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SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions

arXiv:2605.12792v1 Announce Type: new Abstract: There is recently a serious issue that Deep Neural Networks (DNNs) training uses more and more unauthorized data. A clean-label generalization attack, o

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SP-GCRL: Influence Maximization on Incomplete Social Graphs

DGX agent

arXiv:2605.12513v1 Announce Type: cross Abstract: Influence maximization (IM) in real platforms is challenged by incomplete, noisy social graphs and non-stationary diffusion dynamics. We propose SP-GC

safetyarxiv-cs-ai

14 May 2026

Tutorials

Sparse Code Uplifting for Efficient 3D Language Gaussian Splatting

arXiv:2605.13600v1 Announce Type: new Abstract: 3D Language Gaussian Splatting (3DLGS) augments 3D Gaussian Splatting with language-aligned visual features for open-vocabulary 3D scene understanding.

tutorialsarxiv-cs-cv

SpatialReward: Bridging the Perception Gap in Online RL for Image Editing via Explicit Spatial Reasoning

model-releasesarxiv-cs-cv

arXiv:2602.07458v4 Announce Type: replace Abstract: Online Reinforcement Learning (RL) offers a promising avenue for complex image editing but is currently constrained by the scarcity of reliable and

Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks

arXiv:2605.13566v1 Announce Type: new Abstract: Land Surface Temperature (LST) is a key variable for various applications, such as urban climate and ecology studies. Yet, existing satellite-derived LS

Spectral Energy Centroid: a Metric for Improving Performance and Analyzing Spectral Bias in Implicit Neural Representations

arXiv:2605.12709v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) model continuous signals using multilayer perceptrons (MLPs), enabling compact, differentiable, and high-fidelity

Spectral Flattening Is All Muon Needs: How Orthogonalization Controls Learning Rate and Convergence

arXiv:2605.13079v1 Announce Type: cross Abstract: Muon orthogonalizes the momentum buffer before each update, replacing its singular values with ones via Newton-Schulz iterations. This simple change l

SpectralTrain: A Universal Framework for Hyperspectral Image Classification

arXiv:2511.16084v2 Announce Type: replace-cross Abstract: Hyperspectral image (HSI) classification typically involves large-scale data and computationally intensive training, which limits the practica

SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting

model-releasesarxiv-cs-lg

arXiv:2605.12992v1 Announce Type: cross Abstract: Neural population models, which predict the joint firing of many simultaneously recorded neurons forward in time, are typically evaluated by a single

SPOT: Selective Prompt Projection via Total Variation for Inference-Only Safe Text-to-Image Generation

arXiv:2602.00616v3 Announce Type: replace Abstract: Text-to-Image (T2I) diffusion models enable high quality open ended synthesis, but practical use requires suppressing unsafe generations while prese

SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification

model-releasesarxiv-cs-cv

arXiv:2605.13672v1 Announce Type: new Abstract: Few-shot classification (FSC) is widely used for learning from limited labeled data, yet most evaluations implicitly assume that target concepts are ind

SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecasting

arXiv:2605.12550v1 Announce Type: cross Abstract: Large vision models (LVMs) have recently proven to be surprisingly effective time series forecasters, simply by rendering temporal data as images. Thi

applicationsarxiv-cs-ai

Stable Attention Response for Reliable Precipitation Nowcasting

arXiv:2605.13181v1 Announce Type: cross Abstract: Precipitation nowcasting remains challenging due to the highly localized, rapidly evolving, and heterogeneous nature of atmospheric dynamics. Although

STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition

arXiv:2605.13202v1 Announce Type: cross Abstract: Few-shot action recognition (FSAR) requires models to generalize to novel action categories from only a handful of annotated samples. Despite progress

Agents

State-Centric Decision Process

arXiv:2605.12755v1 Announce Type: new Abstract: Language environments such as web browsers, code terminals, and interactive simulations emit raw text rather than states, and provide none of the runtim

agentsarxiv-cs-ai

State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives

arXiv:2605.13127v1 Announce Type: cross Abstract: Determinantal point processes (DPPs) have emerged as a kernelized alternative to vanilla independent sampling for generating efficient minibatches, co

State-Space NTK Collapse Near Bifurcations

model-releasesarxiv-cs-lg

arXiv:2605.12763v1 Announce Type: new Abstract: Rich feature learning in tasks that unfold over time often requires the model to pass through bifurcations, constituting qualitative changes in the unde

Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts

arXiv:2605.12890v1 Announce Type: cross Abstract: The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-written text. W

Agents

Still Camouflage, Moving Illusion: View-Induced Trajectory Manipulation in Autonomous Driving

arXiv:2605.12743v1 Announce Type: cross Abstract: Existing physical adversarial attacks on vision-based autonomous driving induce time-evolving perception errors, including biased object tracking or t

agentsarxiv-cs-cv

Stochastic Dimension-Free Zeroth-Order Estimator for High-Dimensional and High-Order PINNs

model-releasesarxiv-cs-lg

arXiv:2603.24002v2 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) for high-dimensional and high-order partial differential equations (PDEs) are primarily constrained by the

STORM: Segment, Track, and Object Re-Localization from a Single Image

arXiv:2511.09771v3 Announce Type: replace Abstract: Accurate 6D pose estimation and tracking are core capabilities for physical AI systems, yet real-world deployment remains brittle and labor-intensiv

applicationsarxiv-cs-cv

Strategic PAC Learnability via Geometric Definability

arXiv:2605.13426v1 Announce Type: new Abstract: Strategic classification studies learning settings in which individuals can modify their features, at a cost, in order to influence the classifier's dec

StreamGaze: Gaze-Guided Temporal Reasoning and Proactive Understanding in Streaming Videos

arXiv:2512.01707v3 Announce Type: replace-cross Abstract: Streaming video understanding requires models not only to process temporally incoming frames, but also to anticipate user intention for realis

Stress-Testing the Reasoning Competence of LLMs With Proofs Under Minimal Formalism

arXiv:2605.12524v1 Announce Type: cross Abstract: We introduce ProofGrid, a benchmark suite for evaluating LLM reasoning through machine-checkable proofs rather than final answers alone. ProofGrid con

Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning

arXiv:2605.13153v1 Announce Type: new Abstract: Temporal Knowledge Graph Reasoning (TKGR) aims at inferring missing (especially future) events from historical data. Current evaluation in TKGR uniforml

Structural Diversity Drives Disruptive Scientific Innovation

arXiv:2605.12514v1 Announce Type: cross Abstract: Scientific innovation increasingly depends on collaboration, yet the organizational structure that fosters breakthrough ideas remains poorly understoo

safetyarxiv-cs-cv

Stylized Text-to-Motion Generation via Hypernetwork-Driven Low-Rank Adaptation

arXiv:2605.13333v1 Announce Type: cross Abstract: Text-driven motion diffusion models are capable of generating realistic human motions, but text alone often struggles to express fine-level nuances of

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling

arXiv:2602.23013v3 Announce Type: replace Abstract: Detecting visual anomalies in industrial inspection often requires training with only a few normal images per category. Recent few-shot methods achi

researcharxiv-cs-cv

SupChain-Bench: Benchmarking Large Language Models for Real-World Supply Chain Management

arXiv:2602.07342v2 Announce Type: replace Abstract: Large language models (LLMs) have shown promise in complex reasoning and tool-based decision making, motivating their application to real-world supp

Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis

arXiv:2605.13312v1 Announce Type: new Abstract: We present Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework for integrative brain network analysis that generalizes S

Support-Conditioned Flow Matching Is Kernel Smoothing

arXiv:2605.13386v1 Announce Type: new Abstract: Generative models are often conditioned on a small set of examples via cross-attention. Under the Gaussian optimal-transport path, we show that the exac

Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks

arXiv:2603.29475v2 Announce Type: replace Abstract: Survival analysis is crucial for many medical applications, but remains challenging for modern machine learning due to limited data, censoring, and

applicationsarxiv-cs-lg

Sustaining AI safety: Control-theoretic external impossibility, intrinsic necessity, and structural requirements

arXiv:2605.12963v1 Announce Type: new Abstract: As AI systems become increasingly capable, safety strategies must be evaluated not only by how much they reduce present risk, but by whether they could

Switching Successor Measures for Hierarchical Zero-shot Reinforcement Learning

arXiv:2605.13207v1 Announce Type: new Abstract: Hierarchical reinforcement learning can improve generalization by decomposing long-horizon decision-making into simpler subproblems. However, existing a

SymbolSight: Minimizing Inter-Symbol Interference for Reading with Prosthetic Vision

arXiv:2601.17326v2 Announce Type: replace Abstract: Retinal prostheses restore limited visual perception, but low spatial resolution and temporal persistence make reading difficult. In sequential lett

researcharxiv-cs-cv

SynCABEL: Synthetic Contextualized Augmentation for Biomedical Entity Linking

arXiv:2601.19667v2 Announce Type: replace-cross Abstract: We present SynCABEL (Synthetic Contextualized Augmentation for Biomedical Entity Linking), a framework that addresses a central bottleneck in

Systematic Failures in Collective Reasoning under Distributed Information in Multi-Agent LLMs

arXiv:2505.11556v4 Announce Type: replace-cross Abstract: Multi-agent systems built on large language models (LLMs) are expected to enhance decision-making by pooling distributed information, yet syst

T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation

arXiv:2506.12075v2 Announce Type: replace-cross Abstract: High school English Literature teachers often encounter barriers to assembling diverse, thematically aligned text sets due to limited planning

Table-R1: Region-based Reinforcement Learning for Table Understanding

arXiv:2505.12415v3 Announce Type: replace-cross Abstract: Tables present unique challenges for language models due to their structured row-column interactions, necessitating specialized approaches for

Taming the Long Tail: Rebalancing Adversarial Training via Adaptive Perturbation

arXiv:2605.13395v1 Announce Type: cross Abstract: Deep neural networks are highly vulnerable to adversarial examples, i.e.,small perturbations that can significantly degrade model performance. While a

applicationsarxiv-cs-cv

Teacher-Guided Policy Optimization for LLM Distillation

arXiv:2605.13230v1 Announce Type: cross Abstract: The convergence of reinforcement learning and imitation learning has positioned Reverse KL (RKL) as a promising paradigm for on-policy LLM distillatio

Teaching and Learning under Deductive Errors

arXiv:2605.13384v1 Announce Type: new Abstract: Most models of machine teaching and learning assume the learner makes no errors in its internal deductive inference. However, humans and large language

TeleGate: Whole-Body Humanoid Teleoperation via Gated Expert Selection with Motion Prior

arXiv:2602.09628v2 Announce Type: replace Abstract: Real-time whole-body teleoperation is a critical method for humanoid robots to perform complex tasks in unstructured environments. However, developi

safetyarxiv-cs-ro

Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment

arXiv:2605.13537v1 Announce Type: cross Abstract: Inference-time alignment techniques offer a lightweight alternative or complement to costly reinforcement learning, while enabling continual adaptatio

Test-time Offline Reinforcement Learning on Goal-related Experience

arXiv:2507.18809v2 Announce Type: replace Abstract: Foundation models compress a large amount of information in a single, large neural network, which can then be queried for individual tasks. There ar

Test-time Sparsity for Extreme Fast Action Diffusion

arXiv:2605.13316v1 Announce Type: new Abstract: Action diffusion excels at high-fidelity action generation but incurs heavy computational costs owing to its iterative denoising nature. Despite current

safetyarxiv-cs-cv

The Bayesian Origin of the Probability Weighting Function in Human Representation of Probabilities

arXiv:2510.04698v3 Announce Type: replace-cross Abstract: Humans systematically misrepresent probability in a stereotyped inverse-S pattern. It has been documented for decades, but its origin remains

The critical slowing down in diffusion models

arXiv:2605.12597v1 Announce Type: cross Abstract: Computational sampling has been central to the sciences since the mid-20th century. While machine-learning-based approaches have recently enabled majo