Search: “benchmark”

The Forensic Cost of Watermark Removal

arXiv:2604.25491v1 Announce Type: new Abstract: Current watermark removal methods are evaluated on two axes: attack success rate and perceptual quality. We show this is insufficient. While state-of-th

The Thinking Pixel: Recursive Sparse Reasoning in Multimodal Diffusion Latents

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DGX agent

arXiv:2604.25299v1 Announce Type: new Abstract: Diffusion models have achieved success in high-fidelity data synthesis, yet their capacity for more complex, structured reasoning like text following ta

model-releasesarxiv-cs-cv

29 Apr 2026

TSN-Affinity: Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning

arXiv:2604.25898v1 Announce Type: new Abstract: Continual offline reinforcement learning (CORL) aims to learn a sequence of tasks from datasets collected over time while preserving performance on prev

UniSER: A Foundation Model for Unified Soft Effects Removal

arXiv:2511.14183v3 Announce Type: replace Abstract: Digital images are often degraded by soft effects such as lens flare, haze, shadows, and reflections, which reduce aesthetics even though the underl

tutorialsarxiv-cs-cv

Applications

VAE-Inf: A statistically interpretable generative paradigm for imbalanced classification

arXiv:2604.25334v1 Announce Type: new Abstract: Imbalanced classification remains a pervasive challenge in machine learning, particularly when minority samples are too scarce to provide a robust discr

applicationsarxiv-cs-lg

Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Models

arXiv:2604.25570v1 Announce Type: new Abstract: Spiking Transformers have shown strong potential for long-range visual modeling through spike-driven self-attention. However, their quadratic token inte

researcharxiv-cs-cv

VLM Judges Can Rank but Cannot Score: Task-Dependent Uncertainty in Multimodal Evaluation

arXiv:2604.25235v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used as automated judges for multimodal systems, yet their scores provide no indication of reliability.

Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution

arXiv:2604.03472v2 Announce Type: replace Abstract: Co-evolutionary self-play, where one language model generates problems and another solves them, promises autonomous curriculum learning without huma

safetyarxiv-cs-cl

Local Ai

We're open-sourcing Hy-MT1.5-1.8B-1.25bit — a 440MB translation model that runs fully offline on your phone, supports 33 languages, and outp…

We're open-sourcing Hy-MT1.5-1.8B-1.25bit — a 440MB translation model that runs fully offline on your phone, supports 33 languages, and outperforms Google Translate. At 1.8B parameters, it matches com

local-aiclem-delangue--x

What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective

arXiv:2604.25132v1 Announce Type: new Abstract: Instruction-tuning datasets often contain substantial redundancy and low-quality samples, necessitating effective data selection methods. We propose an

researcharxiv-cs-cl

When the Forger Is the Judge: GPT-Image-2 Cannot Recognize Its Own Faked Documents

arXiv:2604.25213v1 Announce Type: new Abstract: OpenAI's GPT-Image-2 has effectively erased the visual boundary between authentic and AI-edited document images: a single number on a receipt can be rep

When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs

arXiv:2510.07499v2 Announce Type: replace Abstract: Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge

Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective

arXiv:2603.14248v2 Announce Type: replace-cross Abstract: Large language model (LLM) web agents are increasingly used for web navigation but remain far from human reliability on realistic, long-horizo

researcharxiv-cs-cl

2D Pre-Training for 3D Pose Estimation

arXiv:2604.22830v1 Announce Type: new Abstract: Pre-training is a general method that is used in a range of deep learning tasks. By first training a model on one task, and then further training on the

tutorialsarxiv-cs-cv

6thGrid-Net: Unified Remote Sensing Image Dehazing Based on Color Restoration and Edge-Preserving

arXiv:2604.24149v1 Announce Type: new Abstract: Remote sensing images are frequently degraded by adverse weather conditions, particularly clouds and haze, which severely impair downstream applications

A BERTology View of LLM Orchestrations: Token- and Layer-Selective Probes for Efficient Single-Pass Classification

arXiv:2601.13288v2 Announce Type: replace Abstract: Production LLM systems often rely on separate models for safety and other classification-heavy steps, increasing latency, VRAM footprint, and operat

A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs

arXiv:2603.07475v2 Announce Type: replace Abstract: Autoregressive (AR) language models build representations incrementally via left-to-right prediction, while diffusion language models (dLLMs) are tr

safetyarxiv-cs-cl

A Fano-Style Accuracy Upper Bound for LLM Single-Pass Reasoning in Multi-Hop QA

arXiv:2509.21199v3 Announce Type: replace Abstract: Multi-Hop Question Answering (MHQA) requires integrating dispersed, interdependent evidence through sequential reasoning under noise. This task is c

A Heterogeneous Two-Stream Framework for Video Action Recognition with Comparative Fusion Analysis

arXiv:2604.23415v1 Announce Type: new Abstract: Most two-stream action recognition networks apply the same convolutional backbone to both RGB and optical flow streams, ignoring the fact that the two m

researcharxiv-cs-cv

A Hierarchical Self-Consistent Regularization Approach to Satellite Image Time Series Classification

arXiv:2510.04916v2 Announce Type: replace Abstract: Deep learning has become increasingly important in remote sensing image classification due to its ability to extract semantic information from compl

Agents

A Systematic Approach for Large Language Models Debugging

arXiv:2604.23027v1 Announce Type: new Abstract: Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based re

agentsarxiv-cs-ai

A systematic evaluation of vision-language models for observational astronomical reasoning tasks

arXiv:2604.24589v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly proposed as general-purpose tools for scientific data interpretation, yet their reliability on real astro

Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy

arXiv:2604.23821v1 Announce Type: cross Abstract: Autonomous neutron spectroscopy must solve three distinct tasks: detection (where is the signal?), inference (which Hamiltonian governs it?), and refi

Applications

AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting

arXiv:2604.23239v1 Announce Type: new Abstract: Accurate long-term time series forecasting (LTSF) requires the capture of complex long-range dependencies and dynamic periodic patterns. Recent advances

applicationsarxiv-cs-ai

Adaptive-Distribution Randomized Neural Networks for PDEs: A Low-Dimensional Distribution-Learning Framework

arXiv:2604.23999v1 Announce Type: cross Abstract: Randomized neural networks (RaNNs) are attractive for partial differential equations (PDEs) because they replace expensive end-to-end training with a

Adaptive ToR: Complexity-Aware Tree-Based Retrieval for Pareto-Optimal Multi-Intent NLU

arXiv:2604.24219v1 Announce Type: new Abstract: Multi-intent natural language understanding requires retrieval systems that simultaneously achieve high accuracy and computational efficiency, yet exist

AdaRubric: Task-Adaptive Rubrics for LLM Agent Evaluation

arXiv:2603.21362v2 Announce Type: replace Abstract: LLM-as-Judge evaluation fails agent tasks because a fixed rubric cannot capture what matters for this task: code debugging demands Correctness and E

safetyarxiv-cs-ai

Agent-Centric Visual Reinforcement Learning under Dynamic Perturbations

model-releasesarxiv-cs-ro

arXiv:2604.24661v1 Announce Type: new Abstract: Visual reinforcement learning aims to empower an agent to learn policies from visual observations, yet it remains vulnerable to dynamic visual perturbat

AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications

arXiv:2512.20164v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) excel at text comprehension and generation, making them ideal for automated tasks like code review and content mo

An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations

arXiv:2604.23290v1 Announce Type: cross Abstract: Active learning algorithms automatically identify the most informative samples from large amounts of unlabeled data and tremendously reduce human anno

An Efficient Beam Search Algorithm for Active Perception in Mobile Robotics

model-releasesarxiv-cs-ro

arXiv:2604.23327v1 Announce Type: new Abstract: Active perception is a fundamental problem in autonomous robotics in which the robot must decide where to move and what to sense in order to obtain the

An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code

arXiv:2604.23361v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance on a wide range of software engineering tasks, including code generation and analysi

Animalbooth: multimodal feature enhancement for animal subject personalization

arXiv:2509.16702v2 Announce Type: replace Abstract: Personalized animal image generation is challenging due to rich appearance cues and large morphological variability. Existing approaches often exhib

safetyarxiv-cs-cv

Agents

Anonymization-Enhanced Privacy Protection for Mobile GUI Agents: Available but Invisible

arXiv:2602.10139v3 Announce Type: replace-cross Abstract: Mobile Graphical User Interface (GUI) agents have demonstrated strong capabilities in automating complex smartphone tasks by leveraging multim

agentsarxiv-cs-ai

ATTN-FIQA: Interpretable Attention-based Face Image Quality Assessment with Vision Transformers

arXiv:2604.22841v1 Announce Type: new Abstract: Face Image Quality Assessment (FIQA) aims to assess the recognition utility of face samples and is essential for reliable face recognition (FR) systems.

Applications

Au-M-ol: A Unified Model for Medical Audio and Language Understanding

arXiv:2604.23284v1 Announce Type: cross Abstract: In this work, we present Au-M-ol, a novel multimodal architecture that extends Large Language Models (LLMs) with audio processing. It is designed to i

applicationsarxiv-cs-ai

AutoRISE: Agent-Driven Strategy Evolution for Red-Teaming Large Language Models

arXiv:2604.22871v1 Announce Type: cross Abstract: Automated red-teaming methods for large language models typically optimize attack prompts within a fixed, human-designed strategy, leaving the attack

AV-Master: Dual-Path Comprehensive Perception Makes Better Audio-Visual Question Answering

arXiv:2510.18346v2 Announce Type: replace Abstract: Audio-Visual Question Answering (AVQA) requires models to effectively utilize both visual and auditory modalities to answer complex and diverse ques

tutorialsarxiv-cs-cv

Aycromo: An Open-Source Platform for Automatic Chromosome Detection in Metaphase Images Based on Deep Learning

arXiv:2604.24685v1 Announce Type: new Abstract: Chromosome analysis is a fundamental step in the diagnosis of genetic diseases, but the manual karyotyping workflow is time-consuming and heavily depend

researcharxiv-cs-cv

BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal

arXiv:2604.24428v1 Announce Type: cross Abstract: Electroencephalography (EEG) is highly susceptible to artifact contamination, such as electrooculographic (EOG) and electromyographic (EMG) interferen

Bayesian Optimization for Function-Valued Responses under Min-Max Criteria

arXiv:2512.07868v2 Announce Type: replace-cross Abstract: Bayesian optimization is widely used for optimizing expensive black box functions, but most existing approaches focus on scalar responses. In

researcharxiv-cs-ai

Betting for Sim-to-Real Performance Evaluation

arXiv:2604.24018v1 Announce Type: new Abstract: This paper studies the problem of robot performance evaluation, focusing on how to obtain accurate and efficient estimates of real-world behavior under

safetyarxiv-cs-ro

Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories

arXiv:2510.17381v2 Announce Type: replace Abstract: Detecting out-of-distribution (OOD) data is critical for machine learning, be it for safety reasons or to enable open-ended learning. However, beyon

safetyarxiv-cs-lg

Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen LLMs

arXiv:2602.02556v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are largely static and often redo reasoning or repeat mistakes. Prior experience reuse typically relies on extern

tutorialsarxiv-cs-ai

Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition

arXiv:2604.23413v1 Announce Type: new Abstract: Cloud-hosted Large Language Models (LLMs) offer unmatched reasoning capabilities and dynamic knowledge, yet submitting raw queries to these external ser

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment

arXiv:2604.24273v1 Announce Type: new Abstract: The deployment of intelligent reinforcement learning (RL) agents on resource-constrained edge devices remains a fundamental challenge due to the substan

Boosting MLLM Spatial Reasoning with Geometrically Referenced 3D Scene Representations

arXiv:2603.08592v2 Announce Type: replace Abstract: While Multimodal Large Language Models (MLLMs) have achieved remarkable success in 2D visual understanding, their ability to reason about 3D space r

Bridging Reasoning and Action: Hybrid LLM-RL Framework for Efficient Cross-Domain Task-Oriented Dialogue

arXiv:2604.23345v1 Announce Type: new Abstract: Cross-domain task-oriented dialogue requires reasoning over implicit and explicit feasibility constraints while planning long-horizon, multi-turn action

safetyarxiv-cs-cl