Search: “benchmark”

VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning

arXiv:2603.01195v2 Announce Type: replace-cross Abstract: The effectiveness of multimodal instruction tuning depends not only on dataset scale, but critically on whether training samples genuinely req

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Visual-Language-Guided Task Planning for Horticultural Robots

DGX agent

arXiv:2601.11906v2 Announce Type: replace Abstract: Crop monitoring is essential for precision agriculture, but current systems lack high-level reasoning. We introduce a novel, modular framework that

model-releasesarxiv-cs-ro

26 Jun 2026

Vulnerability of Natural Language Classifiers to Evolutionary Generated Adversarial Text

arXiv:2606.27215v1 Announce Type: new Abstract: Deep learning models have achieved impressive performance across various fields but remain vulnerable to adversarial inputs, particularly in NLP, where

When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents

arXiv:2602.08995v2 Announce Type: replace Abstract: Computer-use agents (CUAs) have made tremendous progress in the past year, yet they still frequently produce misaligned actions that deviate from th

When are likely answers right? On Sequence Probability and Correctness in LLMs

arXiv:2606.27359v1 Announce Type: cross Abstract: Many decoding methods for large language models can be understood as shifting probability mass toward outputs that are more likely under the model, ei

When to Write and When to Suppress: Route-Specialized Dual Adapters for Memory-Assisted Knowledge Editing

arXiv:2606.14668v3 Announce Type: replace Abstract: Knowledge editing systems must update selected facts while preserving nearby but irrelevant behavior. This paper studies this problem in a memory-as

2K Retrofit: Entropy-Guided Efficient Sparse Refinement for High-Resolution 3D Geometry Prediction

arXiv:2603.19964v3 Announce Type: replace Abstract: High-resolution geometric prediction is essential for robust perception in autonomous driving, robotics, and AR/MR, but current foundation models ar

A 3D-Printable Dataset for Fair Testing and Comparisons of Tactile Sensors

arXiv:2606.25886v1 Announce Type: cross Abstract: Existing texture datasets for tactile sensing primarily consist of sensor readings from a specific sensor interacting with available surfaces/objects

A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids

arXiv:2606.25200v1 Announce Type: new Abstract: The accelerated digitalization of renewable energy smart grids through IoT sensors, AMI, and SCADA systems has significantly expanded the attack surface

A Systematic Analysis of Hybrid Linear Attention

arXiv:2507.06457v2 Announce Type: replace Abstract: Transformers face quadratic complexity and memory issues with long sequences, prompting the adoption of linear attention mechanisms using fixed-size

researcharxiv-cs-cl

Agent-as-a-Router: Agentic Model Routing for Coding Tasks

arXiv:2606.22902v2 Announce Type: replace Abstract: Real-world users typically have access to multiple Large Language Models (LLMs) from different providers, and these LLMs often excel at distinct dom

AI-Assisted Computational Reproducibility on the FABRIC Testbed

arXiv:2606.25879v1 Announce Type: cross Abstract: Computational reproducibility remains difficult despite being central to scientific research. In this paper, we show how the international FABRIC test

researcharxiv-cs-ai

AISPO: Enhancing Depth Reliability for Robotic Manipulation of Non-Lambertian Objects via Affine-Invariant Shape Prior

arXiv:2606.25503v1 Announce Type: cross Abstract: Reliable depth perception is critical for robotic manipulation, especially for non-Lambertian objects such as transparent or highly specular surfaces,

Are Tabular Foundation Models Robust to Realistic Query Distribution Shifts in Microbiome Data?

arXiv:2606.24995v1 Announce Type: new Abstract: Tabular foundation models (TFMs) achieve strong performance on microbiome abundance data, yet their robustness under realistic distribution shift remain

Are We There Yet? Exploring the Capabilities of MLLMs in Assistive AI Applications

arXiv:2606.25084v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have redefined visual understanding by combining vision encoders with large-scale language models. This unified

ArteryX: A Reliable End-to-End Toolbox for Standardized Intracranial Artery Feature Extraction from 3D TOF-MRA

arXiv:2507.07920v2 Announce Type: replace-cross Abstract: Cerebrovascular research heavily relies on quantitative analysis of intracranial arteries from time-of-flight magnetic resonance angiography,

safetyarxiv-cs-cv

Applications

Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints

arXiv:2603.11606v2 Announce Type: replace Abstract: Building high-fidelity digital twins of articulated objects from visual data remains a central challenge. Existing approaches depend on multi-view c

applicationsarxiv-cs-cv

BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

arXiv:2511.11421v2 Announce Type: replace Abstract: Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge. Vision-language models s

BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understanding

arXiv:2606.25400v1 Announce Type: new Abstract: Brain-Computer Interfaces (BCIs) and brain signal understanding are pivotal for clinical health and next-generation interactions. Despite this significa

Bridging Spherical Black-Box Optimizers

arXiv:2606.25761v1 Announce Type: new Abstract: When gradient information is unavailable, black-box optimization (BBO) methods provide a practical alternative. While Evolution Strategies (ES), Consens

CARE: Controlling LLM-Generated Policies through Auditable Review of Evidence in Scientific Experimentation

arXiv:2606.14581v2 Announce Type: replace Abstract: Granting LLMs direct control over costly, irreversible scientific experiments leads to unsafe exploration and unstable performance, but discarding L

CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection

arXiv:2606.25273v1 Announce Type: new Abstract: Zero-shot 3D anomaly detection is essential for industrial quality inspection, where labeled anomaly samples are scarce. Meanwhile, existing methods lac

researcharxiv-cs-cv

CoLA: Cross-Modal Low-rank Adaptation for Multimodal Downstream Tasks

arXiv:2604.03314v2 Announce Type: replace-cross Abstract: Foundation models have revolutionized AI, but adapting them efficiently for multimodal tasks, particularly in dual-stream architectures compos

Colon-Bench: An Agentic Workflow for Scalable Dense Lesion Annotation in Full-Procedure Colonoscopy Videos

arXiv:2603.25645v2 Announce Type: replace-cross Abstract: Early screening via colonoscopy is critical for colon cancer prevention, yet developing robust AI systems for this domain is hindered by the l

Communicability-Inspired Positional Encoding (CIPE)

arXiv:2606.25293v1 Announce Type: new Abstract: Positional encodings (PEs) are essential for Transformers. Yet designing effective PEs for non-Euclidean graphs remains challenging. Such encodings shou

safetyarxiv-cs-lg

ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure

arXiv:2602.01472v2 Announce Type: replace Abstract: Large reasoning models (LRMs) typically solve reasoning-intensive tasks by generating long chain-of-thought (CoT) traces, leading to substantial inf

researcharxiv-cs-cl

ConSolv: Solvent-Conditional Machine Learning Implicit Solvent Potential

arXiv:2606.24983v1 Announce Type: cross Abstract: Implicit solvent machine learning potentials (MLPs) offer a powerful route to bridging the gap between accuracy and efficiency in molecular simulation

Convex--Concave Quadratic Spectral Filtering for Graph Neural Networks

arXiv:2606.24956v1 Announce Type: new Abstract: Spectral graph neural networks (GNNs) interpret message passing as frequency-selective filtering. While low-order spectral filters are efficient, their

Agents

Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing

arXiv:2606.25332v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promise for automated penetration testing, yet existing end-to-end black-box evaluations are highly susceptibl

agentsarxiv-cs-ai

Decoupling Semantics and Geometric Grounding: Spatial Visual Prompts for Language-Conditioned Imitation Learning

arXiv:2606.25360v1 Announce Type: new Abstract: While end-to-end Vision-Language-Action (VLA) models show promise in robotic manipulation, their monolithic paradigm inherently couples semantic reasoni

safetyarxiv-cs-ro

Deep Neural Networks with Ordinal Loss for Medical Applications

arXiv:2606.25769v1 Announce Type: new Abstract: In many prediction problems in medical applications, target labels exhibit an inherent ordinal structure, where class ordering reflects clinically meani

DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning

arXiv:2606.25939v1 Announce Type: new Abstract: Demonstration augmentation is proposed for cost-efficient data acquisition, but existing methods are fundamentally limited in deformable manipulation du

safetyarxiv-cs-ro

Dense Supervision Is Not Enough: The Readout Blind Spot in Looped Language Models

arXiv:2606.24898v1 Announce Type: new Abstract: Looped language models turn hidden states into runtime state: each state is decoded for prediction and fed back into future computation. This creates a

Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning

arXiv:2606.26036v1 Announce Type: new Abstract: Training-time data poisoning during fine-tuning poses a significant threat to large language models (LLMs) deployed for abstractive text summarization,

Tutorials

Did Models Learn Sufficiently? Attribution-Guided Training via Subset-Selected Counterfactual Augmentation

arXiv:2511.12100v2 Announce Type: replace Abstract: In current visual model training, models often rely on only limited sufficient causes for their predictions, which makes them sensitive to distribut

tutorialsarxiv-cs-cv

Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography

arXiv:2606.25546v1 Announce Type: new Abstract: Vision-language pre-training (VLP) holds great promise for general-purpose medical AI by leveraging radiology reports as rich textual supervision, yet e

Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation

arXiv:2606.25782v1 Announce Type: new Abstract: With the widespread adoption of large language models (LLMs) in chatbots and everyday applications, companies increasingly need guardrails that are effe

Do vision-language models search like humans? Reasoning tokens as a reaction-time analog in classic visual-search paradigms

arXiv:2606.25066v1 Announce Type: cross Abstract: Visual search has been one of the most productive paradigms in the study of visual attention: the way reaction time scales with the number of items di

Dual Distribution Estimation for Zero-shot Noisy Test-Time Adaptation with VLMs

arXiv:2606.25758v1 Announce Type: new Abstract: While test-time adaptation (TTA) empowers vision-language models to adapt without costly retraining, it remains highly vulnerable to out-of-distribution

Efficient Analytic Uncertainty Quantification for Multi-Modal Regression

arXiv:2606.25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate und

Local Ai

Efficient and Trainable Language Model Test-Time Scaling via Local Branch Routing

arXiv:2606.25354v1 Announce Type: new Abstract: Test-time scaling improves language-model reasoning, but existing approaches often face a difficult trade-off: long chain-of-thought sampling remains si

local-aiarxiv-cs-cl

Efficient Real-World Dehazing via Physics-Inspired Global-Local Decoupling

arXiv:2606.25732v1 Announce Type: new Abstract: Real-world single image dehazing is highly ill-posed due to spatially and spectrally varying scattering, while practical deployment demands lightweight

Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models

arXiv:2606.25324v1 Announce Type: new Abstract: The computational complexity of Transformers scales quadratically with the number of tokens, which significantly constrains the efficiency of vision mod

Elo-Disentangled Player-Style Embeddings for Human Chess via Rating-Conditioned Residual Move Model

arXiv:2606.25176v1 Announce Type: new Abstract: We study representation learning for individual human chess style: a per-player embedding learned from a player's move history such that inner products

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval

arXiv:2512.00804v3 Announce Type: replace-cross Abstract: When answering user queries, LLMs often retrieve knowledge from external sources stored in retrieval-augmented generation (RAG) databases. The

EveLoad: Cognitive Workload Recognition from Event-Based Eye Movements

arXiv:2606.25177v1 Announce Type: new Abstract: Cognitive workload monitoring is important for adaptive rehabilitation and assistive interfaces, where task difficulty, pacing, and feedback should be a

Evidential Perfusion Physics-Informed Neural Networks with Residual Uncertainty Quantification

arXiv:2603.09359v2 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) have shown promise in addressing the ill-posed deconvolution problem in computed tomography perfusion (CTP)

Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis

arXiv:2606.25606v1 Announce Type: new Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for ea

researcharxiv-cs-cv