Search: “benchmark”

Vendor-Conditioned Contrastive Learning for Predicting Organizational Cyber Threat Targets

arXiv:2012.14425v2 Announce Type: replace-cross Abstract: Cyberattacks cause billions of dollars in damage annually, with malicious hackers often sharing exploit code and techniques on underground for

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VGGT-Omega

DGX agent

arXiv:2605.15195v1 Announce Type: new Abstract: Recent feed-forward reconstruction models, such as VGGT, have proven competitive with traditional optimization-based reconstructors while also providing

safetyarxiv-cs-cv

15 May 2026

Video-Zero: Self-Evolution Video Understanding

arXiv:2605.14733v1 Announce Type: new Abstract: Self-evolution offers a promising path for improving reasoning models without relying on intensive human annotation. However, extending this paradigm to

researcharxiv-cs-cv

Agents

Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining

arXiv:2605.14747v1 Announce Type: cross Abstract: Recent advances in multimodal large language models have driven growing interest in graphical user interface (GUI) agents, yet their generalization re

agentsarxiv-cs-ai

Vision-Based Runtime Monitoring under Varying Specifications using Semantic Latent Representations

model-releasesarxiv-cs-cv

arXiv:2605.13923v1 Announce Type: cross Abstract: We study certified runtime monitoring of past-time signal temporal logic (ptSTL) from visual observations under partial observability. The monitor mus

Wahkon: A Statistically Principled Deep RKHS Superposition Network

arXiv:2605.14041v1 Announce Type: cross Abstract: Deep learning excels at prediction but often lacks finite-sample guarantees and calibrated uncertainty; RKHS (Reproducing Kernel Hilbert Space)-based

researcharxiv-cs-lg

Agents

WARD: Adversarially Robust Defense of Web Agents Against Prompt Injections

arXiv:2605.15030v1 Announce Type: cross Abstract: Web agents can autonomously complete online tasks by interacting with websites, but their exposure to open web environments makes them vulnerable to p

agentsarxiv-cs-ai

Web Agents Should Adopt the Plan-Then-Execute Paradigm

arXiv:2605.14290v1 Announce Type: cross Abstract: ReAct has become the default architecture across LLM agents, and many existing web agents follow this paradigm. We argue that it is the wrong default

Why Retrieval-Augmented Generation Fails: A Graph Perspective

arXiv:2605.14192v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become a powerful and widely used approach for improving large language models by grounding generation in ret

researcharxiv-cs-ai

WikiCLIP: An Efficient Contrastive Baseline for Open-domain Visual Entity Recognition

arXiv:2603.09921v3 Announce Type: replace Abstract: Open-domain visual entity recognition (VER) seeks to associate images with entities in encyclopedic knowledge bases such as Wikipedia. Recent genera

researcharxiv-cs-cv

3D Primitives are a Spatial Language for VLMs

arXiv:2605.12586v1 Announce Type: cross Abstract: Vision-language models (VLMs) exhibit a striking paradox: they can generate executable code that reconstructs a 3D scene from geometric primitives wit

A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing

arXiv:2605.13464v1 Announce Type: new Abstract: Diabetes mellitus affects over 537 million adults worldwide and remains a major challenge in preventive healthcare. Existing machine-learning studies pr

applicationsarxiv-cs-lg

Action Emergence from Streaming Intent

model-releasesarxiv-cs-cv

arXiv:2605.12622v1 Announce Type: cross Abstract: We formalize action emergence as a target capability for end-to-end autonomous driving: the ability to generate physically feasible, semantically appr

AdaFocus: Adaptive Relevance-Diversity Sampling with Zero-Cache Look-back for Efficient Long Video Understanding

arXiv:2605.12954v1 Announce Type: cross Abstract: Long video understanding is heavily bottlenecked by a rigid one-shot paradigm: existing methods either densely encode videos at prohibitive memory and

local-aiarxiv-cs-ai

AdaptNC: Adaptive Nonconformity Scores for Conformal Prediction under Distribution Shift

arXiv:2602.01629v2 Announce Type: replace Abstract: Rigorous uncertainty quantification is essential for the safe deployment of autonomous systems in unconstrained environments. Conformal Prediction (

safetyarxiv-cs-lg

Agents

AI co-mathematician: Accelerating mathematicians with agentic AI

arXiv:2605.06651v2 Announce Type: replace Abstract: We introduce the AI co-mathematician, a workbench for mathematicians to interactively leverage AI agents to pursue open-ended research. The AI co-ma

agentsarxiv-cs-ai

Aligning Forest and Trees in Images & Long Captions for Visually Grounded Understanding

arXiv:2602.02977v2 Announce Type: replace-cross Abstract: Vision-language models such as CLIP often struggle to faithfully understand long, detail-rich captions, relying on dominant scene cues while o

An Efficient Insect-inspired Approach for Visual Point-goal Navigation

arXiv:2601.16806v3 Announce Type: replace Abstract: In this work we develop a novel insect-inspired model for visual point-goal navigation. This combines abstracted models of two insect brain structur

AttenA+: Rectifying Action Inequality in Robotic Foundation Models

arXiv:2605.13548v1 Announce Type: cross Abstract: Existing robotic foundation models, while powerful, are predicated on an implicit assumption of temporal homogeneity: treating all actions as equally

Attention Once Is All You Need: Efficient Streaming Inference with Stateful Transformers

arXiv:2605.13784v1 Announce Type: new Abstract: Conventional transformer inference engines are request-driven, paying an O(n) prefill cost on every query. In streaming workloads, where data arrives co

Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes

arXiv:2605.13088v1 Announce Type: new Abstract: Dynamical modelling is central to many scientific domains, including pharmacometrics, systems biology, physiology, and epidemiology. In these settings,

researcharxiv-cs-lg

Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models

model-releasesarxiv-cs-cv

arXiv:2603.05582v2 Announce Type: replace-cross Abstract: The issue of algorithmic biases in deep learning has led to the development of various debiasing techniques, many of which perform complex tra

BlitzGS: City-Scale Gaussian Splatting at Lightning Speed

arXiv:2605.13794v1 Announce Type: cross Abstract: We present BlitzGS, a distributed 3DGS framework that reduces active Gaussian workload for fast city-scale reconstruction. BlitzGS manages this worklo

safetyarxiv-cs-cv

Block-wise Adaptive Caching for Accelerating Diffusion Policy

arXiv:2506.13456v2 Announce Type: replace Abstract: Diffusion Policy has demonstrated strong visuomotor modeling capabilities, but its high computational cost renders it impractical for real-time robo

BlockVLA: Accelerating Autoregressive VLA via Block Diffusion Finetuning

arXiv:2605.13382v1 Announce Type: new Abstract: While autoregressive (AR) Vision-Language-Action (VLA) models have demonstrated formidable reasoning capabilities in robotic tasks, their sequential dec

local-aiarxiv-cs-ro

BoostTaxo: Zero-Shot Taxonomy Induction via Boosting-Style Agentic Reasoning and Constraint-Aware Calibration

arXiv:2605.12520v1 Announce Type: cross Abstract: Taxonomy induction is crucial for organizing concepts into explicit and interpretable semantic hierarchies. While existing methods have achieved promi

Bridging the Missing-Modality Gap: Improving Text-Only Calibration of Vision Language Models

arXiv:2605.12517v1 Announce Type: cross Abstract: Vision-language models (VLMs) are often deployed on text-only inputs, although they are trained with images. We find that removing the vision modality

researcharxiv-cs-ai

Building Interactive Real-Time Agents with Asynchronous I/O and Speculative Tool Calling

arXiv:2605.13360v1 Announce Type: new Abstract: There is a growing demand for agentic AI technologies for a range of downstream applications like customer service and personal assistants. For applicat

CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution

arXiv:2605.13295v1 Announce Type: cross Abstract: LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling,

local-aiarxiv-cs-ai

Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling

arXiv:2605.13646v1 Announce Type: cross Abstract: End-to-end autonomous driving, which bypasses traditional modular pipelines by directly predicting future trajectories from sensor inputs, has recentl

CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks

arXiv:2605.12580v1 Announce Type: new Abstract: Randomized neural networks (RdNNs) enable efficient, backpropagation-free training by freezing randomly initialized input-to-hidden weights, which permi

researcharxiv-cs-lg

Tutorials

Characteristic Root Analysis and Regularization for Linear Time Series Forecasting

arXiv:2509.23597v5 Announce Type: replace-cross Abstract: Time series forecasting remains a critical challenge across numerous domains, yet the effectiveness of complex models often varies unpredictab

tutorialsarxiv-cs-ai

ChatSR: Multimodal Large Language Models for Scientific Formula Discovery

arXiv:2406.05410v3 Announce Type: replace Abstract: Current multimodal large language models (MLLMs) are mainly focused on the understanding and processing of perceptual modalities such as images and

Conditional Compatibility Learning for Context-Dependent Anomaly Detection

arXiv:2601.22868v3 Announce Type: replace Abstract: Anomaly detection usually assumes that abnormality is an intrinsic property of an observation. A defect is a defect, and a rare object is rare, rega

applicationsarxiv-cs-cv

Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models

arXiv:2511.15743v2 Announce Type: replace Abstract: Operational forecasting of the ionosphere remains a critical space weather challenge due to sparse observations, complex coupling across geospatial

ConRetroBert: EMA Stabilized Dual Encoders for Template-Based Single-Step Retrosynthesis

arXiv:2605.12736v1 Announce Type: new Abstract: Template based single step retrosynthesis predicts reactants by selecting and applying an explicit reaction template, making each prediction traceable t

Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling

arXiv:2605.12754v1 Announce Type: new Abstract: Deep generative models provide state-of-the-art performance across a wide array of applications, with recent studies showing increasing applicability fo

applicationsarxiv-cs-lg

Contextual Bandits for Resource-Constrained Devices using Probabilistic Learning

arXiv:2605.13346v1 Announce Type: new Abstract: Contextual bandits (CB) are online sequential decision-making problems under partial feedback that underpin many adaptive services. There is a growing d

local-aiarxiv-cs-lg

Continual Fine-Tuning of Large Language Models via Program Memory

arXiv:2605.13162v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT), particularly Low-Rank Adaptation (LoRA), has become a standard approach for adapting Large Language Models (LLMs

Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs

arXiv:2605.12713v1 Announce Type: cross Abstract: In the field of quantum reservoir computing (QRC), many different computational models and architectures have been proposed. From these models, we ide

Tutorials

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces

arXiv:2605.12809v1 Announce Type: cross Abstract: A critical step for reliable large language models (LLMs) use in healthcare is to attribute predictions to their training data, akin to a medical case

tutorialsarxiv-cs-ai

CUBic: Coordinated Unified Bimanual Perception and Control Framework

arXiv:2605.13452v1 Announce Type: cross Abstract: Recent advances in visuomotor policy learning have enabled robots to perform control directly from visual inputs. Yet, extending such end-to-end learn

D-VLA: A High-Concurrency Distributed Asynchronous Reinforcement Learning Framework for Vision-Language-Action Models

arXiv:2605.13276v1 Announce Type: new Abstract: The rapid evolution of Embodied AI has enabled Vision-Language-Action (VLA) models to excel in multimodal perception and task execution. However, applyi

DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions

arXiv:2509.19538v2 Announce Type: replace-cross Abstract: Diffusion-based world models have demonstrated strong capabilities in synthesizing realistic long-horizon trajectories for offline reinforceme

Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models

arXiv:2605.13540v1 Announce Type: cross Abstract: Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learnin

applicationsarxiv-cs-ai

Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration

arXiv:2603.22273v4 Announce Type: replace Abstract: The process of discovery requires active exploration -- the act of collecting new and informative data. However, efficient autonomous exploration re

safetyarxiv-cs-lg

Di-BiLPS: Denoising induced Bidirectional Latent-PDE-Solver under Sparse Observations

arXiv:2605.13790v1 Announce Type: cross Abstract: Partial differential equations (PDEs) are fundamental for modeling complex natural and physical phenomena. In many real-world applications, however, o

applicationsarxiv-cs-ai

Differentiable Evolutionary Reinforcement Learning

arXiv:2512.13399v2 Announce Type: replace Abstract: Crafting effective reward signals remains a central challenge in Reinforcement Learning (RL), especially for complex reasoning tasks. Existing autom