Search: “papers”

A New Strategy for Artificial Intelligence: Training Foundation Models Directly on Human Brain Data

arXiv:2601.12053v2 Announce Type: replace-cross Abstract: While foundation models have achieved remarkable results across a diversity of domains, they still rely on human-generated data, such as text,

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Research

A Theoretical Analysis of Generalization Dynamics in Neural Networks under Gradient Descent with Weight Decay

DGX agent

arXiv:2609.07755v1 Announce Type: new Abstract: Understanding generalization remains a central challenge in machine learning because it requires jointly considering data, architecture, and training dy

researcharxiv-cs-lg

9 Sept 2026

A Theoretical Analysis of Provable Compositional Generalization in Neural Networks: A Necessary and Sufficient Condition

arXiv:2505.02627v2 Announce Type: replace Abstract: Compositional generalizationnicode{x2013}the ability to systematically process novel combinations of known componentsnicode{x2013}is a hallmark of h

safetyarxiv-cs-lg

A Theoretical Framework for Masked Pretraining (MPT)

arXiv:2609.06460v1 Announce Type: new Abstract: Recently, Masked Pretraining (MPT) based on reconstruction pretraining tasks has risen to a promising self-supervised learning paradigm across various d

safetyarxiv-cs-lg

AAMBERS-UAV: Acquisition-Aware Multimodal Backbone Evaluation and Ranking for UAV Weedy Rice Segmentation

arXiv:2609.05762v1 Announce Type: new Abstract: UAV image collections contain spatially and temporally related frames, yet semantic-segmentation benchmarks commonly split them at image level. Such spl

Accelerated Spatio-Temporal Gaussian Rendering via Kinematic and Semantic Priors

arXiv:2605.25909v2 Announce Type: replace Abstract: Reconstructing dynamic 3D scenes from multi-view videos is a foundational task for robotics, AR/VR, and digital twins. While 3D Gaussian Splatting (

Accelerating Diffusion Transformers with Gaussian Process Rectified Feature Cache

arXiv:2609.05981v1 Announce Type: new Abstract: Diffusion Transformers have become the dominant paradigm in generative AI, but their high computational costs severely hinder real-time applications. Pr

Agents

Adaptive Cost-Sensitive Machine Learning for Autonomous Robot Navigation Failure Prediction: When Not All Errors Are Equal

arXiv:2609.05593v1 Announce Type: new Abstract: Autonomous robot navigation failures differ not only in categorical severity but also in the physical context in which they occur. A near-miss at low sp

agentsarxiv-cs-ro

Tutorials

ADELE - Adaptive Delaunay Grids for High-Fidelity Mesh-Native Reconstruction

arXiv:2609.06723v1 Announce Type: new Abstract: Meshes remain the most practical representation for geometry reasoning and integration into graphics pipelines, yet existing reconstruction methods stru

tutorialsarxiv-cs-cv

Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

arXiv:2406.04314v4 Announce Type: replace Abstract: Generating visually appealing images is fundamental to modern text-to-image generation models. A potential solution to better aesthetics is direct p

Tutorials

AI and TCAD for Inverse Design and Defect Discovery: From Simple Machine Learning to LLM

arXiv:2609.07046v1 Announce Type: new Abstract: AI has revolutionized various engineering domains, but its impact on semiconductor device design and defect discovery is still limited, due to limited d

tutorialsarxiv-cs-lg

alpha-Graph: Attention-Infused Normalizing Flow Approach to Tractable Graph Modeling

arXiv:2609.07961v1 Announce Type: cross Abstract: Graph modeling, a crucial task for representing complex relationships in graph-structured data, has achieved significant success in recent years. Howe

Architectural and Regularization Components in Deep Learning Medical Image Registration: Systematic Ablation Study

arXiv:2609.05484v1 Announce Type: new Abstract: Deep learning registration methods routinely stack two kinds of enhancement on a base network: architectural additions such as affine pre-alignment stag

Asymptotically-informed neural networks for Black-Scholes implied volatility computation

arXiv:2609.05491v1 Announce Type: cross Abstract: The computation of Black-Scholes implied volatility is a fundamental task in quantitative finance, underpinning option valuation, model calibration an

Attention-Enhanced Deep Features with Heterogeneous Ensemble Learning for Glaucoma Detection

arXiv:2609.06699v1 Announce Type: new Abstract: Glaucoma is a progressive optic neuropathy characterized by irreversible damage to the optic nerve, making timely diagnosis critical to prevent permanen

Boundary Voting Network for Ambiguity-Aware Timestamp-Supervised Action Segmentation

arXiv:2609.08167v1 Announce Type: new Abstract: Timestamp-supervised action segmentation aims to segment and classify actions in untrimmed videos with a random frame annotated per action. Precisely lo

Budgeted Task-Aware Acquisition of Dynamic Networks

arXiv:2609.05862v1 Announce Type: new Abstract: Learning on dynamic graphs is difficult when changes in the underlying network are only partially observed. Acquiring current graph information incurs o

CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs

arXiv:2404.06349v3 Announce Type: replace Abstract: The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual re

Characterizing Privacy-Audit Alignment in Behavioral Audit of Machine Unlearning

arXiv:2606.14518v2 Announce Type: replace Abstract: The removal of learned data from Machine Learning models through Machine Unlearning (MU) has been widely studied; however, there is no agreed-upon s

Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access

arXiv:2609.05702v1 Announce Type: cross Abstract: Quantum Machine Learning (QML) has shown rapid advances by utilizing quantum computing for machine learning tasks. Meanwhile, the privacy risks accomp

CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

arXiv:2609.07779v1 Announce Type: cross Abstract: As AI-code assistant tools become widespread, automatic assessment of the correctness of generated code becomes a significant challenge. Code LLMs are

Collision-Free Velocity Scheduling for Multi-Agent Systems on Predefined Routes via Inexact-Projection ADMM

arXiv:2603.21913v2 Announce Type: replace Abstract: In structured multi-agent transportation systems, agents often must follow predefined routes, making spatial rerouting undesirable or impossible. Th

safetyarxiv-cs-ro

Compressed Recurrent Feedback in Tsetlin Machines: A Reproducible Boolean-FSM Study

arXiv:2609.06133v1 Announce Type: new Abstract: Sequential inference on small devices requires a model to retain useful history without repeatedly processing a long input record. A Recurrent Tsetlin M

Conditional Timed Partial Orders: An Expressive and Interpretable Framework for Robot Task Specification and Planning

arXiv:2609.07905v1 Announce Type: new Abstract: Timed Partial Orders (TPOs), originally proposed for workflows, provide an interpretable framework for robot task specification with planning algorithms

researcharxiv-cs-ro

Conditioned Initialization for Attention

arXiv:2609.07086v1 Announce Type: cross Abstract: Transformers are a dominant architecture in modern machine learning, powering applications across vision, language, and beyond. At the core of their s

Constrained Bayesian Optimization for Hierarchical Federated Learning in IoT Networks for Plant Disease Classification

arXiv:2609.06830v1 Announce Type: new Abstract: The deployment of Hierarchical Federated Learning (HFL) in resource-constrained Internet of Things (IoT) environments requires careful configuration to

Applications

Context-Aware System Synthesis, Task Assignment, and Routing

arXiv:1706.04580v3 Announce Type: replace Abstract: The design and organization of complex robotic systems traditionally requires laborious trial-and-error processes to ensure both hardware and softwa

applicationsarxiv-cs-ro

Coverage Path Planning for Redundant Manipulators using Generalized Spanning Trees

arXiv:2609.08409v1 Announce Type: new Abstract: Surface coverage with task-redundant manipulators is challenging because each surface point may admit multiple inverse kinematics (IK) solutions, and co

researcharxiv-cs-ro

CRISP: Corneal Confocal Microscopy Real-Time Image Stitching Pipeline

arXiv:2609.07336v1 Announce Type: new Abstract: Morphology of the sub-basal nerve plexus (SNP) reflects peripheral nerve health, and corneal confocal microscopy (CCM) provides an important means for i

CrossModalQA: A Cross-modal and Multi-hop Benchmark for Multimodal Retrieval-augmented Generation

arXiv:2609.05518v1 Announce Type: new Abstract: Despite the strong capabilities of multimodal large language models (MLLMs), their parametric knowledge remains incomplete and difficult to update, moti

CVT-GS: Learning to Simplify 3D Gaussian Splatting with Centroidal Voronoi Tessellation

arXiv:2609.08730v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time novel view synthesis, rendering high-fidelity scenes often rel

Agents

D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systems

arXiv:2609.07350v1 Announce Type: cross Abstract: Climate change is increasing the severity and unpredictability of natural disasters. In time-critical crises such as wildfires, traditional monitoring

agentsarxiv-cs-cv

Damage-Aware Bandit Pruning for Vision and Language Transformers

arXiv:2609.05448v1 Announce Type: cross Abstract: Structured post-training pruning of transformers requires selecting complete functional units whose suppression causes limited degradation. We formula

Applications

Data Quality Rule Generation with LLMs

arXiv:2609.06053v1 Announce Type: new Abstract: The validation of data, such as customer and employee data, is an important task in many organizations. Errors in data can have severe consequences. For

applicationsarxiv-cs-lg

DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space

arXiv:2509.25180v3 Announce Type: replace Abstract: Existing text-to-image diffusion models excel at generating high-quality images, but face significant efficiency challenges when scaled to high reso

DDPM Score Matching and Distribution Learning

arXiv:2504.05161v2 Announce Type: replace-cross Abstract: Score estimation is the backbone of score-based generative models (SGMs), especially denoising diffusion probabilistic models (DDPMs). A key r

Deep Learning for Biopsy-Free Subtyping of Basal Cell Carcinoma from Dermatoscopic Images

arXiv:2609.07180v1 Announce Type: new Abstract: Basal Cell Carcinoma (BCC) is the most common type of skin cancer, accounting for nearly 80% of skin cancer di- agnoses. Its optimal clinical management

Depth-to-Image Synthesis-Driven Generative Unguided Depth Completion

arXiv:2609.06007v1 Announce Type: new Abstract: Guided depth completion methods heavily depend on RGB quality and alignment, while unguided ones often suffer from limited precision due to the absence

DISEIL: Demonstration Distillation for Sample-Efficient Imitation Learning

arXiv:2609.08123v1 Announce Type: cross Abstract: A robot that can be taught a new task from a handful of demonstrations has to work out for itself what it still cannot do, and then ask for exactly th

safetyarxiv-cs-lg

Agents

Distributed Dexterous Manipulation with Spatially Conditioned Multi-Agent Transformers

arXiv:2609.06930v1 Announce Type: cross Abstract: Distributed Dexterous Manipulation (DDM) is a novel paradigm that presents significant control challenges due to high action-space redundancy, inter-r

agentsarxiv-cs-lg

Distribution-free inference on the number of changepoints

arXiv:2609.08234v1 Announce Type: cross Abstract: Suppose we are given an ordered sequence of independent data whose distribution changes K times at unknown locations, for some unknown K geq 0. In thi

DroneGround: Open-Vocabulary Drone Payload Characterization Using Synthetic Data and Grounded Vision-Language Models

arXiv:2609.07780v1 Announce Type: new Abstract: Automated drone surveillance has become increasingly important for public safety, critical infrastructure protection,and restricted airspace monitoring.

Local Ai

EcoFair: Energy-Efficient Inference Routing for Edge AI under Data Degradation

arXiv:2603.26483v2 Announce Type: replace Abstract: Medical edge-AI systems must operate under a difficult tension: delivering reliable diagnostic inference while running on devices with limited batte

local-aiarxiv-cs-lg

Local Ai

Efficient and Microphone-Fault-Tolerant 3D Sound Source Localization

arXiv:2505.20961v2 Announce Type: replace-cross Abstract: Sound source localization (SSL) is a critical technology for determining the position of sound sources in complex environments. However, exist

local-aiarxiv-cs-lg

Efficient Learning and Symmetry Discovery under Exact Invariances

arXiv:2609.07031v1 Announce Type: new Abstract: Learning with group invariances is central to many scientific and geometric learning problems, yet its computational foundations remain poorly understoo

Enhancing Table Structure Recognition via Bounding Box Guidance

arXiv:2609.08705v1 Announce Type: new Abstract: Table Structure Recognition (TSR) aims to extract the bounding boxes of cells and table structure (e.g., HTML) from table images. Although current appro

Evaluating Vision-Language Models for Image Quality Assessment using Psychophysical Data

arXiv:2603.24578v2 Announce Type: replace Abstract: Psychophysical experiments remain the most reliable approach for perceptual image quality assessment (IQA), yet their cost and limited scalability m

Event-Based De-Snowing for Autonomous Driving

arXiv:2507.20901v2 Announce Type: replace Abstract: Adverse weather conditions, particularly heavy snowfall, pose significant challenges to both human drivers and autonomous vehicles. Traditional imag