Search: “evaluation”

Detector-Evasive LLM Paraphrasing via Constrained Policy Optimization

arXiv:2606.00392v1 Announce Type: cross Abstract: AI-text detectors are vulnerable to paraphrasing and detector-guided paraphrasing attacks, but existing detector-evasion methods often lack precise co

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Dexterity-BEV: Aligning 3D World and Actions for Generalizable Robot Policies Learning

DGX agent

arXiv:2606.02274v1 Announce Type: new Abstract: End-to-end manipulation policies, combined with web-scale pretrained Vision-Language Models (VLMs), show the promise for generalizable and dexterous rob

safetyarxiv-cs-ro

2 Jun 2026

Dialectics of Alignment: Harnessing Unsafe Knowledge for Dynamic Safety Routing

arXiv:2606.00686v1 Announce Type: new Abstract: The prevailing paradigm in large language model (LLM) alignment operates via erasure, filtering unsafe data or training models to strictly refuse harmfu

safetyarxiv-cs-lg

Digging Up Citations: FOSSIL, a Dataset and Workflow for Reference Extraction in Law and the Humanities

arXiv:2606.01109v1 Announce Type: cross Abstract: Citation extraction tools are designed for the structured end-of-document bibliographies of the natural sciences, but law and humanities scholarship c

researcharxiv-cs-cl

Applications

Digital-to-Physical Transfer of Adversarial Patches for Aerial Vehicle Detection

arXiv:2606.00159v1 Announce Type: cross Abstract: Deep neural network (DNN)-based object detectors are widely used for analyzing aerial and satellite imagery in applications such as environmental moni

applicationsarxiv-cs-ai

Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding in Vision-Language-Action Policies

arXiv:2508.20072v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models adapt large vision-language backbones to map images and instructions into robot actions. However, prevailing VLA

researcharxiv-cs-cv

Disentangling Similarity and Relatedness in Topic Models

arXiv:2603.10619v2 Announce Type: replace Abstract: The recent success of large pre-trained language models (PLMs) has motivated their integration into topic modeling. However, PLM-augmented topic mod

DisFlow: Scene Flow from Distance Field for Object Pose, Velocity Tracking, and Dynamic Object Reconstruction

arXiv:2606.01824v1 Announce Type: new Abstract: We present DisFlow, a novel framework for online scene flow estimation from distance field that enables 6DoF dynamic object pose estimation, motion trac

researcharxiv-cs-ro

Distillation of Large Language Models via Concrete Score Matching

arXiv:2509.25837v3 Announce Type: replace-cross Abstract: Large language models (LLMs) deliver remarkable performance but are costly to deploy, motivating knowledge distillation (KD) for efficient inf

Distilling Neuro-Symbolic Programs into 3D Multi-modal LLMs

arXiv:2606.01215v1 Announce Type: cross Abstract: Current 3D spatial reasoning methods face a fundamental trade-off: neuro-symbolic 3D (NS3D) concept learners achieve interpretable reasoning through c

Tutorials

Distortion-Aware Fusion of Statistical and Vision-Language Features for Blind Image Quality Assessment

arXiv:2606.02002v1 Announce Type: new Abstract: Blind image quality assessment (BIQA) aims to predict perceived image quality without access to a reference image. Classical natural scene statistics (N

tutorialsarxiv-cs-cv

DLLM-JEPA: Joint Embedding Predictive Architectures for Masked Diffusion Language Models

arXiv:2606.00091v1 Announce Type: cross Abstract: Joint Embedding Predictive Architectures (JEPAs) have reshaped self-supervised representation learning in vision. The recent LLM-JEPA ported JEPA to a

Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gains

arXiv:2606.02357v1 Announce Type: cross Abstract: Tool-augmented multimodal agents show strong benchmark gains, often taken as evidence that agents have learned to use tools. We argue that this interp

Do Text Edits Generalize to Visual Generation? Benchmarking Cross-Modal Knowledge Editing in UMMs

arXiv:2606.00477v1 Announce Type: new Abstract: Unified multimodal models (UMMs) have emerged as a promising paradigm for general-purpose multimodal intelligence. As they are deployed in real-world ap

Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization

arXiv:2606.02345v1 Announce Type: cross Abstract: Many machine learning problems, including similarity learning, ranking, and clustering, rely on empirical pairwise loss functions whose quadratic comp

Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing

arXiv:2606.01393v1 Announce Type: cross Abstract: Document parsing and recognition are fundamental capabilities for vision-language models (VLMs) and document processing systems. However, existing Opt

DraDDP: A Multimodal Multi-Party Dialogue Discourse Parsing Dataset

arXiv:2606.00012v1 Announce Type: cross Abstract: Multi-party dialogue discourse parsing aims to identify dependency structures and relation types between utterances in conversations. Previous studies

Drifting Preference Optimization for One-Step Generative Models

arXiv:2606.02521v1 Announce Type: cross Abstract: One-step text-to-image generators are attractive for deployment because they generate an image with a single forward pass, but preference finetuning t

safetyarxiv-cs-cv

DriveAnchor: Progressive Anchor-based Flow Learning for Autonomous Driving Planning

arXiv:2606.00519v1 Announce Type: new Abstract: We present DriveAnchor, a three-stage framework for autonomous driving planning that achieves behavioral diversity, controllability, and safety in a com

safetyarxiv-cs-ro

DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties

arXiv:2606.00313v1 Announce Type: cross Abstract: Robust deployment of deep reinforcement learning (DRL) policies on real robots remains challenging due to discrepancies between simulation and real-wo

DrugClaw and DrugAudit: A Primary-Source-Grounded Agent and Authority-Aware Benchmark for Drug-Information Question Answering

arXiv:2606.01434v1 Announce Type: new Abstract: Drug-information question answering is a high-stakes setting where hallucinated facts can mislead clinical decision-making and the provenance of each ci

Hardware

DuetServe: Harmonizing Prefill and Decode for LLM Serving via Adaptive GPU Multiplexing

arXiv:2511.04791v2 Announce Type: replace Abstract: Modern LLM serving systems must sustain high throughput while meeting strict latency SLOs across two distinct inference phases: compute-intensive pr

hardwarearxiv-cs-lg

Dynamic Coordination Strategy Selection for Enterprise Multi-Agent Systems

arXiv:2606.00804v1 Announce Type: cross Abstract: Enterprise multi-agent systems increasingly expose multiple coordination patterns, but deployments often lack evidence for when to use consensus, deba

Agents

Dynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus

arXiv:2606.01828v1 Announce Type: cross Abstract: Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specializati

agentsarxiv-cs-ai

Tutorials

E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation

arXiv:2606.01634v1 Announce Type: cross Abstract: Generating realistic time series is essential for scientific research and real-world applications. However, existing methods often emphasize overall d

tutorialsarxiv-cs-ai

Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking

model-releasesarxiv-cs-lg

arXiv:2602.03912v4 Announce Type: replace Abstract: This paper investigates the performance of Echo State Networks (ESNs) for univariate forecasting of monthly and quarterly time series from the M4 Fo

Edge-aware Decoding for Neural Asymmetric Routing

arXiv:2606.02136v1 Announce Type: new Abstract: Neural asymmetric routing models increasingly encode directionality through matrix representations and asymmetry-aware attention. The final routing acti

Edge-Based QoS-Aware Adaptive Task Placement: A Closed-Loop Control in Multi-Robot Systems

arXiv:2606.00552v1 Announce Type: cross Abstract: Multi-robot systems (MRS) increasingly offload compute-intensive perception tasks to edge nodes to meet strict time-sensitive Quality-of-Service (QoS)

researcharxiv-cs-ro

Edge Prediction for Roof Wireframe Reconstruction with Transformers

arXiv:2606.02406v1 Announce Type: new Abstract: This paper presents a competitive solution to the S23DR Challenge 2026, which aims to reconstruct 3D house roof wireframe models from sparse SfM point c

researcharxiv-cs-cv

EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction

arXiv:2606.02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.

Efficient Exploration for Iterative Nash Preference Optimization

arXiv:2606.01382v1 Announce Type: cross Abstract: Preference alignment is central to improving large language models, but standard reward-based formulations can be restrictive when human preferences a

Efficient Weighted Sampling via Score-based Generative Models

arXiv:2502.04646v2 Announce Type: replace-cross Abstract: Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is

Ego-METAS: Egocentric online Multimodal Energy-efficient Temporal Action Segmentation benchmark

model-releasesarxiv-cs-cv

arXiv:2606.02246v1 Announce Type: new Abstract: To operate in the physical world, embodied agents must perceive their environment in an 'always-on' fashion, selectively accessing the most informative

Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying

arXiv:2606.00151v1 Announce Type: cross Abstract: In reinforcement learning (RL), agents benefit from exploration only because they repeatedly encounter similar states: trying different actions can im

Emergent Ordinal Geometry in Transformers Trained on Local Comparisons

arXiv:2606.01269v1 Announce Type: new Abstract: Transitive inference is the challenge of inferring that A < C from knowing only adjacent relations (A < B, B < C). It is solved by humans and animals no

Applications

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction

arXiv:2606.00506v1 Announce Type: new Abstract: Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning. Although advanced m

applicationsarxiv-cs-ai

Enhancing Blind Source Separation with Dissociative Principal Component Analysis

model-releasesarxiv-cs-cv

arXiv:2411.12321v2 Announce Type: replace Abstract: Principal component analysis (PCA) and its sparse variants (sPCA) are widely used as a precursor to independent component analysis (ICA) for blind s

Enhancing LLM Metacognition via Cognitive Pairwise Training

model-releasesarxiv-cs-lg

arXiv:2606.00869v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to

Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imaging

arXiv:2606.02339v1 Announce Type: cross Abstract: Entropy minimization (EM) is the dominant objective for test-time adaptation, yet its failure mode, model collapse, remains poorly understood. In this

safetyarxiv-cs-cv

ERICA: Quantifying Replicability of Cluster Analysis

arXiv:2606.00302v1 Announce Type: cross Abstract: Despite being ubiquitous in science, clustering remains a technique whose results are not quantitatively scrutinized via a framework. We present an an

Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding

arXiv:2603.03312v3 Announce Type: replace-cross Abstract: Decoding natural language from non-invasive EEG signals is a promising yet challenging task. However, current state-of-the-art models remain c

Estimating Mutual Information between Time Series and Temporal Event Sequences Across Diverse Analysis Tasks

arXiv:2606.01602v1 Announce Type: cross Abstract: Pairwise dependence measures such as correlation and causality are fundamental to temporal data mining, yet there is still no principled and robust wa

EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks

arXiv:2606.01767v1 Announce Type: new Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-s

Local Ai

Explainable AI Through a Democratic Lens: DhondtXAI for D'Hondt-Projected Feature Attribution

arXiv:2411.05196v3 Announce Type: replace Abstract: This study presents DhondtXAI as a SHAP-independent, D'Hondt-based attribution framework for tabular XAI. Instead of model-native feature importance

local-aiarxiv-cs-ai

Exploiting In-Sensor Computing for Energy-Efficient Earth Observation

arXiv:2606.01271v1 Announce Type: new Abstract: The rapid growth of the satellite industry has driven a significant increase in geospatial data acquisition, highlighting a critical bottleneck: the sev

researcharxiv-cs-cv

Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks

arXiv:2602.02763v3 Announce Type: replace Abstract: Interpretable time series deep learning systems are often assessed by checking temporal consistency on explanations, implicitly treating this as evi

ExpWeaver: LLM Agents Learn from Experience via Latent RAG

arXiv:2606.01041v1 Announce Type: new Abstract: Experience learning has achieved promising results in enhancing LLM agent planning and reasoning by integrating past interactions as reusable knowledge.

FACT: A Simple and Efficient Framework for Active Finetuning

model-releasesarxiv-cs-cv

arXiv:2606.02079v1 Announce Type: new Abstract: The main goal of active finetuning is to improve a pretrained model's performance on a specific task or domain by finetuning it with carefully selected