All entries

Running the same CNA search on base models (before instruction tuning) yields a structurally similar set of neurons, but ablating them produ…

Running the same CNA search on base models (before instruction tuning) yields a structurally similar set of neurons, but ablating them produces almost no behavioral change. We read this as evidence th

safetynous-research--x

Agents

S-Bus: Automatic Read-Set Reconstruction for Multi-Agent LLM State Coordination

DGX agent

arXiv:2605.17076v1 Announce Type: cross Abstract: Concurrent LLM agents sharing mutable natural-language state produce Structural Race Conditions (SRCs): write-write and cross-shard stale-read conflic

agentsarxiv-cs-ai

19 May 2026

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs

arXiv:2605.18579v1 Announce Type: new Abstract: Pre-training on text-attributed graphs (TAGs) is central to building transferable graph foundation models, where LLM-as-Aligner methods align graph and

safetyarxiv-cs-lg

SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering

arXiv:2605.17526v1 Announce Type: cross Abstract: As autonomous coding agents become capable of handling increasingly long-horizon tasks, they have gradually demonstrated the potential to complete end

SADP: Subgoal-Aware Diffusion Policy for Explainable Robots Learned from Foundation Model Generated Demonstrations

arXiv:2605.16871v1 Announce Type: new Abstract: Explainable robots require not only successful task execution but also the ability to expose internal decision-making process in a user-friendly manner.

safetyarxiv-cs-ro

SAFE-SVD: Sensitivity-Aware Fidelity-Enforcing SVD for Physics Foundation Models

arXiv:2605.17985v1 Announce Type: cross Abstract: We propose a new method for compressing physics foundation models (PFMs) which is a new trend in AI for Science. While model compression is essential

SafeDiffusion-R1: Online Reward Steering for Safe Diffusion Post-Training

arXiv:2605.18719v1 Announce Type: new Abstract: Diffusion models have been widely studied for removing unsafe content learned during pre-training. Existing methods require expensive supervised data, e

safetyarxiv-cs-cv

SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening

model-releasesarxiv-cs-cl

arXiv:2605.17610v1 Announce Type: cross Abstract: The rapid growth of online video platforms and AI-generated content has made reliable video guardrails a key challenge for safety and real-world deplo

Safety Geometry Collapse in Multimodal LLMs and Adaptive Drift Correction

arXiv:2605.18104v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) often fail to transfer safety capabilities learned in the text modality to semantically equivalent non-text inp

safetyarxiv-cs-ai

SAM 2++: Tracking Anything at Any Granularity

model-releasesarxiv-cs-cv

arXiv:2510.18822v4 Announce Type: replace Abstract: Due to the varying granularity of target states across different tasks, most existing trackers are tailored to a single task, which specificity limi

SAME: A Semantically-Aligned Music Autoencoder

arXiv:2605.18613v1 Announce Type: cross Abstract: Latent representations are at the heart of the majority of modern generative models. In the audio domain they are typically produced by a neural-audio

Agents

Same Signal, Different Semantics: A Cross-Framework Behavioral Analysis of Software Engineering Agents

arXiv:2605.18332v1 Announce Type: cross Abstract: Behavioral studies of LLM-based software engineering agents extract operational rules about which trajectory shapes correlate with higher resolution r

agentsarxiv-cs-ai

Sample efficient inductive matrix completion with noise and inexact side information

arXiv:2605.17189v1 Announce Type: cross Abstract: Low-rank matrix completion is a widely studied problem with many variants. Inductive matrix completion (IMC) incorporates row and column side informat

SAMRI: Segment Any MRI

arXiv:2510.26635v3 Announce Type: replace-cross Abstract: Summary: SAMRI is an MRI-specialized adaptation of the Segment Anything Model achieving superior whole-body MRI segmentation, particularly for

local-aiarxiv-cs-cv

SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation

arXiv:2605.17648v1 Announce Type: new Abstract: Generative recommendation treats next-item prediction as autoregressive item-identifier generation. Specifically, items are encoded as semantic identifi

safetyarxiv-cs-ai

SAS: Semantic-aware Sampling for Generative Dataset Distillation

arXiv:2605.18012v1 Announce Type: cross Abstract: Deep neural networks have achieved impressive performance across a wide range of tasks, but this success often comes with substantial computational an

SC3D: Dynamic and Differentiable Causal Discovery for Temporal and Instantaneous Graphs

arXiv:2602.02830v3 Announce Type: replace Abstract: Discovering causal structures from multivariate time series is a key problem because interactions span across multiple lags and possibly involve ins

Scalable Bi-causal Optimal Transport via KL Relaxation and Policy Gradients

arXiv:2605.17271v1 Announce Type: cross Abstract: Bi-causal optimal transport (OT) is a natural framework for comparing and coupling stochastic processes under nonanticipative information constraints,

safetyarxiv-cs-lg

Scalable Decision-Focused Learning through Cost-Sensitive Regression

arXiv:2605.18005v1 Announce Type: new Abstract: Many real-world combinatorial problems involve uncertain parameters, which can be predicted given contextual features and historical data. These `predic

Scalable Environments Drive Generalizable Agents

arXiv:2605.18181v1 Announce Type: new Abstract: Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues that such gene

Scalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured Updates

model-releasesarxiv-cs-lg

arXiv:2605.16686v1 Announce Type: new Abstract: Knowledge editing (KE) provides a lightweight alternative to repeated fine-tuning of LLMs. However, most existing KE methods target dense feed-forward l

Scalable Uncertainty Reasoning in Knowledge Graphs

arXiv:2605.16568v1 Announce Type: new Abstract: Knowledge Graphs are pivotal for semantic data integration. The real-world data they model is often inherently uncertain. Within knowledge graphs, uncer

applicationsarxiv-cs-ai

Agents

Scalable voice agent design with Amazon Nova Sonic: multi-agent, tools, and session segmentation

In this post, you’ll learn how to use Amazon Nova Sonic, Amazon Bedrock AgentCore, and Strands BidiAgent to build scalable, maintainable voice agents that handle these challenges efficiently, resultin

agentsaws-ml-blog

Scale Determines Whether Language Models Organize Representation Geometry for Prediction

arXiv:2605.17084v1 Announce Type: cross Abstract: In language models, what a representation encodes is determined by the geometry of its representation space: distances, not activations, carry meaning

safetyarxiv-cs-cl

Scale-Equivariant Generative Forecasting: Weight-Tied Dilated Convolutions, Wavelet Scattering Inputs, and Spectral-Consistency Training for Self-Similar Time Series

model-releasesarxiv-cs-lg

arXiv:2605.17582v1 Announce Type: new Abstract: Many natural and engineered time series -- equity returns, climate anomalies, turbulent velocities, neural recordings, packet-level network traffic -- a

Scale-Invariant Neural Network Optimization: Norm Geometry and Heavy-Tailed Noise

arXiv:2605.18528v1 Announce Type: cross Abstract: A growing lesson from neural network optimization is that optimizer design should respect how the model is parametrized. Scale-invariant methods becom

researcharxiv-cs-lg

Scales++: Compute Efficient Evaluation Subset Selection with Cognitive Scales Embeddings

arXiv:2510.26384v2 Announce Type: replace Abstract: The prohibitive cost of evaluating large language models (LLMs) on comprehensive benchmarks necessitates the creation of small yet representative da

Scaling Accessible Mathematics on arXiv: HTML Conversion and MathML 4

arXiv:2605.16562v1 Announce Type: new Abstract: We report on the ongoing development of arXiv's HTML Papers offering, available on every new TeX/LaTeX submission since its initial release in 2023. The

researcharxiv-cs-cl

Scaling Laws for Code: A More Data-Hungry Regime

model-releasesarxiv-cs-cl

arXiv:2510.08702v2 Announce Type: replace Abstract: Code Large Language Models (LLMs) are revolutionizing software engineering. However, scaling laws that guide the efficient training are predominantl

SCAR: Self-Supervised Continuous Action Representation Learning

arXiv:2605.16412v1 Announce Type: cross Abstract: Despite the central role of action in embodied intelligence, learning transferable action representations from visual transitions remains a fundamenta

researcharxiv-cs-cv

SCARED-C: Corrected Camera Poses for Endoscopic Depth Estimation

model-releasesarxiv-cs-cv

arXiv:2605.16628v1 Announce Type: new Abstract: The SCARED dataset is a widely used benchmark for endoscopic depth estimation, offering ground-truth 3D reconstructions captured with a structured light

Scenario Generation in Roundabouts with Adjustable Interaction Intensity

arXiv:2605.18026v1 Announce Type: new Abstract: Roundabouts, characterized by frequent merging and yielding interactions, remain a safety-critical corner case for the development and testing of intell

safetyarxiv-cs-ro

Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework

arXiv:2605.18454v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) has recently emerged as a promising approach to solve combinatorial optimization problems such as job shop schedulin

Tutorials

scHelix: Asymmetric Dual-Stream Integration via Explicit Gene-Level Disentanglement

arXiv:2605.18576v1 Announce Type: new Abstract: A critical challenge in single-cell RNA sequencing (scRNA-seq) integration is resolving the tension between eliminating batch effects and maintaining bi

tutorialsarxiv-cs-lg

SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science

arXiv:2605.18630v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as scientific AI as- sistants, and a growing body of benchmarks evaluates their capabilities acro

Scientific Logicality Enriched Methodology for LLM Reasoning: A Practice in Physics

arXiv:2605.17104v1 Announce Type: new Abstract: With the continuous advancement of reasoning abilities in Large Language Models (LLMs), their application to scientific reasoning tasks has gained signi

SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets

arXiv:2605.16620v1 Announce Type: new Abstract: Learning causal relationships between variables from data is a fundamental research area with many applications across disciplines. Most existing causal

SD-Search: On-Policy Hindsight Self-Distillation for Search-Augmented Reasoning

arXiv:2605.18299v1 Announce Type: new Abstract: Search-augmented reasoning agents interleave internal reasoning with calls to an external retriever, and their performance relies on the quality of each

safetyarxiv-cs-ai

SE-GA: Memory-Augmented Self-Evolution for GUI Agents

model-releasesarxiv-cs-lg

arXiv:2605.16883v1 Announce Type: new Abstract: Autonomous Graphical User Interface (GUI) agents often struggle with multi-step tasks due to constrained context windows and static policies that fail t

SeamCam: Quantifying Seamless Camouflage via Multi-Cue Visual Detectability

arXiv:2605.16515v1 Announce Type: new Abstract: Animals are described as effectively camouflaged when they blend seamlessly with their surrounding, yet no standardized quantitative measure of this sea

local-aiarxiv-cs-cv

Industry

Secretary Lutnick met with @SpaceX President and COO @Gwynne_Shotwell. The Commerce Department is focused on dominating supply chains and tr…

Secretary Lutnick of the Commerce Department met with SpaceX President and COO Gwynne Shotwell to discuss supply chain dominance and trade strategy. The meeting reflects the Commerce Department's focu

industryelon-musk--x

SEDD: Scalable and Efficient Dataset Deduplication with GPUs

model-releasesarxiv-cs-cl

arXiv:2501.01046v4 Announce Type: replace Abstract: Dataset deduplication is widely recognized as a crucial preprocessing step that enhances data quality and improves the performance of large language

SEDualVLN: A Spatially-Enhanced Dual-System for Vision-Language Navigation

arXiv:2605.17249v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) approaches have currently followed two primary paradigms: the end-to-end Vision-Language Model (VLM) policy fine-tuned

local-aiarxiv-cs-ro

See Silhouettes in Motion with Neuromorphic Vision

arXiv:2605.17984v1 Announce Type: cross Abstract: Quasi-bimodal objects, such as text, road signs, and barcodes, play a basic yet vital role in daily visual communication. By boiling these down to cle

researcharxiv-cs-cv

See What I Mean: Aligning Vision and Language Representations for Video Fine-grained Object Understanding

arXiv:2605.18018v1 Announce Type: cross Abstract: We present SWIM (See What I Mean), a novel training strategy that aligns vision and language representations to enable fine-grained object understandi

local-aiarxiv-cs-ai

Seeing Together:Multi-Robot Cooperative Egocentric Spatial Reasoning with Multimodal Large Language Models

model-releasesarxiv-cs-cv

arXiv:2605.18431v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have made substantial progress in egocentric video understanding, but their ability to reason cooperatively fro

Seeking the Unfamiliar but Memorable: Conceptual Creativity as Meta-Learning

arXiv:2605.16477v1 Announce Type: cross Abstract: What does it mean to create a new concept, rather than retrieve a familiar one? Repeatedly sampling a generative model at the same prompt produces var

applicationsarxiv-cs-cv

Industry

Seems a lot of autoregressive models will be converted to diffusion models

Emad Mostaque, CEO of Stability AI, suggests that many autoregressive models may transition to or be replaced by diffusion-based approaches. This reflects speculation or prediction about a potential s

industryemad-mostaque--x