gaussian Splatting (4DGS) has achieved impressive dynamic scene reconstruction. While these methods demonstrate remarkable perfor

From prompting to evidence-based translation: A RAG+prompt system for Japanese-Chinese translation and its pedagogical potential

arXiv:2605.03387v1 Announce Type: new Abstract: Large language models perform well on high-resource pairs but are less reliable for Japanese-Chinese sentences containing noun-modifying clause construc

From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications

arXiv:2605.02859v1 Announce Type: cross Abstract: Scientists increasingly rely on sensor-based data, yet transforming raw streams into insights across the edge-to-cloud continuum remains difficult. Pr

Full-Graph vs. Mini-Batch Training: Comprehensive Analysis from a Batch Size and Fan-Out Size Perspective

arXiv:2601.22678v2 Announce Type: replace Abstract: Full-graph and mini-batch Graph Neural Network (GNN) training approaches have distinct system design demands, making it crucial to choose the approp

Fully Automatic Trace Gas Plume Detection

arXiv:2605.03372v1 Announce Type: new Abstract: Future imaging spectrometers will increase data volumes by orders of magnitude, requiring automated detection of trace gas point sources. We present a f

FUS3DMaps: Scalable and Accurate Open-Vocabulary Semantic Mapping by 3D Fusion of Voxel- and Instance-Level Layers

arXiv:2605.03669v1 Announce Type: new Abstract: Open-vocabulary semantic mapping enables robots to spatially ground previously unseen concepts without requiring predefined class sets. Current training

researcharxiv-cs-ro

Gated Subspace Inference for Transformer Acceleration

arXiv:2605.03109v1 Announce Type: new Abstract: A method is presented for accelerating inference in transformer language models by exploiting the low effective rank of the token activation manifold at

GEM-FI: Gated Evidential Mixtures with Fisher Modulation

arXiv:2605.03750v1 Announce Type: new Abstract: Evidential Deep Learning (EDL) enables single-pass uncertainty estimation by predicting Dirichlet evidence, but it can remain overconfident and poorly c

Generative-AI and the transformation of workforce. A job postings-driven analysis

arXiv:2605.00843v1 Announce Type: cross Abstract: This paper investigates how generative-artificial intelligence AI is reshaping job requirements, skill compositions and sectoral dynamics across globa

Geolocating News about Extreme Climate Events: A Comparative Analysis of Off-the-Shelf Tools for Toponym Identification in German

arXiv:2605.03414v1 Announce Type: new Abstract: Determining the geolocation of extreme climate events and disasters in texts is a common problem in climate impact and adaptation research. Named-entity

GeoTopoDiff: Learning Geometry--Topology Graph Priors through Boundary-Constrained Mixed Diffusion for Sparse-Slice 3D Porous Reconstruction

arXiv:2605.03764v1 Announce Type: new Abstract: Diffusion-based voxel prior modelling is challenging for the reconstruction of large-scale 3D porous microstructures. Due to the demanding requirements

Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution

arXiv:2605.03795v1 Announce Type: new Abstract: Urban air quality forecasting is challenging because pollutant concentrations are nonlinear, nonstationary, spatiotemporally dependent, and often affect

GRIFDIR: Graph Resolution-Invariant FEM Diffusion Models in Function Spaces over Irregular Domains

arXiv:2605.03497v1 Announce Type: new Abstract: Score-based diffusion models in infinite-dimensional function spaces provide a mathematically principled framework for modelling function-valued data, o

Highly Adaptive Principal Component Regression

arXiv:2602.10613v2 Announce Type: replace-cross Abstract: The Highly Adaptive Lasso (HAL) is a nonparametric regression method that achieves almost dimension-free convergence rates under minimal smoot

Illumination-Aware Contactless Fingerprint Spoof Detection via Paired Flash-Non-Flash Imaging

arXiv:2603.17679v2 Announce Type: replace Abstract: Contactless fingerprint recognition enables hygienic and convenient biometric authentication but poses new challenges for spoof detection due to the

Imbalanced Classification under Capacity Constraints

arXiv:2605.03289v1 Announce Type: cross Abstract: In many classification settings, the class of primary interest is underrepresented, leading to imbalanced data problems that arise in applications suc

Information Plane Analysis of Binary Neural Networks

arXiv:2605.03636v1 Announce Type: new Abstract: Information plane (IP) analysis has been suggested to study the training dynamics of deep neural networks through mutual information (MI) between inputs