Search: “models”

Sparse Attention as Compact Kernel Regression

arXiv:2601.22766v3 Announce Type: replace Abstract: Recent work has revealed a link between self-attention mechanisms in transformers and test-time kernel regression via the Nadaraya-Watson estimator,

Spectral Filtering for Complex Linear Dynamical Systems

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arXiv:2601.22400v2 Announce Type: replace-cross Abstract: We study the problem of learning complex-valued linear dynamical systems (CLDS) with sector-bounded spectrum. This class captures oscillatory

researcharxiv-cs-ai

11 May 2026

Spectral Surgery: Class-Targeted Post-Hoc Rebalancing via Hessian Spike Perturbation

arXiv:2605.07790v1 Announce Type: cross Abstract: The Hessian spectrum of trained deep networks exhibits a characteristic structure: a continuous bulk of near-zero eigenvalues and a small number of la

SphereVAD: Training-Free Video Anomaly Detection via Geodesic Inference on the Unit Hypersphere

arXiv:2605.08003v1 Announce Type: new Abstract: Video anomaly detection (VAD) aims to automatically identify events that deviate from normal patterns in untrimmed surveillance videos. Existing methods

SSP-based construction of evaluation-annotated data for fine-grained aspect-based sentiment analysis

arXiv:2605.07446v1 Announce Type: new Abstract: We report the construction of a Korean evaluation-annotated corpus, hereafter called 'Evaluation Annotated Dataset (EVAD)', and its use in Aspect-Based

researcharxiv-cs-cl

Supervised sparse auto-encoders for interpretable and compositional representations

arXiv:2602.00924v2 Announce Type: replace Abstract: Sparse auto-encoders (SAEs) have re-emerged as a prominent method for mechanistic interpretability, yet they face two significant challenges: the no

safetyarxiv-cs-ai

Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning

arXiv:2605.07980v1 Announce Type: new Abstract: These notes introduce the theory of susceptibilities as developed in [arXiv:2504.18274, arXiv:2601.12703] for interpreting neural networks. The suscepti

local-aiarxiv-cs-lg

SWaRL: Safeguard Code Watermarking via Reinforcement Learning

arXiv:2601.02602v2 Announce Type: replace-cross Abstract: We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLMs by embedd

Task-Oriented Communication for Human Action Understanding via Edge-Cloud Co-Inference

arXiv:2605.07354v1 Announce Type: cross Abstract: The expanding application of smart sensing has created a growing demand for the accurate understanding of human action at the network edge. Traditiona

Tessellations of Semi-Discrete Flow Matching

arXiv:2605.07513v1 Announce Type: new Abstract: We study Flow Matching in a semi-discrete setting where a Gaussian source is transported toward a discrete target supported on finitely many points. Thi

The AI-Native Large-Scale Agile Software Development Manifesto

arXiv:2605.07717v1 Announce Type: cross Abstract: Despite the widespread adoption of agile methods, achieving true agility at scale remains elusive. Large-scale agile frameworks remain largely human-c

The Effective Depth Paradox: Evaluating the Relationship between Architectural Topology and Trainability in Deep CNNs

arXiv:2602.13298v3 Announce Type: replace-cross Abstract: This paper investigates the relationship between convolutional neural network (CNN) topology and image recognition performance through a compa

researcharxiv-cs-ai

Towards Differentially Private Reinforcement Learning with General Function Approximation

arXiv:2605.07049v1 Announce Type: cross Abstract: We present the first theoretical guarantees for differentially private online reinforcement learning (RL) with general function approximation, extendi

safetyarxiv-cs-ai

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation

arXiv:2605.06891v1 Announce Type: new Abstract: Labeled datasets reflect the biases of their annotation pipelines, which sometimes introduce label bias: group-conditional label errors that cause syste

safetyarxiv-cs-cv

Towards multi-modal forgery representation learning for AI-generated video detection and localization

arXiv:2605.07232v1 Announce Type: new Abstract: Recent advances in generative AI have democratized video creation at scale. AI-generated videos, including partially manipulated clips across visual and

local-aiarxiv-cs-cv

Applications

Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework

arXiv:2605.07429v1 Announce Type: new Abstract: Existing mobile devices are constrained by compact optical designs, such as small apertures, which make it difficult to produce natural, optically reali

applicationsarxiv-cs-cv

Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments

arXiv:2605.07412v1 Announce Type: cross Abstract: Although Global Navigation Satellite Systems (GNSS) provide a general solution for bike tracking outdoors, there still exist complex riding environmen

local-aiarxiv-cs-ai

TriDE: Triangle-Consistent Translation Directions for Global Camera Pose Estimation

arXiv:2605.06889v1 Announce Type: new Abstract: Pairwise translation directions are a key input to camera location estimation in global structure-from-motion. Existing estimators usually process each

local-aiarxiv-cs-cv

UFT: Unifying Fine-Tuning of SFT and RLHF/DPO/UNA through a Generalized Implicit Reward Function

arXiv:2410.21438v3 Announce Type: replace Abstract: By pretraining on trillions of tokens, an LLM gains the capability of text generation. However, to enhance its utility and reduce potential harm, SF

safetyarxiv-cs-cl

UNA: A Unified Supervised Framework for Efficient LLM Alignment Across Feedback Types

arXiv:2408.15339v4 Announce Type: replace-cross Abstract: RL alignment methods, including RLHF and DPO, are primarily based on pairwise preference data. Although scalar or score-based feedback has bee

safetyarxiv-cs-cl

Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods

arXiv:2605.07987v1 Announce Type: cross Abstract: Atlas-based approaches allow high-quality, patient-specific shape reconstructions of cardiac anatomy from sparse and/or noisy data such as point cloud

Unlocking High-Fidelity Molecular Generation from Mass Spectra via Dual-Stream Line Graph Diffusion

arXiv:2605.07048v1 Announce Type: cross Abstract: De novo molecular generation from tandem mass spectra is a challenging inverse problem whose core difficulty lies in the circular dependency between a

local-aiarxiv-cs-ai

Variable Aerodynamic Damping via Co-Contraction: A Dynamic Isomorphism with Variable Stiffness Actuators

arXiv:2605.07292v1 Announce Type: new Abstract: We prove that aerodynamic co-contraction in a redundant dual-rotor actuator can tune a passive, trim-defined aero-mechanical damping while keeping the c

local-aiarxiv-cs-ro

VDCook:DIY video data cook your MLLMs

arXiv:2603.05539v2 Announce Type: replace-cross Abstract: We introduce VDCook: a self-evolving video data operating system, a configurable video data construction platform for researchers and vertical

VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agents

arXiv:2506.21582v5 Announce Type: replace-cross Abstract: Text analytics has traditionally required specialized knowledge in Natural Language Processing (NLP) or text analysis, which presents a barrie

VISD: Enhancing Video Reasoning via Structured Self-Distillation

arXiv:2605.06094v2 Announce Type: replace-cross Abstract: Training VideoLLMs for complex reasoning remains challenging due to sparse sequence level rewards and the lack of fine grained credit assignme

safetyarxiv-cs-ai

Weather-Robust Scene Semantics with Vision-Aligned 4D Radar

arXiv:2605.07367v1 Announce Type: cross Abstract: Cameras and LiDAR degrade in rain, fog, and snow, while millimeter-wave radar remains largely unaffected. We align a radar encoder to frozen SigLIP vi

Tutorials

When Symbol Names Should Not Matter: A Logistic Theory of Fresh-Symbol Classification

arXiv:2605.07120v1 Announce Type: new Abstract: Template tasks have emerged as a clean testbed for asking whether transformers reason with abstract symbols rather than concrete token names. We study t

tutorialsarxiv-cs-lg

Applications

XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction

arXiv:2605.05866v1 Announce Type: cross Abstract: Multiphase powder X-ray diffraction (PXRD) analysis remains a fundamental bottleneck in structure identification, as real-world synthesis often produc

applicationsarxiv-cs-lg

Hardware

XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling

arXiv:2605.06927v1 Announce Type: cross Abstract: Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception f

hardwarearxiv-cs-ai

Hardware

Zero-Shot Neural Network Evaluation with Sample-Wise Activation Patterns

arXiv:2605.07378v1 Announce Type: new Abstract: Zero-shot proxies, also known as training-free metrics, are widely adopted to reduce the computational overhead in neural network evaluation for scenari

hardwarearxiv-cs-lg

A Harmonic Mean Formulation of Average Reward Reinforcement Learning in SMDPs

arXiv:2605.04880v1 Announce Type: new Abstract: Recent research has revived and amplified interest in algorithms for undiscounted average reward reinforcement learning in infinite-horizon, non-episodi

A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning

arXiv:2506.14432v3 Announce Type: replace-cross Abstract: We present FOMO260K, a large-scale, heterogeneous dataset of 260,927 brain Magnetic Resonance Imaging (MRI) scans from 77,589 MRI sessions and

A Universal Space of Brain Dynamics for Unveiling Cognitive Transitions and Individual Differences

arXiv:2605.02936v1 Announce Type: cross Abstract: Representing dynamical systems through data-driven universal spaces has proven effective; however, achieving this universality for human brain activit

researcharxiv-cs-ai

A Workflow-Oriented Framework for Asynchronous Human-AI Collaboration in Hybrid and Compute-Intensive HPC Environments

arXiv:2605.03743v1 Announce Type: cross Abstract: Human involvement is critical in training and deploying AI systems in high-stakes defence and security contexts. However, real-time interaction is imp

local-aiarxiv-cs-ai

AdapShot: Adaptive Many-Shot In-Context Learning with Semantic-Aware KV Cache Reuse

arXiv:2605.03644v1 Announce Type: new Abstract: Many-Shot In-Context Learning (ICL) has emerged as a promising paradigm, leveraging extensive examples to unlock the reasoning potential of Large Langua

researcharxiv-cs-ai

AI-Aided Advancements in Autonomous Underwater Vehicle Navigation

arXiv:2605.04672v1 Announce Type: new Abstract: Autonomous underwater vehicles (AUVs) have become indispensable for deep-sea exploration, spanning critical scientific research and commercial applicati

agentsarxiv-cs-ro

Analogy between Boltzmann machines and Feynman path integrals

arXiv:2301.06217v1 Announce Type: cross Abstract: We provide a detailed exposition of the connections between Boltzmann machines commonly utilized in machine learning problems and the ideas already we

Angle-I2P: Angle-Consistent-Aware Hierarchical Attention for Cross-Modality Outlier Rejection

arXiv:2605.04541v1 Announce Type: new Abstract: Image-to-point-cloud registration (I2P) is a fundamental task in robotic applications such as manipulation,grasping, and localization. Existing deep lea

local-aiarxiv-cs-cv

ARMATA: Auto-Regressive Multi-Agent Task Assignment

arXiv:2605.04225v1 Announce Type: cross Abstract: Coordinating multi-agent systems over spatially distributed areas requires solving a complex hierarchical problem: first distributing areas among agen

agentsarxiv-cs-ro

Autonomous Laparoscope Control through Unified Mechanics-Based Representation of Multimodal Intraoperative Information

arXiv:2605.04408v1 Announce Type: new Abstract: Laparoscope-holding robots can provide surgeons with a stable laparoscopic field of view (FOV) and reduce the burden on human assistants. To maintain an

agentsarxiv-cs-ro

Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO

arXiv:2605.04077v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a central paradigm for improving reasoning and code generation in large language mode

safetyarxiv-cs-cl

Beyond Rigid Geometries: The Spline-Pullback Metric for Universal Diffeomorphic SPD Representation Learning

arXiv:2605.04406v1 Announce Type: new Abstract: The integration of Symmetric Positive Definite (SPD) matrices into deep learning has historically relied on fixed algebraic Riemannian metrics. Analogou

Bridging Modalities: Joint Synthesis and Registration Framework for Aligning Diffusion MRI with T1-Weighted Images

arXiv:2601.11689v2 Announce Type: replace-cross Abstract: Multimodal image registration between diffusion MRI (dMRI) and T1-weighted (T1w) MRI images is a critical step for aligning diffusion-weighted

Building informative materials datasets beyond targeted objectives

arXiv:2605.05104v1 Announce Type: cross Abstract: Materials science data collection can be expensive, making the reuse and long-term utility of datasets critical important for future discovery campaig

Calibrating Tabular Anomaly Detection via Optimal Transport

arXiv:2602.06810v2 Announce Type: replace Abstract: Tabular anomaly detection (TAD) remains challenging due to the heterogeneity of tabular data: features lack natural relationships, vary widely in di

Can LLMs Make (Personalized) Access Control Decisions?

arXiv:2511.20284v2 Announce Type: replace-cross Abstract: Precise access control decisions are crucial for the security of both traditional applications and emerging agent-based systems. Typically, th

Causal discovery under mean independence and linearity

arXiv:2605.04381v1 Announce Type: cross Abstract: Causal discovery methods such as LiNGAM identify causal structure from observational data by assuming mutually independent disturbances. This assumpti

safetyarxiv-cs-lg