Search: “models”

pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development

arXiv:2607.21268v1 Announce Type: cross Abstract: In many social-science research tasks, such as economics, LLM-based agents must produce outputs for which no cheap, task-complete, machine-readable co

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

PISmith: Reinforcement Learning-based Red Teaming for Prompt Injection Defenses

DGX agent

arXiv:2603.13026v2 Announce Type: replace Abstract: Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents. Although many defenses have been propo

model-releasesarxiv-cs-lg

24 Jul 2026

QATMA: Quantization-Aware Training with Multimodal Alignment for Open-Vocabulary Object Detection

arXiv:2603.05964v3 Announce Type: replace Abstract: Quantizing open-vocabulary object detection (OVOD) models reduces their memory and computational costs, but extremely low-bit quantization severely

safetyarxiv-cs-cv

Reexamining zero-shot summarization: Empirical investigation of trustworthiness of LLM-summarizers

arXiv:2607.21010v1 Announce Type: new Abstract: Zero-shot summarization using Large Language Models (LLMs) has significantly advanced the abstractive summarization task by producing coherent and fluen

safetyarxiv-cs-ai

REFACT: Adaptive Fact Restatement for Compact and Faithful Chain-of-Thought Reasoning

arXiv:2607.20833v1 Announce Type: new Abstract: Large language models increasingly rely on long-form reasoning for complex tasks, yet their reasoning traces may drift away from the supplied context wh

local-aiarxiv-cs-cl

Relative Value Learning

arXiv:2607.21120v1 Announce Type: cross Abstract: In reinforcement learning, critics typically estimate absolute state values V(s), estimating how good a particular situation is in isolation. However,

Safety-oriented sidewalk and road segmentation for smartphone-based assistive navigation

arXiv:2607.21137v1 Announce Type: new Abstract: Independent sidewalk mobility is essential for blind and visually impaired pedestrians (BVIPs), yet smartphone-based assistive navigation requires perce

safetyarxiv-cs-cv

Spectral functions in Minkowski quantum electrodynamics from neural reconstruction

arXiv:2510.24728v2 Announce Type: replace-cross Abstract: We study neural reconstructions of quenched rainbow quantum electrodynamics (QED) Dyson--Schwinger benchmarks in Minkowski-related kinematics.

Applications

Synthetic data generation framework for quality control automation in gravure printing

arXiv:2607.21577v1 Announce Type: cross Abstract: Quality control in printing, particularly in rotogravure printing, still depends on slow, costly, and subjective manual inspection. Automated surface

applicationsarxiv-cs-ai

The Human-AI Substitution Principle: When will you be replaced by AI in your organization?

arXiv:2607.20781v1 Announce Type: new Abstract: Artificial Intelligence (AI) is rapidly transforming organizations, raising a fundamental organizational and economic question: when will a human employ

The RealDefocus Benchmark for Defocus Deblurring

arXiv:2607.21078v1 Announce Type: new Abstract: Single-Image Defocus Deblurring (SIDD) aims to recover an all-in-focus image from a single defocused observation, but rigorous and reproducible evaluati

torchsom: The Reference PyTorch Library for Self-Organizing Maps

arXiv:2510.11147v2 Announce Type: replace-cross Abstract: This paper introduces torchsom, an open-source Python library that provides a reference implementation of the Self-Organizing Map (SOM) in PyT

Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach

arXiv:2607.21417v1 Announce Type: new Abstract: Federated prompt tuning (FPT) enables collaborative adaptation of vision--language models (VLMs) using lightweight prompts. Existing methods often addre

local-aiarxiv-cs-cv

Uncertainty-Aware Trust Estimation for Multi-LLM Systems via Structured Expert Judgement

arXiv:2607.20529v1 Announce Type: cross Abstract: Large Language Model (LLM) ensembles are increasingly used to improve reliability by combining predictions from multiple LLMs. However, existing aggre

Verifier-First Evaluation of Agentic LLMs for Infrastructure-as-Code Generation

arXiv:2607.20478v1 Announce Type: cross Abstract: Infrastructure-as-Code (IaC) generation from natural language requires satisfying provider schemas, dependency planning, and organizational policy con

VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method

arXiv:2607.21400v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions. However, route-level instructions commonly encod

Weight-norm Criticality: A Mechanism for Loss Spikes Induced by the Normalization and Weight Decay

arXiv:2607.21005v1 Announce Type: new Abstract: Most explanations of training instability focus on learning-rate criticality, typically characterized by the Edge of Stability, beyond which optimizatio

Applications

Word meaning co-determines vowel-inherent spectral change. A corpus-based investigation of conversational Mandarin

arXiv:2607.21391v1 Announce Type: new Abstract: This study investigates vowel-inherent spectral change (VISC) in spontaneous conversational Mandarin. Using the generalized additive model and word embe

applicationsarxiv-cs-cl

4DGS360: 360{eg} Gaussian Reconstruction of Dynamic Objects from a Single Video

arXiv:2603.21618v2 Announce Type: replace Abstract: We introduce 4DGS360, a diffusion-free framework for 360^{irc} dynamic object reconstruction from casual monocular video. Existing methods often fai

Agents

Adaptive Capitulation: A Structural Failure Mode of LLM Responses in Vulnerability Contexts

arXiv:2607.19629v1 Announce Type: cross Abstract: Large language models operating in emotionally sensitive contexts face a structural trilemma: when users in vulnerable states request information that

agentsarxiv-cs-ai

Analytic Distribution of Classifier-Free Guidance for Schedule Design

arXiv:2607.19725v1 Announce Type: new Abstract: Classifier-free guidance (CFG) is the default mechanism for conditional generation in diffusion models, but the distribution sampled by its deterministi

researcharxiv-cs-lg

Audio-Zero: Label-Free Self-Evolution for Fine-Grained Audio Reasoning

arXiv:2607.20166v1 Announce Type: cross Abstract: Large Audio Language models (LALMs) have made rapid progress on acoustic understanding, yet they still struggle with fine-grained audio reasoning (e.g

ChronoStitch: Training-Free Composition of Visual KV Memories for Long-Horizon Temporal Reasoning

arXiv:2607.19547v1 Announce Type: new Abstract: Long-video question answering requires a model to preserve visual evidence over time without repeatedly reprocessing the same video. A practical approac

researcharxiv-cs-cv

CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs

arXiv:2607.19996v1 Announce Type: new Abstract: Machine Learning models are widely used for automating classification tasks by extracting statistical patterns from data. However, their performance det

CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representation

arXiv:2607.16577v2 Announce Type: replace Abstract: This paper presents a latent-space 3D shape editing framework built upon a coupled neural shape (CNS) representation and a neural feature volume opt

local-aiarxiv-cs-cv

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

researcharxiv-cs-cv

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

researcharxiv-cs-lg

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

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

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

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

emb-diversity: A Tool for Embedding-Based Measurement of Data Diversity

arXiv:2607.19848v1 Announce Type: new Abstract: There is growing evidence that data diversity is crucial for developing fair and robust NLP models. However, current approaches to measure diversity rem

researcharxiv-cs-cl

End-to-End Differential Privacy in Training Deep Neural Network Classifiers

arXiv:2607.19580v1 Announce Type: new Abstract: Differentially private machine learning enables model training on sensitive data while ensuring that individual data is unlikely to be recoverable from

researcharxiv-cs-lg

EvoDRC: A Self-Evolving Agentic Framework for Automated DRC Violation Repair

arXiv:2607.20019v1 Announce Type: new Abstract: Design rule check (DRC) closure remains a major bottleneck in advanced-node physical design. Although detailed routers are rule-aware, residual design r

Frequency-Hierarchical Active k-Space Sampling for Diagnostic MRI

arXiv:2607.19779v1 Announce Type: new Abstract: Active sampling for accelerated MRI must distribute a tight sampling budget across spatial frequencies that carry very different kinds of information. L

From Trajectories to Prefixes: Reusing Teacher Trajectories via Replayed Prefixes and Online Continuation

arXiv:2607.19395v1 Announce Type: cross Abstract: Small language models are attractive backbones for interactive agents, but direct distillation from strong teacher trajectories often turns rich multi

safetyarxiv-cs-ai

G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection

arXiv:2607.19942v1 Announce Type: cross Abstract: This work introduces G-MAD, an open-source framework that uses Arma3 to generate synchronized multi-view RGB-T data for aerial object detection. G-MAD

Geometry-Guided Constraint Learning for LLM Safety Classification

arXiv:2607.19366v1 Announce Type: new Abstract: Safety as Polytope (SaP) learns linear half-space constraints in LLM hidden space but requires per-category tuning of the constraint count K. We show th

IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion

arXiv:2607.19032v1 Announce Type: new Abstract: Existing Multi-view Anomaly Detection (MAD) methods assume that all views are completely available and model each view separately. However, in real indu

local-aiarxiv-cs-cv

Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion

arXiv:2503.22214v2 Announce Type: replace Abstract: The extraction of geoelectric structural information from airborne transient electromagnetic (ATEM) data primarily involves data processing and inve

ITPEval: Benchmarking Formal Translation Across Interactive Theorem Provers

arXiv:2607.19407v1 Announce Type: new Abstract: Formal theorem proving has emerged as a frontier challenge for machine learning, yet the ecosystem is fragmented: proofs remain siloed across incompatib

LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning

arXiv:2607.19391v1 Announce Type: cross Abstract: Low-Rank Adaptation is widely used for parameter-efficient fine-tuning, yet existing methods typically assign the same adapter rank to every transform

LC-SLab -- An object-based deep learning framework for large-scale land cover classification from satellite imagery and sparse in-situ labels

arXiv:2509.15868v2 Announce Type: replace Abstract: Large-scale land cover maps generated using deep learning play a critical role across a wide range of Earth science applications. Open in-situ datas

researcharxiv-cs-cv

Tutorials

Learning Semantic-Robust Change Detection via Semantic-Invariant Self-Distillation

arXiv:2607.19000v1 Announce Type: new Abstract: Change detection aims to identify semantic changes between remote sensing images. However, features from models are easily disturbed by non-semantic var

tutorialsarxiv-cs-cv

Local Label-Informed Feature Transfer for Generating Ground-Truth Medical Images: A Comparison of GAN- and Diffusion-Based Approaches

arXiv:2607.18882v1 Announce Type: new Abstract: Validating Explainable Artificial Intelligence (XAI) methods in medical imaging requires ground-truth data with known locations of informative features.

Long-Term Sequential Decision Making under Risk

arXiv:2607.19914v1 Announce Type: new Abstract: We study finite-horizon MDP planning under root-based (resolute) risk objectives that apply a rank-dependent functional to the distribution of total ret

NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation

model-releasesarxiv-cs-ro

arXiv:2607.19695v1 Announce Type: new Abstract: Robots deployed in delivery, campus, and emergency-response settings often need to navigate from buildings to streets within a single continuous episode

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training

arXiv:2607.20238v1 Announce Type: new Abstract: Visible-infrared (VIS-IR) alignment is a key pre-training task for robust multi-sensor perception. Most existing methods use uniform patch-wise contrast