All entries

SAVER: Stochastic Adaptive Variance-Driven Exploration and Reconstruction for Low-Dose Computed Tomography

arXiv:2607.03761v1 Announce Type: new Abstract: Computed Tomography (CT) is indispensable in clinical diagnostics, yet minimizing radiation dose without compromising image quality remains a critical c

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

Saving GPU Hours in LLM Inference System Development and Online Workloads with Simulation and DBMS-Inspired Cache Replacement Policies

DGX agent

arXiv:2411.07447v5 Announce Type: replace-cross Abstract: LLMs are increasingly used world-wide from daily tasks to agentic systems and data analytics, requiring significant GPU resources. While LLM i

safetyarxiv-cs-ai

7 Jul 2026

Scalable Cross-Attention Transformer for Cooperative Multi-AP OFDM Uplink Reception

arXiv:2602.04728v3 Announce Type: replace-cross Abstract: We propose a cross-attention Transformer for joint decoding of uplink OFDM signals received by multiple coordinated access points. A shared pe

Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions

arXiv:2602.07341v2 Announce Type: replace Abstract: This paper focuses on the scalable robot learning for manipulation in the dexterous robot arm-hand systems, where the remote human-robot interaction

Scalable Differentially Private Data Compression via Diffusion and Stochastic Codes

arXiv:2607.03392v1 Announce Type: cross Abstract: The ever-increasing collection of personal data has created mounting pressure to develop technologies that protect sensitive aspects of individual ide

Scalable Maximal Frequent Episode Mining with Desbordante

arXiv:2607.03188v1 Announce Type: cross Abstract: Episode mining aims to extract subsequences of events that possess certain distinctive properties and constitute facts valuable to the user. Maximal f

Scalable Semantic Steering of Embedding Projections

arXiv:2607.03978v1 Announce Type: cross Abstract: Low-dimensional projections support interactive visual analysis of high-dimensional data embeddings, but their structure often does not align with ana

Hardware

Scaling Weisfeiler-Leman Expressiveness Analysis to Massive Graphs with GPUs

arXiv:2607.02603v1 Announce Type: cross Abstract: The stable coloring of the Weisfeiler-Leman (1-WL) test is a cornerstone of Graph Neural Networks because it provides an upper bound to the expressive

hardwarearxiv-cs-lg

Model Releases

SceneFrom3D: Geometry-Conditioned Outdoor 3D Scene Generation via View Scheduling with Object-Level Control

model-releasesarxiv-cs-cv

arXiv:2607.04540v1 Announce Type: cross Abstract: Geometry-conditioned 3D scene generation enables the creation of 3D environments from user-provided geometry, offering direct control over scene struc

Schedulable Job-Level Dependencies for Cause-Effect Chains via Graph Neural Networks

arXiv:2607.02624v1 Announce Type: cross Abstract: Modern automotive software architectures comprise large sets of mixed-criticality functions executing on shared multi-core platforms with strict real-

Score Distributions, Not Cells: Evaluating Single-Cell Perturbations Under Class Overlap

arXiv:2607.04595v1 Announce Type: new Abstract: Most classification problems assume the classes are roughly separable, so that an individual sample can usually be assigned to one class. Single-cell pe

Score-Regularized Joint Sampling with Importance Weights for Flow Matching

arXiv:2511.17812v3 Announce Type: replace-cross Abstract: Flow matching models effectively represent complex distributions, yet estimating expectations of functions of their outputs remains challengin

Industry

SCOTUS lets Texas enforce app store law that Big Tech calls 'censorship regime'

The Supreme Court allowed Texas to continue enforcing a law requiring app stores to verify buyers' ages and obtain parental consent for minors to download apps and purchase paid content. Challengers a

industryars-technica

Applications

SE-UNet: Singular Equivariant Imaging for Real-World Constrained Generation

arXiv:2607.02628v1 Announce Type: new Abstract: While diffusion models have revolutionized image synthesis, their application to real-world inverse problems is often hampered by the need for massive d

applicationsarxiv-cs-cv

SEAM: Smooth Execution of Action-Chunked Motion for Vision-Language-Action Policies

arXiv:2607.04609v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies that execute fixed-length action chunks can exhibit multimodal bifurcation: a cross-chunk inconsistency in which a

safetyarxiv-cs-ro

Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation

arXiv:2607.05382v1 Announce Type: cross Abstract: Visual generators excel at rendering, but they confidently fabricate what they do not know. User requests are unbounded, evolving, and deeply long-tai

agentsarxiv-cs-ai

Securing Multi-Tool AI Agent Chains With Dynamic, Real-Time Compositional Policies

arXiv:2607.03423v1 Announce Type: cross Abstract: Modern AI agent implementations such as frontier coding agents chain multiple tools at runtime that create a security surface that per-tool guardrails

Model Releases

Seduced by the Narrative: Assessing Rule Adherence in Semi-Open Textual Sandboxes

model-releasesarxiv-cs-ai

arXiv:2607.02802v1 Announce Type: cross Abstract: As LLMs are increasingly deployed as autonomous adjudicators in semi-open textual game environments, robust rule adherence becomes critical when user

See the Emotion: A Facial Emoji Proxy Modeling for EEG Emotion Recognition

arXiv:2607.02912v1 Announce Type: new Abstract: Despite the high accuracy of EEG-based emotion recognition, existing models remain opaque 'black boxes', lacking semantic grounding between abstract neu

researcharxiv-cs-cv

Seeing Once is Enough? Online Geometry-Aware Token Pruning for 3D Question Answering

arXiv:2607.04079v1 Announce Type: cross Abstract: Recent Multi-modal Large Language Models (MLLMs) have demonstrated remarkable performance on 2D question answering tasks. However, extending these mod

Tutorials

Seeing Through WiFi: Lightweight Human Pose Estimation with Dynamic Kernel Attention

arXiv:2607.03196v1 Announce Type: new Abstract: WiFi-based human pose estimation (HPE) enables the detection and interpretation of human body positions and movements without the need for wearable devi

tutorialsarxiv-cs-cv

SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering

arXiv:2607.04163v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual understanding tasks such as image captioning and visual question answ

Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration

arXiv:2606.12913v2 Announce Type: replace-cross Abstract: The rapid growth of modern training datasets has significantly increased computational cost, motivating dataset pruning~(DP) methods which ret

researcharxiv-cs-cv

Selective Disclosure Watermarking for Large Language Models

arXiv:2607.05353v1 Announce Type: cross Abstract: Watermarking methods embed imperceptible and verifiable signals into text generated by large language models (LLMs). Existing approaches include zero-

Model Releases

Selective Mask Propagation for Multi-Object Tracking

model-releasesarxiv-cs-cv

arXiv:2606.13033v2 Announce Type: replace Abstract: Multi-object tracking has a heavy-tailed difficulty distribution: most frames are easy for a lightweight base tracker, while a small fraction are in

Selectivity Estimation for Linear Queries via Online Learning

arXiv:2607.02895v1 Announce Type: cross Abstract: Learning-based approaches for selectivity estimation in databases have gained significant traction in recent years. However, theoretical studies of th

Industry

Self driving humanoids will have a far bigger impact than self driving cars.

Emad Mostaque argues that autonomous humanoid robots will have greater societal and economic impact than autonomous vehicles due to their broader applicability across multiple industries and tasks. Th

industryemad-mostaque--x

Self-Improving Diffusion Classifiers with Minority Preference Optimization

arXiv:2607.03770v1 Announce Type: cross Abstract: Prior studies have demonstrated that diffusion classifiers achieve robust zero-shot classification performance. However, their effectiveness is strong

Self-Reference in Large Language Models: The Introspection Threshold for Recursive Self-Improvement

arXiv:2607.04277v1 Announce Type: cross Abstract: The pursuit of self-evolving AI raises a critical question: when is autonomous self-improvement sustainable rather than degenerative? Drawing an analo

Self-Specializing Vision-Language Transmon Chip Calibration in a Physics-Grounded Environment

arXiv:2607.03193v1 Announce Type: cross Abstract: Calibrating a superconducting transmon chip is a sequential decision problem under noise, drift, and a finite budget: an expert must choose experiment

agentsarxiv-cs-ai

Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration

arXiv:2509.10656v2 Announce Type: replace-cross Abstract: For groups of autonomous agents to achieve a particular goal, they must engage in coordination and long-horizon reasoning. Rather than relying

agentsarxiv-cs-ai

Self-Supervised Learning from Structural Invariance

arXiv:2602.02381v2 Announce Type: replace Abstract: Joint-embedding self-supervised learning (SSL), the key paradigm for unsupervised representation learning from visual data, learns from invariances

SelfMem: Self-Optimizing Memory for AI Agents

arXiv:2607.03726v1 Announce Type: new Abstract: While current AI agents support increasingly long context windows, tool use, and skill execution for long-horizon tasks, they still require memory syste

agentsarxiv-cs-cl

Semantic-Guided Progressive Object Removal with Gaussian Splatting

arXiv:2607.04144v1 Announce Type: new Abstract: Removing unwanted objects from reconstructed 3D scenes is an important task in computer vision, supporting applications in AR/VR, robotics, and digital

researcharxiv-cs-ro

Semantic Homogenization in Italian Popular Music: A Diachronic Analysis

arXiv:2607.04832v1 Announce Type: new Abstract: In recent years, studies have revealed a decline in semantic variety across popular music lyrics, particularly in English-language songs on streaming pl

researcharxiv-cs-cl

Local Ai

Semantic Integration and Lexical Expectation Shape N400 and P600 Dynamics During Naturalistic Reading

arXiv:2607.04107v1 Announce Type: new Abstract: Word surprisal is a well-established computational predictor of human neural responses during language comprehension, but it remains less clear whether

local-aiarxiv-cs-cl

Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images

arXiv:2607.03253v1 Announce Type: cross Abstract: As hematoxylin & eosin (H&E) staining constitutes the primary entry point in routine diagnostic workflows, computer-aided diagnosis from whole-slide H

Semantic Video Communication via Multi-Scale Convolution and Dynamic Routing for Next-Generation Networks

arXiv:2607.05093v1 Announce Type: new Abstract: The exponential growth of video traffic demands novel semantic communication paradigms that transmit meaning rather than raw bits. We present a generati

safetyarxiv-cs-cv

Applications

Semi-parametric Functional Classification via Path Signatures Logistic Regression with Adaptive Order Selection

arXiv:2507.06637v2 Announce Type: replace-cross Abstract: We propose Path Signatures Logistic Regression (PSLR), a semi-parametric framework for classifying vector-valued functional data with scalar c

applicationsarxiv-cs-lg

Semidefinite programming relaxations and debiasing for MAXCUT-based clustering

arXiv:2401.10927v3 Announce Type: replace-cross Abstract: In this paper, we consider the problem of partitioning a small data sample of size n drawn from a mixture of 2 sub-gaussian distributions in R

Sensitivity Sampling with Predictions for k-Means Clustering

arXiv:2607.04949v1 Announce Type: new Abstract: We study the problem of k-means clustering on large datasets. The state-of-the-art for the problem is given by coresets-based approaches, which build sm

Separating Representation from Reconstruction Enables Scalable Text Encoders

arXiv:2607.04011v1 Announce Type: cross Abstract: While decoders have rapidly scaled, encoders have remained largely unchanged since BERT. We revisit this disparity by frozen backbone evaluation via p

Sequential Cohort Selection under Uncertainty

arXiv:2508.16386v2 Announce Type: replace Abstract: We study the problem of fair cohort selection under uncertainty, motivated by university admissions where applicant outcomes are only partially obse

Sequential Correlations Change In-Context Learning: Effective Context Length and Architectural Mismatch

arXiv:2607.03660v1 Announce Type: cross Abstract: Modern sequence models have a striking capacity for in-context learning (ICL); they can perform new tasks based only on examples given in the prompt.

Serious Games: Human-AI Interaction, Evolution, and Coevolution

arXiv:2505.16388v2 Announce Type: replace Abstract: The serious games between humans and AI have only just begun. Evolutionary Game Theory (EGT) models the competitive and cooperative strategies of bi

Server-side Anti-cheat in FPS games for Aimbot detection using Deep learning and Machine learning

arXiv:2607.04336v1 Announce Type: new Abstract: Modern video games are becoming more complex day by day. Most of these modern games are multiplayer first-person shooter (FPS) games. The rising popular

SGF-CDNet: A Consistency-Discrepancy Graph Network over Semantic-Geometric Fused Nodes for Face Forgery Detection

arXiv:2607.03883v1 Announce Type: new Abstract: The rapid advancement of deepfakes necessitates robust face forgery detection. Although forged faces may lack obvious artifacts, they often contain subt

researcharxiv-cs-cv

Shapley-based Data Valuation for LLM Alignment via Sequential Preference Optimization

arXiv:2512.15765v3 Announce Type: replace Abstract: Data valuation is a natural framework for understanding which preference datasets matter most when aligning a Large Language Model (LLM) using multi