Search: “framework”

DreamStyle3D: Efficient 3D Stylized Asset Generation via Dual-Attention Disentanglement

arXiv:2607.24721v1 Announce Type: new Abstract: With the growth of gaming, animation, and virtual reality industries, the demand for efficient generation of stylized 3D assets is rapidly increasing. H

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DSTFView: Multi-View Cloud-Edge Workload Forecasting with Dual-Input Spatio-Temporal-Frequency Modeling

DGX agent

arXiv:2607.22565v1 Announce Type: new Abstract: With the widespread deployment of edge-side AI inference, edge platforms are increasingly required to support latency-sensitive, highly concurrent, and

researcharxiv-cs-ai

28 Jul 2026

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

arXiv:2607.23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent loc

Effective Receptive Field Ordering Matters for Infrared Small Target Detection

arXiv:2607.23994v1 Announce Type: new Abstract: In this work, we investigate a previously unexplored architectural dimension for infrared small target detection: the organization of effective receptiv

Efficiency Matters in Autonomous Research

arXiv:2607.24647v1 Announce Type: new Abstract: AI-driven autonomous research (AR) systems are becoming increasingly effective across a broad range of tasks. Their performance, however, is still evalu

Epistemic Norms for AI Safety and Alignment Research

arXiv:2607.24243v1 Announce Type: new Abstract: Mainstream AI research emphasises capability growth and tolerates low failure rates when average-case performance is high. AI safety and alignment resea

Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests

arXiv:2607.22883v1 Announce Type: cross Abstract: While Large Language Models (LLMs) show great promise for automating unit test generation, recent studies suggest that the quality of generated tests

Evaluating LLMs as Interpretable Controllers for Dynamical Systems

arXiv:2607.22609v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for decision-making and reasoning tasks, yet their potential as controllers for physical systems rema

Evaluating the Impact of Explainable AI on Trust in AI-Assisted Code Review

arXiv:2607.24601v1 Announce Type: cross Abstract: Background: Large language models (LLMs) are increasingly used to automate code review, but the reasoning behind their decisions remains hard to under

Evidence Attribution in Visual Document Understanding without Coordinates or Region Labels

arXiv:2607.24651v1 Announce Type: cross Abstract: Reliable visual document understanding requires a model to attribute each answer to the evidence regions that support it. Recent benchmarks and system

researcharxiv-cs-cl

Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution

arXiv:2607.22546v1 Announce Type: new Abstract: Machine translation (MT) systems continue to produce gender-biased translations. In a time where self-expression is paramount, mistranslations based on

researcharxiv-cs-cl

Fairness Interventions in Classification: A Study on AI Explainability

arXiv:2407.14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and

FedSLIM: Privacy-Preserving Federated MDL-Based Descriptive Pattern Mining Across Data Silos

arXiv:2607.23236v1 Announce Type: cross Abstract: Federated learning has achieved considerable success for predictive modelling, yet federated descriptive analytics remains largely unexplored. Existin

FilmBench: A Film-Grade Benchmark for Cinematic Video Generation

arXiv:2607.24241v1 Announce Type: cross Abstract: Progress in video generation keeps narrowing the visual gap between AI-generated and professionally produced footage, yet most benchmarks still draw p

Flow4R: Unifying 4D Reconstruction and Tracking with Scene Flow

arXiv:2602.14021v2 Announce Type: replace Abstract: Reconstructing and tracking dynamic 3D scenes is a fundamental challenge in computer vision. Existing methods typically decouple geometry from motio

Agents

From Cognitive Architectures to Language Agents: A Mechanism-Level Review of Lineage, Convergence, and Migration Gaps

arXiv:2607.23942v1 Announce Type: new Abstract: Memory, planning, reflection, and tool use are often compared as feature labels, obscuring the control semantics that determine how an agent actually ru

agentsarxiv-cs-ai

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How

arXiv:2607.22811v1 Announce Type: cross Abstract: Hybrid mechanistic/data-driven models, which combine first-principles with learned components, are increasingly used in process engineering and scient

From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search

arXiv:2607.24280v1 Announce Type: new Abstract: Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this

From Vibe to Code -- and Back: Lexical Oscillation in the Formation of Design Intent with Generative AI

arXiv:2607.23126v1 Announce Type: cross Abstract: Generative AI design tools make natural-language prompts a starting point for design, placing new articulation demands on designers. Rather than treat

gamma-Bridge: A Look-Parametric Diffusion Bridge

model-releasesarxiv-cs-cv

arXiv:2607.22719v1 Announce Type: new Abstract: Multiplicative Gamma noise is a signal-dependent degradation in coherent imaging; synthetic aperture radar (SAR) despeckling is its most prominent real-

Generative Artificial Intelligence (GenAI) to convert images of queuing networks into verifiable simulation models: an open-weight LLM workflow approach

arXiv:2607.24259v1 Announce Type: new Abstract: Recent work has explored the use of Large Language Models (LLMs) to automate simulation model building, typically by generating executable code directly

Agents

GNN-based Multi-Agent Control of Traffic Shockwaves in Sparse Vehicular Ad-hoc Networks

arXiv:2607.23792v1 Announce Type: cross Abstract: Traffic shockwaves are stop-and-go waves that propagate upstream through the streams of vehicles and are one of the major causes of traffic congestion

agentsarxiv-cs-lg

Guiding Language Models to Be More Empathetic: Culturally Sensitive Mental Health Advice Generation Through Human-LLM Collaboration

model-releasesarxiv-cs-cl

arXiv:2607.23538v1 Announce Type: new Abstract: Despite recent advances in large language models (LLMs), their ability to generate empathetic mental health counseling responses in low-resource languag

Local Ai

Harnessing X-ray Absorption Spectroscopy Data through Multimodal Mining of Battery Literature

arXiv:2607.23886v1 Announce Type: cross Abstract: X-ray absorption spectroscopy (XAS) is central to understanding the local electronic and atomic structure of materials, yet most published spectra rem

local-aiarxiv-cs-ai

Hierarchical Soft Actor-Critic for Sparse-Reward Long-Horizon Reinforcement Learning

arXiv:2607.23726v1 Announce Type: cross Abstract: Exploration in sparse-reward long-horizon tasks poses significant challenges for reinforcement learning. To address these challenges, we propose a two

safetyarxiv-cs-lg

Hybrid AI-Physical Modeling for Penetration Bias Correction in X-band InSAR DEMs: A Greenland Case Study

arXiv:2504.08909v2 Announce Type: replace Abstract: Digital elevation models derived from Interferometric Synthetic Aperture Radar (InSAR) data over glacial and snow-covered regions often exhibit syst

InterOCF: Spatio-Temporal 2D-3D Interaction for Camera-Only 4D Occupancy Forecasting

arXiv:2607.24431v1 Announce Type: new Abstract: Camera-only 4D occupancy forecasting enables autonomous vehicles to predict future 3D semantic scenes solely from historical multi-view images, which is

safetyarxiv-cs-cv

Local Ai

Intuitionistic j-Do-Calculus in Topos Causal Models

arXiv:2510.17944v2 Announce Type: replace-cross Abstract: In this paper, we generalize Pearl's do-calculus to an Intuitionistic setting called j-stable causal inference inside a topos of sheaves. Our

local-aiarxiv-cs-ai

Inverse Bayesian Inference for Extracting Lesion Dynamics from Longitudinal Spectral CT

model-releasesarxiv-cs-cv

arXiv:2607.23078v1 Announce Type: new Abstract: Longitudinal medical imaging captures temporal evolution of lesions, yet extracting the underlying dynamical parameters governing this evolution remains

Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics

arXiv:2607.22987v1 Announce Type: cross Abstract: Machinery fault detection (MFD) remains heavily reliant on supervised learning, which struggles with the scarcity of fault labels in real-world settin

Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks

arXiv:2607.24292v1 Announce Type: new Abstract: Autonomous free-flying robots in orbital environments require controllers that are both versatile and resource-efficient, yet maintaining a separate, ta

safetyarxiv-cs-ro

LEGO Co-builder: Exploring Fine-Grained Vision-Language Modeling for Multimodal LEGO Assembly Assistants

arXiv:2507.05515v4 Announce Type: replace Abstract: Vision-language models (VLMs) are facing the challenges of understanding and following multimodal assembly instructions, particularly when fine-grai

LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization

arXiv:2602.10576v2 Announce Type: replace-cross Abstract: Symbolic regression aims to distill mathematical equations from observational data. Recent approaches have successfully leveraged Large Langua

Low-light Image Enhancement via Multi-scale Attention combined with Fourier Transform

arXiv:2607.24002v1 Announce Type: new Abstract: Low-light image enhancement (LLIE) aims to improve image quality and clarity in diverse and demanding low-illumination environments. However, existing d

applicationsarxiv-cs-cv

LowAux-RDNet: Low-Pass Residual Supervision with Scene-Balanced Real-World Training for Single-Image Reflection Removal

model-releasesarxiv-cs-cv

arXiv:2607.22707v1 Announce Type: new Abstract: Single-image reflection removal aims to recover a clean transmission layer from one image captured through glass. We study an explicit decomposition pip

Machine Learning for Cloud Detection in IASI Measurements: A Data-Driven SVM Approach with Physical Constraints

arXiv:2508.10120v2 Announce Type: replace-cross Abstract: Cloud detection is fundamental for the interpretation and operational exploitation of hyperspectral infrared sounders, yet the capability of i

researcharxiv-cs-lg

MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection

arXiv:2607.15166v2 Announce Type: replace Abstract: Most medical AI benchmarks measure whether a model knows the correct answer. MedFailBench asks a different question: which safety boundary failed? W

MioFFAn: an Annotation Software for Formula Formalization with LLM Automation Capabilities

arXiv:2607.22552v1 Announce Type: new Abstract: The automatic translation of mathematical expressions in scientific literature into executable symbolic code (a process we refer to as Formula Formaliza

researcharxiv-cs-cl

Mission-Level Runtime Assurance for LLM-Assisted ISR Swarms over a Verification-Aware Fabric

arXiv:2607.23532v1 Announce Type: cross Abstract: Swarms of LLM-assisted autonomous robots are increasingly proposed for cooperative intelligence, surveillance, and reconnaissance (ISR) in contested e

MixQuant: Adaptive Mixed-Precision Quantization for Large Language Models

arXiv:2607.23047v1 Announce Type: cross Abstract: Mixed-precision quantization improves the accuracy of post-training quantization by allocating higher bitwidths to sensitive layers, but existing meth

Monitoring Post-Disaster Urban Recovery Using High-Resolution SAR Time Series and Unsupervised Learning: Evidence from the 2023 Turkiye-Syria Earthquake

arXiv:2607.24180v1 Announce Type: cross Abstract: Monitoring post-disaster recovery is essential for understanding how urban systems rebuild and progressively return to functionality. However, trackin

Moral Hazard in Multi-Agent Language Models

arXiv:2607.23982v1 Announce Type: cross Abstract: Cooperation can fail when socially valuable effort is costly, weakly observable, and mainly benefits others. Drawing on Holmstrom's team moral-hazard

Moving-Horizon Estimation and Nonlinear Model Predictive Control of Cable-Driven Soft Manipulators

arXiv:2607.24029v1 Announce Type: new Abstract: Precise control of soft manipulators remains challenging due to the difficulty of developing accurate yet computationally tractable models for model-bas

researcharxiv-cs-ro

Multi-Modal Scene Graph with Kolmogorov-Arnold Experts for Audio-Visual Question Answering

arXiv:2511.23304v2 Announce Type: replace Abstract: In this paper, we propose a novel Multi-Modal Scene Graph with Kolmogorov-Arnold Expert Network for Audio-Visual Question Answering (SHRIKE). The ta

Neural operator discovery from heterogeneous trajectories

arXiv:2607.23337v1 Announce Type: new Abstract: Neural operators provide data-driven mappings for modeling dynamical systems. Extending them to families of systems typically requires explicit conditio

applicationsarxiv-cs-lg

ObsDriveBench: Benchmarking Multimodal Understanding under Adverse Weather with Observability Awareness

arXiv:2607.23537v1 Announce Type: new Abstract: Autonomous driving under adverse weather remains a critical challenge, yet existing vision-language benchmarks mainly evaluate under standard conditions

OmniCache: Multidimensional Hierarchical Feature Caching For Diffusion Models

arXiv:2607.23844v1 Announce Type: new Abstract: High-resolution image and video diffusion models, including SD3, FLUX, and recent video diffusion transformers, have substantially improved generative q

On the Order-Conditional Optimality of Gaffke's Bound

model-releasesarxiv-cs-lg

arXiv:2607.22971v1 Announce Type: cross Abstract: Let X = (X_1, ldots, X_n) be a random vector from any Borel probability law on R_+^n. We revisit the problem of deriving a lower confidence bound (LCB