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arXiv:2602.18431v2 Announce Type: replace-cross Abstract: Motivated by the problem of assigning mediators to cases in the Kenyan judicial system, we study an online resource allocation problem where i

agentsarxiv-cs-lg

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Local Ai

Smooth Scaling Laws Hide Stepwise Token Learning

DGX agent

arXiv:2606.29858v1 Announce Type: new Abstract: Language model loss follows remarkably regular scaling laws over model and data size, yet it remains unclear why the aggregate loss should exhibit a pow

local-aiarxiv-cs-cl

30 Jun 2026

SoccerNet 2026 Player-Centric Ball Action Spotting: Per-Player Attention with Agreement-Based Ensembling

arXiv:2606.28389v1 Announce Type: new Abstract: We present our submission to the SoccerNet 2026 Player-Centric Ball Action Spotting challenge, which uses a two-stage pipeline: a Track-Aware Action Det

Solver-Integrated Adversarial Attacking and Training of Neural Operators

arXiv:2510.18989v2 Announce Type: replace Abstract: Neural operators are commonly utilized as fast surrogates for numerical solvers in PDE problems, mapping input functions to solution functions. Howe

Applications

Solver-Verified Formulation Generation and Selection for Multi-Warehouse Inventory Allocation Using Large Language Models

arXiv:2606.29366v1 Announce Type: cross Abstract: Balance-oriented multi-warehouse inventory allocation is a recurring decision problem in large-scale e-commerce supply chains, in which a fixed replen

applicationsarxiv-cs-ai

Local Ai

SonoCLIP: Mask-Guided Region-Aware Vision-Language Pretraining for Fetal Ultrasound Analysis

arXiv:2606.29586v1 Announce Type: cross Abstract: Vision-language foundation models have shown strong potential in medical image analysis. Although foundation models for ultrasound imaging have recent

local-aiarxiv-cs-ai

SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport

arXiv:2602.23353v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model

safetyarxiv-cs-ai

SP-CACW: Convergence-Aware Client Weighting for Selfish Personalized Learning

arXiv:2606.29322v1 Announce Type: new Abstract: Collaborative learning is sustainable only when it benefits each participant. Standard federated learning optimizes a global average objective, which ca

safetyarxiv-cs-lg

SPACE: Swarm Pheromone Fields for Adaptive Collision-Aware Exploration

arXiv:2606.29372v1 Announce Type: new Abstract: Massive robot swarms can explore unknown environments quickly, but adding robots eventually stops helping. Doorways and dense traffic create congestion,

safetyarxiv-cs-ro

Hardware

Spanning the Visual Analogy Space with a Weight Basis of LoRAs

arXiv:2602.15727v2 Announce Type: replace-cross Abstract: Visual analogy learning enables image editing via demonstration rather than textual description, allowing users to specify complex transformat

hardwarearxiv-cs-ai

SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence

arXiv:2412.04739v2 Announce Type: replace Abstract: Deep learning models exhibit fairness concerns when predictions are inadvertently influenced by sensitive attributes. However, existing attempts to

safetyarxiv-cs-cv

SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning

arXiv:2602.02472v2 Announce Type: replace-cross Abstract: Progressive Learning (PL) reduces pre-training computational overhead by gradually increasing model scale. While prior work has extensively ex

researcharxiv-cs-cl

Sparse Autoencoders are Capable LLM Jailbreak Mitigators

arXiv:2602.12418v2 Announce Type: replace-cross Abstract: Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering (CC-Delta), an SAE-

SparsePixels: Efficient Convolution for Sparse Data on FPGAs

arXiv:2512.06208v3 Announce Type: replace-cross Abstract: Inference of standard convolutional neural networks (CNNs) on FPGAs often incurs high latency and a long initiation interval due to the deep n

Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference

model-releasesarxiv-cs-lg

arXiv:2510.08762v2 Announce Type: replace Abstract: Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, t

Applications

Spatially Localized Image Degradation Embeddings for Image Quality Assessment

arXiv:2606.29162v1 Announce Type: new Abstract: Self-supervised learning (SSL) currently drives state-of-the-art performance in no-reference image quality assessment (NR-IQA). However, standard SSL pi

applicationsarxiv-cs-cv

SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference

arXiv:2602.20610v3 Announce Type: replace-cross Abstract: Specifications are vital for ensuring program correctness, yet writing them manually remains challenging and time-intensive. Recent large lang

Spectral Embedding via Chebyshev Bases for Robust DeepONet Approximation

model-releasesarxiv-cs-lg

arXiv:2512.09165v2 Announce Type: replace Abstract: Deep Operator Networks (DeepONets) have emerged as a powerful framework for data-driven operator learning, providing flexible surrogates for nonline

Local Ai

Spectral Perturbation of the Empirical Fisher Information Matrix under Weight Quantization

arXiv:2606.28432v1 Announce Type: cross Abstract: We study the spectral perturbation of the empirical Fisher Information Matrix (FIM) of a parametric statistical model under two structured perturbatio

local-aiarxiv-cs-ai

Spectral phase transitions and trainability in neural network learning dynamics

arXiv:2606.28486v1 Announce Type: cross Abstract: The emergence of low-dimensional structures in the spectra of neural network weight matrices is a common empirical feature of trained models, but the

safetyarxiv-cs-lg

Speculative Pre-Positioning: Decoding Stateful Sessions to the Next Decision Point Off the Critical Path

arXiv:2606.29565v1 Announce Type: new Abstract: A stateless inference server (vLLM, SGLang, TensorRT-LLM) idles between requests while the accelerator waits; a stateful session reclaims that idle time

Sphere-VIO: Fast and Robust Visual-Inertial Odometry via Unified Spherical Representation for Heterogeneous Multi-Camera Systems

arXiv:2606.29910v1 Announce Type: new Abstract: Multi-camera visual-inertial odometry (VIO) overcomes the inherent limitations of pure visual systems by expanding the field of view. However, existing

safetyarxiv-cs-ro

SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows

model-releasesarxiv-cs-ai

arXiv:2606.29955v1 Announce Type: cross Abstract: Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making. However, most existing spreadsheet benchmarks

SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models

model-releasesarxiv-cs-cl

arXiv:2606.29815v1 Announce Type: new Abstract: Evaluating code large language models (Code LLMs) requires reliable detection of data leakage, where benchmark performance is artificially inflated by e

SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models

arXiv:2510.12784v2 Announce Type: replace-cross Abstract: Recently, remarkable progress has been made in Unified Multimodal Models (UMMs), which integrate vision-language generation and understanding

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization

arXiv:2510.04961v2 Announce Type: replace Abstract: Tokenizers are a key component of state-of-the-art generative image models, extracting the most important features from the signal while reducing da

Hardware

SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces

arXiv:2403.07711v5 Announce Type: replace-cross Abstract: Given the remarkable achievements in image generation through diffusion models, the research community has shown increasing interest in extend

hardwarearxiv-cs-ai

Stability and Concentration in Nonlinear Inverse Problems with Block-Structured Parameters: Lipschitz Geometry, Identifiability, and an Application to Gaussian Splatting

model-releasesarxiv-cs-cv

arXiv:2602.09415v2 Announce Type: replace Abstract: We develop an operator-theoretic framework for stability and statistical concentration in nonlinear inverse problems with block-structured parameter

Stability Boundaries and Motor Performance in Delayed Robot-Mediated Dyadic Interactions

arXiv:2510.14511v3 Announce Type: replace Abstract: This paper establishes analytical stability boundaries for robot-mediated human-human (dyadic) interaction systems, subject to haptic communication

researcharxiv-cs-ro

Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping

arXiv:2606.29983v1 Announce Type: cross Abstract: Looped Transformers, which repeatedly apply a shared transformer block, are an architecturally natural fit for variable-length algorithmic tasks. Alth

researcharxiv-cs-ai

StackingNet: Collective Inference Across Independent AI Foundation Models

arXiv:2602.13792v2 Announce Type: replace Abstract: Artificial intelligence built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems r

safetyarxiv-cs-ai

Staged Hybridisation for Visual Quantum Reinforcement Learning via Knowledge Distillation

arXiv:2606.30520v1 Announce Type: cross Abstract: Visual environments are a demanding setting for quantum reinforcement learning (QRL): high-dimensional observations, unstable RL optimisation, and con

safetyarxiv-cs-lg

StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley

model-releasesarxiv-cs-ai

arXiv:2507.07445v3 Announce Type: replace Abstract: Autonomous agents navigating human society must master both production activities and social interactions, yet existing benchmarks rarely evaluate t

Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models

model-releasesarxiv-cs-ai

arXiv:2606.29091v1 Announce Type: cross Abstract: Tabular foundation models cannot reason about data produced by running systems without access to the rules that govern them. We make this statement fa

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging

model-releasesarxiv-cs-cv

arXiv:2512.01461v2 Announce Type: replace-cross Abstract: Model merging has emerged as a promising paradigm for enabling multi-task capabilities without additional training. However, traditional basic

STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning

arXiv:2606.29834v1 Announce Type: new Abstract: Real-world robot learning increasingly relies on heterogeneous data, but demonstrations and rollouts often mix useful progress with stalls, corrections,

safetyarxiv-cs-ro

Steerable Visual Representations

arXiv:2604.02327v2 Announce Type: replace-cross Abstract: Pretrained Vision Transformers (ViTs) such as DINOv2 and MAE provide generic image features that can be applied to a variety of downstream tas

researcharxiv-cs-ai

STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy

model-releasesarxiv-cs-lg

arXiv:2606.29592v1 Announce Type: new Abstract: A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever

StereoGS: Sparse-View 3D Gaussian Splatting via Stereo Priors

arXiv:2606.30545v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has achieved remarkable success in real-time novel view synthesis, yet it suffers from severe overfitting under sparse-view

Stimulus Motion Perception Studies Imply Specific Neural Computations in Human Visual Stabilization

arXiv:2506.13506v3 Announce Type: replace Abstract: Even during fixation the human eye is constantly in low amplitude motion, jittering over small angles in random directions at up to 100Hz. This moti

Local Ai

Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models

arXiv:2506.20771v2 Announce Type: replace Abstract: We propose a latent score-based generative AI framework for learning stochastic, non-local closure models and constitutive laws in nonlinear dynamic

local-aiarxiv-cs-lg

Stochastic Optimal Control Sampling for Diffusion Inverse Problems

arXiv:2606.28785v1 Announce Type: new Abstract: Benefiting from the strong ability to capture data distributions, diffusion models have become powerful tools for solving image inverse problems. The ke

StrucTab: A Structured Optimization Framework for Table Parsing

model-releasesarxiv-cs-cv

arXiv:2606.29905v1 Announce Type: new Abstract: Table parsing aims to convert table images into structured, machine-readable representations, a task requiring the joint perception of complex spatial l

Structural Certification for Reliable Physical Design with Language Models

arXiv:2606.30107v1 Announce Type: new Abstract: An unreliable language model can be made to produce reliable physical designs if the authority to assert is moved out of the model: the model proposes,

researcharxiv-cs-ai

Structure-Preserving Document Translation via Multi-Stage LLM Pipeline: A Case Study in Marathi

arXiv:2606.28796v1 Announce Type: new Abstract: Government documents in India are predominantly issued in regional languages such as Marathi, creating substantial accessibility barriers for non-native

Structured Proper Loss Geometries for Multiclass Classification: Theory and Controlled Empirical Evaluation

arXiv:2606.29471v1 Announce Type: new Abstract: Strictly proper scoring rules identify the true conditional class distribution at population level, but their curvature can alter optimization and finit

Applications

SUMO: Segment and Track Any Motion with Nonlinear State Space Models

arXiv:2606.29861v1 Announce Type: cross Abstract: Visual Object Tracking (VOT) and Moving Object Segmentation (MOS) are two fundamental tasks in computer vision that involve both spatial and temporal

applicationsarxiv-cs-ai

Supporting Workflow Reproducibility by Linking Bioinformatics Tools across Papers and Executable Code

arXiv:2603.08195v2 Announce Type: replace Abstract: Motivation: The rapid growth of biological data has intensified the need for transparent, reproducible, and well-documented computational workflows.

researcharxiv-cs-cl