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Defense Against LLM Backdoors using Critical Neuron Isolation Pruning

arXiv:2607.19894v1 Announce Type: cross Abstract: Large language models (LLMs) are vulnerable to backdoor attacks, where hidden triggers induce malicious outputs. Existing defenses generally fall into

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Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving

DGX agent

arXiv:2607.19774v1 Announce Type: new Abstract: Vehicle-to-everything-aided autonomous driving (V2X-AD) significantly enhances driving performance through information sharing. However, existing collab

agentsarxiv-cs-ro

23 Jul 2026

DeforM: Reasoning-Guided Physics-Aware Video Generation via Spatial-Temporal Masking

arXiv:2607.18664v1 Announce Type: new Abstract: Video generation models achieve high visual quality but often struggle to generate physics-aware videos. Unlike rigid-body motion, which can be describe

local-aiarxiv-cs-cv

Delineate Anything v2: A Global Foundation Model for Field Delineation

arXiv:2607.19069v1 Announce Type: new Abstract: Accurate agricultural field boundary delineation at large scale is a foundational task for food security, supply chain transparency, and carbon accounti

Denoising Monte Carlo Renders with Diffusion Models

arXiv:2404.00491v3 Announce Type: replace Abstract: Physically-based renderings contain Monte Carlo noise, with variance that increases as the number of rays per pixel decreases. This noise, while zer

Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream

arXiv:2607.19476v1 Announce Type: new Abstract: Detectors for AI-generated video are evaluated offline. A clip is decoded to pixels and scored once, increasingly by a large vision-language model. Dete

Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images

arXiv:2607.20087v1 Announce Type: new Abstract: Aims: Cardiovascular magnetic resonance (CMR) imaging enables non-invasive assessment of myocardial structure, function, and pathology, but requires sub

local-aiarxiv-cs-cv

Did Alice Do Wrong? Cross-Cultural Differences in Student Perceptions of Generative AI Use in University Computing Education

arXiv:2607.19699v1 Announce Type: cross Abstract: The rise of generative AI (GenAI) in higher education has prompted urgent debates surrounding academic integrity and ethical use. This study examines

local-aiarxiv-cs-ai

Differentially Private Neural Network Training Under the Hidden State Assumption

arXiv:2407.08233v3 Announce Type: replace Abstract: Current differentially private learning paradigms face a severe utility bottleneck: DP-SGD degrades performance through noise accumulation over trai

Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes

arXiv:2601.21026v2 Announce Type: replace-cross Abstract: Sampling configurations at thermodynamic equilibrium is a central challenge in statistical physics. Boltzmann Generators (BGs) tackle it by co

Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction

model-releasesarxiv-cs-lg

arXiv:2607.19919v1 Announce Type: cross Abstract: We propose Diffusion ReRoll, a diffusion-based framework for robotic sequential prediction that enables revisable denoising over horizons. Existing di

Digital Twin Modeling of a Highly Automated Agricultural Tractor

arXiv:2607.19912v1 Announce Type: cross Abstract: In efforts to increase research efficiency and availability, a digital twin of our research tractor (AMX G-trac) is created, focusing especially on th

applicationsarxiv-cs-ro

DINS-IO: Learned Inertial Odometry via Differentiable INS Consistency

arXiv:2607.20232v1 Announce Type: new Abstract: The training of learned inertial odometry depends on dense, high-precision position ground truth from motion capture, visual-inertial odometry or SLAM,

safetyarxiv-cs-ro

Directional Kernel Mean Difference: A Fast Signed Statistic for Univariate Distribution Comparison

arXiv:2607.20119v1 Announce Type: cross Abstract: We introduce the Directional Kernel Mean Difference (DKMD), a signed statistic for univariate distribution comparison that preserves the direction of

Distributed Acoustic Localization Array Deployed Using a Soft Everting Vine Robot

arXiv:2607.20392v1 Announce Type: new Abstract: Soft robot exteroception is increasingly being explored for a variety of field applications. In this work, we present a sound-based system for localizin

researcharxiv-cs-ro

Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats

arXiv:2607.20352v1 Announce Type: new Abstract: Aquatic self-reconfigurable robots must assemble into desired shapes while ensuring safe interactions among multiple agents. This paper proposes a hybri

safetyarxiv-cs-ro

Distributed Optimization via Energy Conservation Laws in Dilated Coordinates

arXiv:2409.19279v2 Announce Type: replace-cross Abstract: Continuous-time models can reveal accelerated structures in distributed optimization, but their rates need not survive direct discretization.

researcharxiv-cs-ai

Diverse-Intent Multi-Turn Fashion Image Retrieval

arXiv:2607.20291v1 Announce Type: new Abstract: Real-world fashion search involves interactive retrieval across multiple turns. However, existing multi-turn retrieval methods are built on a restrictiv

Do Sheaf Neural Networks Use Holonomy? A Measure--Intervene--Control Study

arXiv:2607.19514v1 Announce Type: new Abstract: Geometric architectures are often justified by internal mechanisms such as rotations, yet task performance alone cannot show whether those mechanisms dr

DobicVLM: Aligning Chest X-Ray Report Generation with Clinically-Grounded Programmatic Rewards via Group Relative Policy Optimization

arXiv:2607.18988v1 Announce Type: new Abstract: Medical imaging is a cornerstone of diagnostics, yet automated chest X-ray report generation struggles with structural adherence, anatomical completenes

DocOps: A Verifiable Benchmark for Autonomous Agents in Complex Document Operations

arXiv:2607.19865v1 Announce Type: new Abstract: As autonomous agents rapidly evolve, their ability to reliably manipulate ubiquitous digital documents has become critical for enabling general-purpose

DocShield: Towards AI Document Safety via Evidence-Grounded Agentic Reasoning

arXiv:2604.02694v2 Announce Type: replace-cross Abstract: The rapid progress of generative AI has enabled increasingly realistic text-centric image forgeries, posing major challenges to document safet

Domain-Adapted Power Curve for Cross-Farm Applications

arXiv:2607.19744v1 Announce Type: cross Abstract: The wind energy industry relies on accurate power curve models to make power forecast, evaluate turbine performance, quantify upgrade, or support site

Domain Shift in Echocardiography: Interpretable Quantification and Prediction of Cross-Dataset Left Ventricular Segmentation

arXiv:2607.19643v1 Announce Type: new Abstract: Cross-dataset generalisation remains a major barrier to clinical deployment of echocardiographic left ventricular segmentation, yet the sources of this

Dominant vs. Dominated: Concept-Level Generative Collapse in Diffusion Models

arXiv:2512.20666v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models have attracted significant attention for their ability to generate diverse, high-fidelity images. However, in m

Don't Trust the Label: License Laundering in AI Supply Chains

arXiv:2607.20300v1 Announce Type: cross Abstract: AI artifacts move through a multi-platform supply chain, spanning datasets and models on Hugging Face and applications on GitHub. While each artifact

researcharxiv-cs-ai

DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems

arXiv:2607.20225v1 Announce Type: cross Abstract: While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to expon

Agents

Dreamer-CPC: Message Learning with World Models for Decentralized Multi-agent Reinforcement Learning

arXiv:2607.19809v1 Announce Type: cross Abstract: In multi-agent reinforcement learning (MARL), inter-agent communication is effective for improving performance under partial observability. Representa

agentsarxiv-cs-lg

DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal

model-releasesarxiv-cs-lg

arXiv:2503.22223v2 Announce Type: replace Abstract: The semi-airborne transient electromagnetic method (SATEM) is capable of conducting rapid surveys over large-scale and hard-to-reach areas. However,

DRGBT-1K: A Large-scale High-quality Benchmark for Dynamic RGBT Tracking

arXiv:2607.19772v1 Announce Type: new Abstract: Dynamic RGBT (DRGBT) tracking aims to continuously localize a target when the available sensing modalities and observation platforms vary over time. Com

Drift-Aware RL-based Wavelet Denoising for Network-Traffic Anomaly Detection

arXiv:2607.20011v1 Announce Type: cross Abstract: Traffic-utilisation measurements for network monitoring are corrupted by additive noise and statistical drift: time-dependent change in the signal's m

safetyarxiv-cs-ai

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model

arXiv:2607.18958v1 Announce Type: new Abstract: While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs remain vulne

safetyarxiv-cs-cv

Dual-Edged Homogeneous-Modality Similarity: Towards Visible-Infrared Modality-Incomplete Person Re-Identification with Modality Adaptive Matching

arXiv:2607.18688v1 Announce Type: new Abstract: Visible-Infrared Person Re-Identification (VI-ReID) operates under a closed-world assumption, where queries and galleries are from heterogeneous modalit

DuSPiT: Dual-Branch Sub-Patch Pixel Diffusion Transformer

arXiv:2607.18510v1 Announce Type: new Abstract: Diffusion Transformers achieve strong image generation performance, but most operate in compressed latent spaces. Pixel-space diffusion avoids this info

Dynamical and Optimization Trade-offs of Levi--Civita Coordinates for Learned Close-Encounter Dynamics

arXiv:2607.20235v1 Announce Type: cross Abstract: Classical regularization removes the binary-collision singularity from the Kepler problem, but its value as a representation for learned Hamiltonian d

EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness

arXiv:2607.19880v1 Announce Type: new Abstract: Cross-embodiment navigation is a key challenge in embodied intelligence. Due to differences in embodiment, the same visual observation may imply differe

applicationsarxiv-cs-ro

EAR-Net: Pursuing End-to-End Absolute Rotations from Multi-View Images

arXiv:2310.10051v3 Announce Type: replace Abstract: Absolute rotation estimation is an important topic in 3D computer vision. Existing works in literature generally employ a multi-stage (at least two-

ECoNGS: Efficient Compressive Neural Gaussian Splats for Volume Visualization

arXiv:2607.18466v1 Announce Type: new Abstract: Recent advances in differentiable Gaussian splatting have highlighted the potential of primitive-based approaches as alternative scene representations f

Economic Evaluations of Language Models

arXiv:2607.19375v1 Announce Type: cross Abstract: Language models perform economically valuable work, yet they are not currently assessed for how well they perform every economically valuable task. We

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications

arXiv:2607.19676v1 Announce Type: new Abstract: Civil aviation is safety critical and its operations, from flight decks and towers to ramps and maintenance, generate massive, heterogeneous data at the

local-aiarxiv-cs-ai

Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era

arXiv:2602.16018v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation

local-aiarxiv-cs-lg

Efficient Chain-of-Modality Reasoning via Progressive Compression for Spoken Language Models

arXiv:2607.19932v1 Announce Type: new Abstract: Spoken language models (SLMs) enable natural human-computer interaction, but their reasoning ability still lags behind that of text-based large language

researcharxiv-cs-cl

Efficient Clustering with Provable Guardrails for LLM Inference at Scale

arXiv:2607.19704v1 Announce Type: new Abstract: Scaling LLM-based applications to millions of users is bottlenecked by the inference cost and latency of modern foundation models. A natural fix is to c

applicationsarxiv-cs-lg

Efficient Tracking and Understanding Object Transformations

arXiv:2607.19743v1 Announce Type: new Abstract: Tracking objects through state transformations is essential for understanding real-world dynamics. However, existing methods are computationally expensi

applicationsarxiv-cs-cv

Effort-Based Criticality Metrics for Evaluating 3D Perception Errors in Autonomous Driving

arXiv:2603.28029v2 Announce Type: replace-cross Abstract: Criticality metrics such as time-to-collision (TTC) quantify collision urgency but do not distinguish the operational consequences of false-po

safetyarxiv-cs-ro

EgoRecovery: Acquiring Failure Recovery Ability Through Human Recovery Demonstration

arXiv:2607.19745v1 Announce Type: new Abstract: Robust embodied robots should be able to recover from failures and retry tasks in order to operate reliably in unstructured and noisy real-world environ

applicationsarxiv-cs-ro

EGRNet: A Lightweight Semantic Segmentation Network with Edge-Gated Refinement and Adversarial Sensing

arXiv:2607.19617v1 Announce Type: new Abstract: As autonomous systems and smart cities continue to evolve, the demand for efficient and robust scene understanding becomes increasingly critical. Semant

safetyarxiv-cs-cv

ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers

arXiv:2607.20214v1 Announce Type: cross Abstract: The quadratic Nimes N attention score matrix remains a central obstacle to extending Transformers to longer input lengths. Existing efficient attentio

researcharxiv-cs-ai