All entries

Safety-Oriented Evaluation of Language Understanding Systems for Air Traffic Control

arXiv:2605.11769v1 Announce Type: new Abstract: Air Traffic Control (ATC) is a safety-critical domain in which incorrect interpretation of instructions may lead to severe operational consequences. Whi

safetyarxiv-cs-cl

Content type

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SAGAS: Semantic-Aware Graph-Assisted Stitching for Offline Temporal Logic Planning

DGX agent

arXiv:2512.00775v2 Announce Type: replace Abstract: Linear Temporal Logic (LTL) provides a rigorous framework for specifying long-horizon robotic tasks, yet existing approaches face a trade-off: model

safetyarxiv-cs-ro

13 May 2026

SAGE: Scalable Automated Robustness Augmentation for LLM Knowledge Evaluation

model-releasesarxiv-cs-cl

arXiv:2605.12022v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance on standard knowledge evaluation benchmarks, yet recent work shows that their knowledge capabili

@SakanaAILabs @NVIDIAAI Sparser, Faster, Lighter Transformer Language Models https://arxiv.org/abs/2603.23198

This research paper from Sakana AI and NVIDIA explores techniques for creating more efficient transformer language models by reducing sparsity, computational requirements, and model size while maintai

researchdavid-ha--x

Industry

Sam Altman testifies in landmark OpenAI trial, says Musk wanted control of company

OpenAI Group PBC co-founder and Chief Executive Sam Altman faced the witness stand today in the high-stakes trial that could determine the future of the company. Elon Musk is suing the company and its

industrysiliconangle

Sampling-Based Follow-the-Leader Motion Planning for Manipulator-Mounted Continuum Robots

arXiv:2605.11618v1 Announce Type: new Abstract: Follow-the-leader (FTL) motion exploits the unique morphology of continuum robots (CRs) to navigate confined spaces by having the body retrace the path

researcharxiv-cs-ro

Local Ai

Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs

arXiv:2605.11128v1 Announce Type: new Abstract: Diversity is essential for language-model applications ranging from creative generation to scientific discovery, yet modern LLMs often collapse into a n

local-aiarxiv-cs-cl

SB-BEVFusion: Enhancing the Robustness against Sensor Malfunction and Corruptions

arXiv:2605.11799v1 Announce Type: new Abstract: Multimodal sensor fusion has demonstrated remarkable performance improvements over unimodal approaches in 3D object detection for autonomous vehicles. T

agentsarxiv-cs-cv

Scalable Token-Level Hallucination Detection in Large Language Models

arXiv:2605.12384v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities, but they still frequently produce hallucinations. These hallucinations are diffi

researcharxiv-cs-cl

ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation

arXiv:2605.11704v1 Announce Type: new Abstract: We present ScaleMoGen, a scale-wise autoregressive framework for text-driven human motion generation. Unlike conventional autoregressive approaches that

Scaling Laws and Tradeoffs in Recurrent Networks of Expressive Neurons

arXiv:2605.12049v1 Announce Type: new Abstract: Cortical neurons are complex, multi-timescale processors wired into recurrent circuits, shaped by long evolutionary pressure under stringent biological

SCOPE: Siamese Contrastive Operon Pair Embeddings for Functional Sequence Representation and Classification

arXiv:2605.11022v1 Announce Type: cross Abstract: Identifying operons is a fundamental step in understanding prokaryotic gene regulation, as classifying genes into operons supports the reconstruction

ScribbleDose: Scribble-Guided Dose Prediction in Radiotherapy

arXiv:2605.11555v1 Announce Type: new Abstract: Anatomical structure masks are widely adopted in radiotherapy dose prediction, as they provide explicit geometric constraints that facilitate structure-

Search Your Block Floating Point Scales!

arXiv:2605.12464v1 Announce Type: new Abstract: Quantization has emerged as a standard technique for accelerating inference for generative models by enabling faster low-precision computations and redu

Securing AI agents: How AWS and Cisco AI Defense scale MCP and A2A deployments

The Cisco and AWS partnership addresses three challenges enterprises face when scaling AI agents: visibility gaps, security bottlenecks, and compliance risks. In this post, we explore how you can over

agentsaws-ml-blog

See the past: Time-Reversed Scene Reconstruction from Thermal Traces Using Visual Language Models

arXiv:2510.05408v2 Announce Type: replace Abstract: Recovering the past from present observations is an intriguing challenge with potential applications in forensics and scene analysis. Thermal imagin

See What Matters: Differentiable Grid Sample Pruning for Generalizable Vision-Language-Action Model

model-releasesarxiv-cs-cv

arXiv:2605.11817v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown remarkable promise in robotics manipulation, yet their high computational cost hinders real-time deploy

Local Ai

Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation

arXiv:2605.10988v1 Announce Type: new Abstract: Log anomaly detection is a critical task for system operations and security assurance. However, in networked systems at scale, log data are generated at

local-aiarxiv-cs-lg

Applications

Selection, Not Fusion: Radar-Modulated State Space Models for Radar-Camera Depth Estimation

arXiv:2605.11840v1 Announce Type: new Abstract: Radar-camera depth estimation must turn an ultra-sparse, all-weather, metric radar signal into a dense per-pixel depth map. Existing methods -- concaten

applicationsarxiv-cs-cv

Applications

Self-Consistent Latent Reasoning: Long Latent Sequence Reasoning for Vision-Language Model

arXiv:2605.12163v1 Announce Type: new Abstract: In language reasoning, longer chains of thought consistently yield better performance, which naturally suggests that visual latent reasoning may likewis

applicationsarxiv-cs-cv

Self-Distilled Trajectory-Aware Boltzmann Modeling: Bridging the Training-Inference Discrepancy in Diffusion Language Models

arXiv:2605.11854v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have recently emerged as a promising alternative to autoregressive language models, offering stronger global awareness

researcharxiv-cs-cl

Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification

arXiv:2605.12208v1 Announce Type: cross Abstract: Approximate Bayesian inference typically revolves around computing the posterior parameter distribution. In practice, however, the main object of inte

Semi-Supervised Bayesian GANs with Log-Signatures for Uncertainty-Aware Credit Card Fraud Detection

arXiv:2509.00931v3 Announce Type: replace-cross Abstract: We present a novel deep generative semi-supervised framework for credit card fraud detection, formulated as time series classification task. A

researcharxiv-cs-lg

SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation

model-releasesarxiv-cs-cv

arXiv:2605.12389v1 Announce Type: new Abstract: Segmenting small and sparse structures in large-scale images is fundamentally constrained by voxel-level, lattice-bound computation and extreme class im

Local Ai

SenseNova-U1 Technical Report: VAE-free Pixel-level Flow Matching with 32x Compression

local-air-stablediffusion

SenseNova-U1 is a native unified multimodal model built on the NEO-unify architecture that eliminates visual encoders and VAEs, instead using a near-lossless visual interface that preserves semantic s

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture

arXiv:2605.12500v1 Announce Type: new Abstract: Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as disti

agentsarxiv-cs-cv

Sequential Off-Policy Learning with Logarithmic Smoothing

arXiv:2506.10664v2 Announce Type: replace-cross Abstract: Off-policy learning enables training policies from logged interaction data. Most prior work considers the batch setting, where a policy is lea

safetyarxiv-cs-lg

SEVO: Semantic-Enhanced Virtual Observation for Robust VLA Manipulation via Active Illumination and Data-Centric Collection

arXiv:2605.11114v1 Announce Type: new Abstract: Vision-Language-Action (VLA) and imitation-learning policies trained via community toolchains on low-cost hardware frequently fail when deployed outside

safetyarxiv-cs-ro

ShapeCodeBench: A Renewable Benchmark for Perception-to-Program Reconstruction of Synthetic Shape Scenes

model-releasesarxiv-cs-cv

arXiv:2605.11680v1 Announce Type: new Abstract: We introduce ShapeCodeBench, a synthetic benchmark for perception-to-program reconstruction: given a rendered raster image, a model must emit an executa

Shaping Zero-Shot Coordination via State Blocking

arXiv:2605.11688v1 Announce Type: new Abstract: Zero-shot coordination (ZSC) aims to enable agents to cooperate with independently trained partners without prior interaction, a key requirement for rea

agentsarxiv-cs-lg

Shapley Value Approximation Based on k-Additive Games

arXiv:2502.04763v2 Announce Type: replace-cross Abstract: The Shapley value is the prevalent solution for fair division problems in which a payout is to be divided among multiple agents. By adopting a

researcharxiv-cs-lg

ShardTensor: Domain Parallelism for Scientific Machine Learning

arXiv:2605.11111v1 Announce Type: cross Abstract: Scientific Machine Learning (SciML) faces unique challenges for extreme-resolution data, with mitigations that often fail to scale or degrade the accu

researcharxiv-cs-lg

Tutorials

Sharpen Your Flow: Sharpness-Aware Sampling for Flow Matching

arXiv:2605.11547v1 Announce Type: new Abstract: Flow matching models generate samples by numerically integrating a learned velocity field, with each integration step requiring a neural network evaluat

tutorialsarxiv-cs-lg

SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy

arXiv:2605.12247v1 Announce Type: new Abstract: Contact-rich assembly is fundamental in robotics but poses significant challenges due to uncertainties in relative poses, such as misalignments and smal

safetyarxiv-cs-ro

Applications

Sign Language Recognition and Translation for Low-Resource Languages: Challenges and Pathways Forward

arXiv:2605.12096v1 Announce Type: new Abstract: Sign languages are natural, visual-gestural languages used by Deaf communities worldwide. Over 300 distinct sign languages remain severely low-resource

applicationsarxiv-cs-cl

Sign up below for a Hermes Agent meetup in Montreal!

Sign up below for a Hermes Agent meetup in Montreal! Save the date for May 29 at 6:30pm. Be here in Montreal or be square. You are cordially invited to a cozy apartment MEETUP hosted by myself and @vn

agentsnous-research--x

Simpson's Paradox in Behavioral Curves: How Aggregation Distorts Parametric Models of User Dynamics

arXiv:2605.11017v1 Announce Type: new Abstract: Behavioral curve modeling -- fitting parametric functions to engagement-versus-exposure data -- is standard practice in recommendation, advertising, and

safetyarxiv-cs-lg

Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation

arXiv:2603.15759v2 Announce Type: replace-cross Abstract: Robot learning requires adaptation methods that improve reliably from limited, mixed-quality interaction data. This is especially challenging

safetyarxiv-cs-lg

Simulation-Ready Cluttered Scene Estimation via Physics-aware Joint Shape and Pose Optimization

arXiv:2602.20150v2 Announce Type: replace-cross Abstract: Estimating simulation-ready scenes from real-world observations is crucial for downstream planning and policy learning tasks. Regretfully, exi

safetyarxiv-cs-cv

Single-Shot HDR Recovery via a Video Diffusion Prior

arXiv:2605.11628v1 Announce Type: new Abstract: Recent generative methods for single-shot high dynamic range (HDR) image reconstruction show promising results, but often struggle with preserving fidel

SkillGen: Verified Inference-Time Agent Skill Synthesis

arXiv:2605.10999v1 Announce Type: new Abstract: Skills are a promising way to improve LLM agent capabilities without retraining, while keeping the added procedure reusable and controllable. However, h

agentsarxiv-cs-lg

SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs

arXiv:2605.12039v1 Announce Type: new Abstract: Skill libraries enable large language model agents to reuse experience from past interactions, but most existing libraries store skills as isolated entr

safetyarxiv-cs-cl

SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces

model-releasesarxiv-cs-cl

arXiv:2605.12015v1 Announce Type: cross Abstract: Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools,

Slicing and Dicing: Configuring Optimal Mixtures of Experts

model-releasesarxiv-cs-cl

arXiv:2605.11689v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures have become standard in large language models, yet many of their core design choices - expert count, granularit

Smart moves: Building resilient transportation systems with Google AI

What does transportation mean to you? For some, it’s making sure the train is on schedule so they can get to work on time. Maybe it’s making sure you have time connecting between flights. Maybe it’s a

safetygoogle-cloud-ai

Smooth-Rigid-Body Contact as a ReLCP: A Recursively Generated Linear Complementarity Problem

arXiv:2506.14097v2 Announce Type: replace Abstract: This paper reformulates complementarity-based time-stepping for frictionless nonsmooth contact between smooth rigid bodies as a recursively generate

researcharxiv-cs-ro

Tutorials

Smoothed Analysis of Learning from Positive Samples

arXiv:2504.10428v2 Announce Type: replace-cross Abstract: Binary classification from positive-only samples is a variant of PAC learning where the learner receives i.i.d. positive samples and aims to l

tutorialsarxiv-cs-lg

Tutorials

Smoothness Errors in Dynamics Models and How to Avoid Them

arXiv:2602.05352v2 Announce Type: replace Abstract: Modern neural networks have shown promise for solving partial differential equations over surfaces, often by discretizing the surface as a mesh and

tutorialsarxiv-cs-lg