All entries

Scalable Cox Regression via Grouped Risk Sets and Sharper LogSumExp Rates

arXiv:2609.40120v1 Announce Type: new Abstract: Motivated by the computational challenges of large-scale Cox regression, we study stochastic minimization of LogSumExp objectives over large sets. Mini-

researcharxiv-cs-lg

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Scale and Selection: What Makes Automatic Harness Evolution Work for Visual-Interface Robot Agents

DGX agent

arXiv:2609.39304v1 Announce Type: cross Abstract: When an off-the-shelf coding agent is used directly as a robot policy, observing a browser-based 3D interface through screenshots and acting by posing

safetyarxiv-cs-ai

1 Oct 2026

Scale-Split Neural Operator for Memory- and Data-Efficient 3D Turbulence Prediction

arXiv:2609.38977v1 Announce Type: cross Abstract: Neural surrogates have emerged as fast alternatives to the numerical simulation of three-dimensional turbulence. However, training them at high resolu

local-aiarxiv-cs-ai

SCALE: Synthetic Calibration via Agreement Labeling in Embedding Space

arXiv:2609.38705v1 Announce Type: new Abstract: Foundation models for computational pathology are usually evaluated using AUC and accuracy, while calibration is often left untested. This matters becau

researcharxiv-cs-cv

Scaling Laws for Looped Mixture of Experts

arXiv:2609.40316v1 Announce Type: cross Abstract: Looped transformers and Mixture-of-Experts (MoE) offer complementary routes to efficient scaling: recurrence increases computational depth at fixed pa

Scaling Parameter and Context in Attention: Native Sparse Attention from Mixture-of-Head

model-releasesarxiv-cs-cl

arXiv:2609.38832v1 Announce Type: new Abstract: Scaling attention parameters can improve language model quality, but retaining full token histories makes additional heads costly at long contexts. Furt

SCB: SpeechConversationBench for Evaluating Multi-Turn Reasoning in Speech-to-Speech Models

arXiv:2609.40198v1 Announce Type: cross Abstract: Speech-to-speech systems must solve tasks whose requirements emerge across conversational turns. We introduce SpeechConversationBench (SCB), a focused

SceneJail: Exploiting Video Scenario Context to Jailbreak Multimodal LLMs

arXiv:2609.38899v1 Announce Type: cross Abstract: Video Multimodal Large Language Models (Video-MLLMs) support reasoning over video inputs, yet remain vulnerable to jailbreak attacks that elicit polic

Schema: Discovering Unknown Environments via Agentic Program Induction

arXiv:2609.39140v1 Announce Type: new Abstract: Learning to complete tasks in unfamiliar environments with unknown rules remains a key challenge for LLM agents. Current LLM agents often record their d

agentsarxiv-cs-ai

SCIC: Scope- and Codebook-Aware Instruction Conditioning for Speaker-Adapted Expressive TTS

arXiv:2609.39088v1 Announce Type: cross Abstract: Long-form live-streaming TTS requires context-dependent prosody and paragraph-level coherence. However, many existing instruction-based TTS systems us

SCORE-LM: State-Space Radar Representations with Language Models for Fault Diagnosis

arXiv:2609.38980v1 Announce Type: new Abstract: Radar hardware faults threaten automated perception, motivating accurate, compact diagnosis and understandable maintenance guidance. We introduce SCORE-

local-aiarxiv-cs-lg

Scoring Higher, Answering Worse: Mitigating Reward Hacking in Rubric-Based RL via Protocol-Level Rubrics

arXiv:2609.38847v1 Announce Type: cross Abstract: Rubric-based reinforcement learning (Rubric-RL) trains language models where no verifier exists. A judge checks each criterion of a rubric, and the ve

scTrilemma: Balancing Identity, Invariance, and Fidelity in Single-Cell Representation Learning

arXiv:2609.38840v1 Announce Type: cross Abstract: Single-cell RNA-seq representation learning is fundamentally label-free: cell identities, states, and contexts are not fixed training targets, so what

SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision

arXiv:2609.39679v1 Announce Type: cross Abstract: Audio deepfake detection (ADD) must remain effective when new spoofing attacks emerge after deployment. Emerging audio language model (ALM)-based ADD

researcharxiv-cs-lg

SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents

arXiv:2609.35596v2 Announce Type: replace-cross Abstract: Self-evolving LLM agents have gained prominence for their ability to improve after deployment by modifying their harness, including their cont

SEAR: Spoofing Evidence-Grounded Audio Reasoning Benchmark for Audio Language Models

arXiv:2609.39847v1 Announce Type: cross Abstract: Audio language models (ALMs) are increasingly used for audio deepfake detection (ADD), yet existing benchmarks assess their verdicts or rationale plau

Search Shapes Conclusions: Auditing Evidence Selection Bias in Deep Research Agents

arXiv:2609.39026v1 Announce Type: new Abstract: Deep Research agents synthesize evidence into cited reports, yet a well-cited report can still reach a misleading conclusion. Citation correctness check

Searching for BSM Experimental Signatures with Large Lagrangian Models

arXiv:2609.38309v1 Announce Type: cross Abstract: The search for physics Beyond the Standard Model (BSM) is generally limited not by the supply of theory descriptions but by the lack of discriminating

SecureVibe: Making Vibe Coding More Secure

arXiv:2609.38606v1 Announce Type: cross Abstract: As vibe coding becomes increasingly capable and widespread, security vulnerabilities in even functionally correct solutions are a growing concern. Whe

agentsarxiv-cs-cl

Security-Enhanced Seed-Based Weight Quantization for Large Language Models

arXiv:2609.38477v1 Announce Type: cross Abstract: Large language models (LLMs) incur substantial storage, memory-bandwidth and energy costs, motivating compact weight representations. Existing seed-ba

Security Properties of Neural Networks as Decision Problems

arXiv:2609.39768v1 Announce Type: cross Abstract: Certifying a deployed neural network raises decision problems that the verification literature has not classified: whether the model carries a backdoo

Tutorials

Seeing as Humans Do: Learning from Motion to Segment Anything Without Supervision

arXiv:2609.39785v1 Announce Type: new Abstract: The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised m

tutorialsarxiv-cs-cv

Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization

arXiv:2609.38757v1 Announce Type: new Abstract: Large language models are increasingly participating in complex real-world tasks in the form of algorithm-design agents, designing and refining algorith

Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer

arXiv:2609.38372v1 Announce Type: new Abstract: A harness is the code around a language-model agent that organizes prompts, calls tools, manages context, and controls execution. As models grow stronge

Self-Repulsive Sampling for Diffusion Language Models

arXiv:2609.39560v1 Announce Type: new Abstract: Sampling several responses and voting over their answers can improve a language model's accuracy, but repeated answers limit the benefit of additional s

Self-Spec Verifiable Code Generation

arXiv:2609.39568v1 Announce Type: cross Abstract: Large language models (LLMs) may generate unreliable code on corner cases missed by testing, while formal verification can provide machine-checkable g

Semantic-Aware Joint Source-Channel Optimization for Encoder-Agnostic Digital Video Communication

arXiv:2609.39296v1 Announce Type: new Abstract: Video semantic communication has attracted increasing attention as a promising approach to improving video transmission efficiency. However, most existi

Semantic Chunking and the Entropy of Natural Language

arXiv:2602.13194v3 Announce Type: replace-cross Abstract: Humans and large language models can predict next letter or word from its prior context much better than random guessing, indicating strong re

Semantic Misalignment in Vision-Language Models under Perceptual Degradation

arXiv:2601.08355v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) are increasingly deployed in autonomous driving and embodied AI systems, where reliable perception is critical for saf

safetyarxiv-cs-cv

Semantic Watermarking for Malicious Image Manipulation Detection

arXiv:2609.39623v1 Announce Type: new Abstract: The proliferation of high-fidelity generative editing models has made it possible to inject violent or sexual content into otherwise ordinary images whi

researcharxiv-cs-cv

SemiAnalysis estimates ~90% of Anthropic's business comes from agentic AI, while sources say nearly 25% of its revenue in 2025 came from just two clients (Financial Times)

Financial Times: SemiAnalysis estimates ~90% of Anthropic's business comes from agentic AI, while sources say nearly 25% of its revenue in 2025 came from just two clients — Powerful AI tools burn thro

agentstechmeme

Semifactual Credit-Augmented Policy Optimization

arXiv:2609.40360v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has improved the reasoning capabilities of large language models (LLMs), yet their predictions r

safetyarxiv-cs-ai

Sense and Sensitivity: Benchmarking LLM Clinical Triage Recommendations with Physician Experts

arXiv:2609.38600v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly used in clinical settings, it is critical to evaluate their reliability under realistic variation in cl

Sep 2026 AI Security Report: 126 incidents across 38 orgs, 318M+ records stolen — AI-agent exploits were the top attack vector (39 of 126). Live demo Oct 14.

RuntimeAI's September 2026 AI Security Report covered 126 incidents across 38 named organizations — 22 critical, 102 high severity. 53 of those incidents had AI either as the attack tool or the target

local-air-ollama

SEPAL: Separated Expert Pairs with Answer-Level Fusion for Reliable LLM Collaboration

arXiv:2609.39645v1 Announce Type: cross Abstract: Multi-agent collaboration lets large language models (LLMs) improve question answering through deliberation and feedback. Yet shared discussion couple

agentsarxiv-cs-ai

Sequential Bayesian Evaluation of Large Language Model Behavior

model-releasesarxiv-cs-cl

arXiv:2511.10661v2 Announce Type: replace Abstract: It is increasingly important to evaluate the characteristics of systems based on large language models (LLMs). Evaluations in this context often rel

Tutorials

Serve live, governed data in AI-built apps with Amazon Quick

With Live Data in Apps in Amazon Quick, AI-built apps query your governed Quick Sight datasets in real time instead of static, build-time snapshots. Each query runs as the person viewing the app, so r

tutorialsaws-ml-blog

SETA: Scaling Environments for Terminal Agents

arXiv:2607.10891v2 Announce Type: replace Abstract: Large language models (LLMs) are rapidly shifting toward agents that solve tasks through diverse interfaces, including web and graphical user interf

Settle: Learning When to Stop Reasoning

arXiv:2609.38997v1 Announce Type: new Abstract: Reasoning models often continue generating after their answers have settled. Settle learns when to stop from answer stability in completed traces. It tr

researcharxiv-cs-cl

ShamAN-Q: Shampoo Augmented NanoQuant for Sub-1-bit LLM Weights

arXiv:2609.38521v1 Announce Type: cross Abstract: We introduce ShamAN-Q, a sub-1-bit post-training quantization method that extends NanoQuant by replacing each its diagonal reconstruction geometry wit

local-aiarxiv-cs-ai

Hardware

Shared Phase and Retention Control for Efficient Adaptive Spectral Recurrence

arXiv:2609.39082v1 Announce Type: new Abstract: As new evidence arrives, a sequence model must update what it remembers and how memory influences predictions. While Transformers incur computation and

hardwarearxiv-cs-lg

Shared Weights, Selected Computations: How Looped Transformers Route What Each Loop Does

arXiv:2609.39892v1 Announce Type: new Abstract: Looped Transformers repeatedly apply the same set of Transformer layers, giving them a recurrent architecture for latent computation. Their strong perfo

safetyarxiv-cs-lg

Sharp Stationary Gaussian Approximation for Constant-Stepsize SGD

arXiv:2609.39144v1 Announce Type: new Abstract: We prove a sharp Gaussian approximation for the invariant law of constant-stepsize SGD with bounded additive noise generated by an exogenous uniformly e

researcharxiv-cs-lg

Sharp Statistical Rates for Asynchronous TD Learning with Markovian Data

arXiv:2609.38880v1 Announce Type: cross Abstract: We study the last iterate of standard tabular temporal-difference (TD) learning from a single trajectory of a finite Markov reward process. For discou

ShieldCLIP: Selective Safety Alignment for Harmful Content Mitigation in Multimodal Foundation Models

arXiv:2609.39688v1 Announce Type: cross Abstract: Multimodal encoders such as CLIP underlie many downstream systems, but their web-scale training data embed harmful associations that safety alignment

safetyarxiv-cs-ai

SHIFT-Truck: A High-Fidelity Aerodynamics Dataset and Benchmark for Pickup Trucks

arXiv:2609.38638v1 Announce Type: cross Abstract: Pickup trucks account for 14% of new light-duty vehicles produced in the United States, yet are among the least aerodynamic. Their open cargo bed adds

Shifting Mechanisms: How Positional Encoding Choice Shapes In-Context Retrieval

arXiv:2609.38530v1 Announce Type: new Abstract: Language models increasingly use architectures that vary attention span and positional encoding across layers, such as applying RoPE with sliding-window

researcharxiv-cs-cl

Should I stay or should I show? Learning to selectively disclose information

arXiv:2609.39818v1 Announce Type: new Abstract: In many high-stakes settings, human decision-makers can acquire support information before making a decision. However, acquiring information is costly,