All entries

arXiv:2605.16671v1 Announce Type: new Abstract: Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalab

local-aiarxiv-cs-ai

Safety

SutureFormer: Learning Surgical Trajectories via Goal-conditioned Offline RL in Pixel Space

DGX agent

arXiv:2603.26720v2 Announce Type: replace-cross Abstract: Predicting surgical needle trajectories from endoscopic video is critical for robot-assisted suturing, enabling anticipatory planning, real-ti

safetyarxiv-cs-ai

19 May 2026

SVFSearch: A Multimodal Knowledge-Intensive Benchmark for Short-Video Frame Search in the Gaming Vertical Domain

arXiv:2605.17946v1 Announce Type: new Abstract: Multimodal large language models are increasingly used as agent backbones that understand multimodal inputs, plan retrieval actions, invoke external too

SVL: Spike-based Vision-language Pretraining for Efficient 3D Open-world Understanding

arXiv:2505.17674v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. However, existing SNNs still exhibit a signif

safetyarxiv-cs-cv

SWING: Unlocking Implicit Graph Representations for Graph Random Features

arXiv:2602.12703v2 Announce Type: replace Abstract: We propose SWING: Space Walks for Implicit Network Graphs, a new class of algorithms for computations involving Graph Random Features on graphs give

Agents

SWoMo: Neuro-Symbolic World Model for Cataract Surgery Simulation

arXiv:2605.16530v1 Announce Type: new Abstract: Realistic surgical simulation plays a crucial role in training novice surgeons and in the development of autonomous agents. World models can scale such

agentsarxiv-cs-cv

SwordBench: Evaluating Orthogonality of Steering Image Representations

arXiv:2605.16372v1 Announce Type: cross Abstract: Steering or intervening on model representations at inference time to correct predictions is essential for AI interpretability and safety, yet existin

Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers

arXiv:2605.18106v1 Announce Type: cross Abstract: A striking geometric disparity has long persisted in the practice of deep learning. While modern neural network architectures naturally exhibit rich s

Symmetry Matters: Auditing and Symmetrizing 3D Generative Models

arXiv:2512.18953v2 Announce Type: replace Abstract: Symmetry is a strong prior present in many object categories, yet standard benchmarks for 3D generative models rarely report whether this prior is p

researcharxiv-cs-cv

Symphony for Speech-to-Text: Supporting Real-Time Medical Voice Interfaces

arXiv:2605.16545v1 Announce Type: cross Abstract: After decades of use in dictation and, more recently, ambient documentation, speech is emerging as a primary modality for interacting with technology

Synthesis and Verification of Transformer Programs (Technical Report)

arXiv:2602.16473v2 Announce Type: replace Abstract: C-RASP is a simple programming language that was recently shown to capture concepts expressible by transformers. In this paper, we develop new algor

Local Ai

Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network

arXiv:2605.16764v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) have been extensively and successfully applied to the task of synthetic aperture radar (SAR) image change detection

local-aiarxiv-cs-cv

SynVA: A Modular Toolkit for Vessel Generation and Aneurysm Editing

arXiv:2605.17620v1 Announce Type: cross Abstract: Intracranial aneurysms (IAs), characterized by unpredictable growth and risk of rupture, are a major cause of stroke and can lead to life-threatening

Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale

arXiv:2605.17775v1 Announce Type: cross Abstract: Large language models (LLMs) can generate or synthesize clinical text for a wide range of applications, from improving clinical documentation to augme

Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability

arXiv:2605.17236v1 Announce Type: cross Abstract: Manual Pap smear analysis for cervical cancer screening is limited by inter-observer variability, time constraints, and restricted expert availability

Hardware

Systematic Optimization of Real-Time Diffusion Model Inference on Apple M3 Ultra

arXiv:2605.16259v1 Announce Type: cross Abstract: While real-time image generation using diffusion models has advanced rapidly on NVIDIA GPUs, systematic optimization research on non-CUDA platforms su

hardwarearxiv-cs-ai

T-FIX: Text-Based Explanations with Features Interpretable to eXperts

arXiv:2511.04070v3 Announce Type: replace Abstract: As LLMs are deployed in knowledge-intensive settings (e.g., surgery, astronomy, therapy), users are often domain experts who expect not just answers

safetyarxiv-cs-cl

t-gems: text-guided exit modules for decreasing clip image encoder

arXiv:2605.17499v1 Announce Type: new Abstract: Multimodal deep neural networks enhance deep comprehension by integrating diverse data modalities. Data from different modalities are typically projecte

Local Ai

T-STAR: A Context-Aware Transformer Framework for Short-Term Probabilistic Demand Forecasting in Dock-Based Shared Micro-Mobility

arXiv:2602.06866v2 Announce Type: replace Abstract: Reliable short-term demand forecasting is essential for managing shared micro-mobility services and ensuring responsive, user-centered operations. T

local-aiarxiv-cs-lg

TabH2O: A Unified Foundation Model for Tabular Prediction

model-releasesarxiv-cs-lg

arXiv:2605.18383v1 Announce Type: new Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning. Tab

TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates

arXiv:2605.17642v1 Announce Type: new Abstract: Tabular data generation considers a large table with multiple columns -- each column comprised of numerical, categorical, or sometimes ordinal values. T

Applications

TACO: Temporal Consensus Optimization for Continual Neural Mapping

arXiv:2602.04516v3 Announce Type: replace Abstract: Neural implicit mapping has emerged as a powerful paradigm for robotic navigation and scene understanding. However, real-world robotic deployment re

applicationsarxiv-cs-ro

TacSE3: Equivariant SE(3) Motion Estimation from Low-Texture Visuotactile Images for In-Gripper Tracking and Compensation

arXiv:2605.17929v1 Announce Type: new Abstract: Robotic in-hand manipulation requires reliable object-motion tracking under frequent visual occlusion, yet low-texture visuotactile images provide few s

safetyarxiv-cs-ro

Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms

model-releasesarxiv-cs-cv

arXiv:2605.17336v1 Announce Type: cross Abstract: Tactile sensing is a fundamental modality for embodied intelligence, offering unique and direct feedback on contact geometry, material properties, and

TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification

arXiv:2605.16361v1 Announce Type: cross Abstract: We present TailedTS, a large-scale benchmark dataset derived from Wikipedia hourly page view observations throughout 2024, specifically designed to te

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.17577v1 Announce Type: new Abstract: Large-scale pre-trained Vision-Language models (VLMs), such as CLIP, exhibit strong zero-shot generalization, yet remain highly vulnerable to impercepti

Taming Audio VAEs via Target-KL Regularization

arXiv:2605.17085v1 Announce Type: cross Abstract: Latent diffusion models have emerged as the dominant paradigm for many generation tasks including audio generation such as text-to-audio, text-to-musi

Taming 'Zombie'' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution

arXiv:2605.17348v1 Announce Type: new Abstract: Recent advancements in LLM-based multi-agent systems have demonstrated remarkable collaborative capabilities across complex tasks. To improve overall ef

safetyarxiv-cs-cl

Task Abstention for Large Language Models in Code Generation

arXiv:2605.17029v1 Announce Type: cross Abstract: Large language models (LLMs) have revolutionized automated code generation. One serious concern, however, is the so-called ``hallucination'', i.e., LL

Task Capability Improvement Algorithm for Collaborative Manipulators

arXiv:2605.17293v1 Announce Type: new Abstract: This work introduces a cooperative task capability improvement utilizing additional moments. The manipulators apply forces at the object's grasp point.

researcharxiv-cs-ro

Agents

Task-Level AI Readiness Assessment for Business Process Management:The T-IPO Model and LARA Matrix in Financial-Services IT Operations

arXiv:2605.16297v1 Announce Type: cross Abstract: Which tasks inside an enterprise workflow can a large-language-model agent reliably handle, and under what conditions? Most business process modeling

agentsarxiv-cs-ai

TaskGround: Structured Executable Task Inference for Full-Scene Household Reasoning

arXiv:2605.18109v1 Announce Type: new Abstract: In real home deployments, household agents must often operate from a complete household scene and a situated household request, rather than from a clean

Taxonomy and Consistency Analysis of Safety Benchmarks for AI Agents

arXiv:2605.16282v1 Announce Type: cross Abstract: The rapid deployment of LLM-based autonomous agents has introduced safety risks that extend far beyond traditional LLM concerns, prompting a prolifera

TClone: Low-Latency Forking of Live GUI Environments for Computer-Use Agents

arXiv:2605.17320v1 Announce Type: cross Abstract: Computer-use agents increasingly operate inside live personal workspaces, where their actions can modify files, applications, GUI state, credentials,

safetyarxiv-cs-ai

TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?

arXiv:2605.18025v1 Announce Type: new Abstract: While Large Language Models have achieved remarkable integration in various vertical scenarios, their deployment in the telecommunications domain remain

Hardware

TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval

arXiv:2502.20969v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external data sources to enhance factual correctness and domain

hardwarearxiv-cs-lg

Temporal Aware Pruning for Efficient Diffusion-based Video Generation

arXiv:2605.17837v1 Announce Type: cross Abstract: Video diffusion models have recently enabled high-quality video generation with ViT-based architectures, but remain computationally intensive because

Temporal Decay of Co-Citation Predictability: A 20-Year Statute Retrieval Benchmark from 396M Ukrainian Court Citations

model-releasesarxiv-cs-cl

arXiv:2605.17639v1 Announce Type: new Abstract: Co-citation structure is widely assumed to provide stable retrieval signal in legal information systems. We test this assumption longitudinally by const

Temporal Task Diversity: Inductive Biases Under Non-Stationarity in Synthetic Sequence Modelling

arXiv:2605.18281v1 Announce Type: new Abstract: Modern deep learning science often assumes that neural networks learn from a fixed data distribution. However, many practically important learning probl

safetyarxiv-cs-lg

Tensor Channel Equivariant Graph Neural Networks for Molecular Polarizability Prediction

model-releasesarxiv-cs-lg

arXiv:2605.16891v1 Announce Type: new Abstract: We introduce a tensor-channel equivariant graph neural network for direct prediction of molecular polarizability tensors. Building on the efficient PaiN

Tutorials

Tensor Cookbook: Mastering Tensors through Diagrams

arXiv:2605.16610v1 Announce Type: new Abstract: High-dimensional data arise naturally in many areas of science and engineering, including machine learning, signal processing, computational physics, an

tutorialsarxiv-cs-lg

terrific deep dive on Mythos from @cloudflare

terrific deep dive on Mythos from @cloudflare Cloudflare's security team spent the last few weeks testing Anthropic's Mythos against fifty of our own repositories. What we learned about offensive AI,

safetygary-marcus--x

Test-Time Hinting for Black-Box Vision-Language Models

arXiv:2605.16410v1 Announce Type: new Abstract: Test-time scaling (TTS) methods have proven highly effective for LLMs, yet their application to vision-language models (VLMs) remains relatively underex

researcharxiv-cs-cv

Testable and Actionable Calibration for Full Swap Regret

arXiv:2605.17749v1 Announce Type: new Abstract: AI generated predictions increasingly inform decision making in critical tasks, and therefore must be trustworthy. One widely used measure of trustworth

Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generation

model-releasesarxiv-cs-lg

arXiv:2605.18430v1 Announce Type: new Abstract: Text-to-CAD generation aims to create parametric CAD models from natural language, enabling rapid prototyping and intuitive design workflows. However, e

The 13 biggest announcements at Google I/O 2026

model-releasesthe-verge-ai

Google's I/O 2026 keynote today was once again full of AI-related announcements including a new family of Gemini 3.5 AI models, new features for Search and Gmail, and updates about its Project Aura sm

The agentic era: Architecting the blueprint for mission impact across the public sector

model-releasesgoogle-cloud-ai

This is a new era — the agentic era – and the question is no longer, “what’s possible?” but rather, “what creates impact?” Today, organizations across industries around the world are swiftly moving fr

The Alien Space of Science: Sampling Coherent but Cognitively Unavailable Research Directions

arXiv:2603.01092v2 Announce Type: replace Abstract: Scientific discovery is constrained not only by what is true, but by what is cognitively available to the researchers currently exploring a field. M

safetyarxiv-cs-ai