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SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery

arXiv:2602.00953v2 Announce Type: replace Abstract: Engineered image-based biomarkers offer a clinically interpretable alternative to black-box AI in computational pathology, yet their discovery remai

agentsarxiv-cs-lg

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SAID: Safety-Aware Intent Defense via Prefix Probing for Large Language Models

DGX agent

arXiv:2510.20129v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) remain vulnerable to jailbreak attacks, where adversarially crafted prompts induce policy-violating responses des

safetyarxiv-cs-ai

12 May 2026

SalesSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators

arXiv:2605.08334v1 Announce Type: new Abstract: We present SalesSim, a framework and testbed for evaluating the ability of Multimodal Large Language Models (MLLMs) to simulate realistic, persona-drive

safetyarxiv-cs-cl

Sam Altman says Elon Musk’s mind games were damaging OpenAI

OpenAI CEO Sam Altman says Elon Musk did 'huge damage' to the culture of the AI startup. During testimony as part of Musk's lawsuit against OpenAI, Altman said Musk required OpenAI president Greg Broc

industrythe-verge-ai

Sam Altman swearing to tell the whole truth, and then failing to do so. May 2023.

Sam Altman made statements under oath in May 2023 regarding AI safety and OpenAI's practices, but Gary Marcus critiqued these statements as incomplete or misleading, suggesting Altman failed to fully

safetygary-marcus--x

Sam Altman takes the stand in trial against Elon Musk

OpenAI CEO Sam Altman has begun his testimony against Elon Musk in a high-profile jury trial in a California federal courtroom. Altman, alongside OpenAI president Greg Brockman, is a primary defendant

industrythe-verge-ai

Sam Altman was winning on the stand, but it might not be enough

After two weeks of hearing from assorted witnesses that he was a lying snake, the jury finally heard from the lying snake himself: Sam Altman. At the end of the testimony, his lawyer William Savitt as

industrythe-verge-ai

SAMOFT: Robust Multi-Object Tracking via Region and Flow

arXiv:2605.09417v1 Announce Type: new Abstract: Multi-object tracking (MOT) is a fundamental task in computer vision that requires continuously tracking multiple targets while maintaining consistent i

Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift

arXiv:2605.10289v1 Announce Type: new Abstract: Offline-to-online learning aims to improve online decision-making by leveraging offline logged data. A central challenge in this setting is the distribu

safetyarxiv-cs-lg

Samsung and its South Korean labor union fail to reach a pay deal; the union has said workers will strike for 18 days from May 21 if its demands are not met (Reuters)

Reuters: Samsung and its South Korean labor union fail to reach a pay deal; the union has said workers will strike for 18 days from May 21 if its demands are not met — Samsung Electronics (005930.KS)

industrytechmeme

Sanity Checks for Long-Form Hallucination Detection

arXiv:2605.08346v1 Announce Type: cross Abstract: Hallucination detection methods for large language models increasingly operate on chain-of-thought reasoning traces, yet it remains unclear whether th

SAP recasts Joule as the front door to autonomous enterprise AI

SAP SE today introduced at Sapphire 2026, the company’s annual conference, what it calls Autonomous Enterprise, a suite of artificial intelligence tools and agents designed to enhance how humans and A

agentssiliconangle

SAP SAPPHIRE 2026: Google Cloud unveils unified agentic vision and massive compute scaling

model-releasesgoogle-cloud-ai

In today's hyper-connected market, an enterprise's most valuable asset — mission-critical data — often remains trapped in legacy silos. For years, leadership teams have navigated a data pipeline dilem

SAR-RAG: ATR Visual Question Answering by Semantic Search, Retrieval, and MLLM Generation

arXiv:2602.04712v2 Announce Type: replace-cross Abstract: We present a visual-context image-retrieval-augmented generation (ImageRAG)- assisted AI agent for automatic target recognition (ATR) of synth

agentsarxiv-cs-ai

SARL: Label-Free Reinforcement Learning by Rewarding Reasoning Topology

arXiv:2603.27977v2 Announce Type: replace Abstract: Reinforcement learning is critical to improving large reasoning models, but its success relies heavily on verifiable rewards (RLVR), making it hard

safetyarxiv-cs-ai

SayNext-Bench: Why Do LLMs Struggle with Next-Utterance Anticipation?

arXiv:2602.00327v2 Announce Type: replace Abstract: We explore the use of large language models (LLMs) for next-utterance anticipation in human dialogue. Despite recent advances in LLMs demonstrating

Applications

Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model

arXiv:2311.03600v3 Announce Type: replace Abstract: Robots capable of learning from demonstration (LfD) must exhibit stability while executing learned motion skills. To be effective in the real world,

applicationsarxiv-cs-ro

Scalable Gaussian process inference via neural feature maps

model-releasesarxiv-cs-lg

arXiv:2605.10285v1 Announce Type: cross Abstract: We present a theoretically grounded Gaussian process framework that leverages neural feature maps to construct expressive kernels. We show that the le

Applications

Scalable Inspection Planning via Flow-based Mixed Integer Linear Programming

arXiv:2603.16593v2 Announce Type: replace Abstract: Inspection planning is concerned with computing the shortest robot path to inspect a given set of points of interest (POIs) using the robot's sensor

applicationsarxiv-cs-ro

Scalable Mamba-Based Message-Passing Neural Decoder for Error-Correcting Codes

arXiv:2605.10681v1 Announce Type: cross Abstract: Forward error correction is essential for reliable communication over noisy channels. Attention-based model-free neural decoders have shown strong per

researcharxiv-cs-lg

SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysis

arXiv:2605.10327v1 Announce Type: cross Abstract: In this paper, we present SCALAR (Symbolic Conjecture and LLM-Assisted Reasoning), a neurosymbolic framework for automated conjecture generation in qu

Scaling Limits of Long-Context Transformers

arXiv:2605.08505v1 Announce Type: cross Abstract: We study the long-context limit of softmax self-attention with a fixed query and a random context of n i.i.d. keys on the sphere, viewing the inverse

Local Ai

Scaling Mobile Agent Systems: From Capability Density to Collective Intelligence

arXiv:2605.08124v1 Announce Type: cross Abstract: Mobile agent systems are emerging as a key paradigm for enabling intelligent applications on edge devices and in AIoT ecosystems. However, their scala

local-aiarxiv-cs-cl

Scaling the Memory of Balanced Adam

model-releasesarxiv-cs-lg

arXiv:2605.10119v1 Announce Type: new Abstract: Recent evidence suggests that Adam performs robustly when its momentum parameters are tied, eta_1=eta_2, reducing the optimizer to a single remaining pa

Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality

arXiv:2605.10142v1 Announce Type: cross Abstract: Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of po

Scam2Prompt: A Scalable Framework for Auditing Malicious Scam Endpoints in Production LLMs

arXiv:2509.02372v3 Announce Type: replace-cross Abstract: Large Language Models have become critical to modern software development, but their reliance on uncurated web-scale datasets for training int

SceneFactory: GPU-Accelerated Multi-Agent Driving Simulation with Physics-Based Vehicle Dynamics

arXiv:2605.08528v1 Announce Type: cross Abstract: Autonomous-driving simulators typically trade physical fidelity for scalable parallelism. Physics-based platforms such as CARLA and MetaDrive provide

safetyarxiv-cs-ro

ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review

arXiv:2601.22638v2 Announce Type: replace-cross Abstract: The exponential growth of machine learning submissions has strained the traditional peer review process, resulting in slow feedback loops for

agentsarxiv-cs-ai

SciIntegrity-Bench: A Benchmark for Evaluating Academic Integrity in AI Scientist Systems

arXiv:2605.10246v1 Announce Type: new Abstract: AI scientist systems are increasingly deployed for autonomous research, yet their academic integrity has never been systematically evaluated. We introdu

SciLT: Long-tailed Image Classification under Scientific Image Domains

arXiv:2604.03687v2 Announce Type: replace Abstract: Long-tailed recognition has benefited from foundation models and fine-tuning paradigms, yet existing studies and benchmarks are mainly confined to n

SciVQR: A Multidisciplinary Multimodal Benchmark for Advanced Scientific Reasoning Evaluation

model-releasesarxiv-cs-cv

arXiv:2605.10187v1 Announce Type: new Abstract: Scientific reasoning is a key aspect of human intelligence, requiring the integration of multimodal inputs, domain expertise, and multi-step inference a

SCOT: Multi-Source Cross-City Transfer with Optimal-Transport Soft-Correspondence Objective

arXiv:2604.07383v2 Announce Type: replace Abstract: Cross-city transfer improves prediction in label-scarce cities by leveraging labeled data from other cities, but it becomes challenging when cities

safetyarxiv-cs-lg

Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models

arXiv:2605.09630v1 Announce Type: new Abstract: Tokenizer-free language models eliminate the tokenizer step of the language modeling pipeline by operating directly on bytes; patch-based variants furth

researcharxiv-cs-cl

SDFlow: Similarity-Driven Flow Matching for Time Series Generation

arXiv:2605.05736v2 Announce Type: replace Abstract: Vector quantization (VQ) with autoregressive (AR) token modeling is a widely adopted and highly competitive paradigm for time-series generation. How

safetyarxiv-cs-ai

SDG-MoE: Signed Debate Graph Mixture-of-Experts

arXiv:2605.08322v1 Announce Type: cross Abstract: Sparse MoE models achieve a good balance between capacity and compute by routing each token to a small subset of experts. However, in most MoE archite

SDiaReward: Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness

model-releasesarxiv-cs-cl

arXiv:2603.14889v2 Announce Type: replace-cross Abstract: The rapid evolution of end-to-end spoken dialogue systems demands transcending mere textual semantics to incorporate paralinguistic nuances an

SDTalk: Structured Facial Priors and Dual-Branch Motion Fields for Generalizable Gaussian Talking Head Synthesis

arXiv:2605.09956v1 Announce Type: cross Abstract: High-quality, real-time talking head synthesis remains a fundamental challenge in computer vision. Existing reconstruction- and rendering-based method

Tutorials

SE-Bench: Benchmarking Self-Evolution with Knowledge Internalization

arXiv:2602.04811v2 Announce Type: replace-cross Abstract: True self-evolution requires agents to act as lifelong learners that internalize novel experiences to solve future problems. However, rigorous

tutorialsarxiv-cs-ai

SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks

arXiv:2605.09038v1 Announce Type: new Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries. This is especia

SeasonScapes: Learning Large-scale Re-lightable 3D Landscapes with Seasonal Variation from Sparse Webcams

arXiv:2605.09039v1 Announce Type: new Abstract: We introduce SeasonScapes framework and a the SeasonScapes dataset: Swiss Sparse-view Mountain Scenes with Seasonal Changes that covers over 50 km x 60

SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data

model-releasesarxiv-cs-lg

arXiv:2605.08519v1 Announce Type: new Abstract: Learning from scarce labeled data with a larger pool of unlabeled samples, known as semi-supervised few-shot learning (SS-FSL), remains critical for app

SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization

arXiv:2605.08382v1 Announce Type: cross Abstract: LLM coding agents now generate code at an unprecedented scale, yet LLM-generated code introduces cybersecurity vulnerabilities into codebases without

agentsarxiv-cs-cl

Security Risks in Tool-Enabled AI Agents: A Systematic Analysis of Privileged Execution Environments

arXiv:2605.09721v1 Announce Type: cross Abstract: Tool-enabled AI agents are increasingly deployed in cloud-hosted environments and offered as services, where they perform side-effecting operations th

agentsarxiv-cs-ai

Seed Hijacking of LLM Sampling and Quantum Random Number Defense

arXiv:2605.08313v1 Announce Type: cross Abstract: Large language models (LLMs) rely on deterministic pseudorandom number generators (PRNGs) for autoregressive sampling, creating a critical supply-chai

SeePhys Pro: Diagnosing Modality Transfer and Blind-Training Effects in Multimodal RLVR for Physics Reasoning

arXiv:2605.09266v1 Announce Type: new Abstract: We introduce SeePhys Pro, a fine-grained modality transfer benchmark that studies whether models preserve the same reasoning capability when critical in

Segment Anything with Robust Uncertainty-Accuracy Correlation

arXiv:2605.10603v1 Announce Type: new Abstract: Despite strong zero-shot performance, SAM is unreliable under domain shift due to Mask-level Confidence Confusion (MCC), where a single IoU-based mask s

safetyarxiv-cs-cv

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge

arXiv:2407.11906v3 Announce Type: replace Abstract: Surgical data science has seen rapid advancement with the excellent performance of end-to-end deep neural networks (DNNs). Despite their successes,

SEIS: Subspace-based Equivariance and Invariance Scores for Neural Representations

arXiv:2602.04054v2 Announce Type: replace-cross Abstract: Understanding how neural representations respond to geometric transformations is essential for evaluating whether learned features preserve me