Search: “research”

Confidence-Based Mesh Extraction from 3D Gaussians

arXiv:2603.24725v2 Announce Type: replace Abstract: Recently, 3D Gaussian Splatting (3DGS) greatly accelerated mesh extraction from posed images due to its explicit representation and fast software ra

Context Attribution with Multi-Armed Bandit Optimization

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DGX agent

arXiv:2506.19977v2 Announce Type: replace Abstract: Understanding which parts of the retrieved context contribute to a large language model's generated answer is essential for building interpretable a

researcharxiv-cs-ai

23 Apr 2026

Control Consistency Losses for Diffusion Bridges

arXiv:2512.05070v2 Announce Type: replace-cross Abstract: Simulating the conditioned dynamics of diffusion processes, given their initial and terminal states, is an important but challenging problem i

Cover meets Robbins while Betting on Bounded Data: ln n Regret and Almost Sure lnln n Regret

arXiv:2604.20172v1 Announce Type: new Abstract: Consider betting against a sequence of data in [0,1], where one is allowed to make any bet that is fair if the data have a conditional mean m_0 in (0,1)

CrackForward: Context-Aware Severity Stage Crack Synthesis for Data Augmentation

arXiv:2604.19941v1 Announce Type: new Abstract: Reliable crack detection and segmentation are vital for structural health monitoring, yet the scarcity of well-annotated data constitutes a major challe

CXR-LanIC: Language-Grounded Interpretable Classifier for Chest X-Ray Diagnosis

arXiv:2510.21464v2 Announce Type: replace Abstract: Deep learning models have achieved remarkable accuracy in chest X-ray diagnosis, yet their widespread clinical adoption remains limited by the black

Deconstructing Superintelligence: Identity, Self-Modification and Differance

arXiv:2604.19845v1 Announce Type: new Abstract: Self-modification is often taken as constitutive of artificial superintelligence (SI), yet modification is a relative action requiring a supplement outs

Degrees, Levels, and Profiles of Contextuality

arXiv:2603.26692v3 Announce Type: replace-cross Abstract: We introduce a new notion, that of a contextuality profile of a system of random variables. Rather than characterizing a system's contextualit

Depression Risk Assessment in Social Media via Large Language Models

arXiv:2604.19887v1 Announce Type: cross Abstract: Depression is one of the most prevalent and debilitating mental health conditions worldwide, frequently underdiagnosed and undertreated. The prolifera

Diagnosing Urban Street Vitality via a Visual-Semantic and Spatiotemporal Framework for Street-Level Economics

arXiv:2604.19798v1 Announce Type: cross Abstract: Micro-scale street-level economic assessment is fundamental for precision spatial resource allocation. While Street View Imagery (SVI) advances urban

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation

arXiv:2604.20596v1 Announce Type: new Abstract: Federated learning (FL) enables training of a global model while keeping raw data on end-devices. Despite this, FL has shown to leak private user inform

Distributional Value Estimation Without Target Networks for Robust Quality-Diversity

arXiv:2604.20381v1 Announce Type: new Abstract: Quality-Diversity (QD) algorithms excel at discovering diverse repertoires of skills, but are hindered by poor sample efficiency and often require tens

Effects of Cross-lingual Evidence in Multilingual Medical Question Answering

arXiv:2604.20531v1 Announce Type: new Abstract: This paper investigates Multilingual Medical Question Answering across high-resource (English, Spanish, French, Italian) and low-resource (Basque, Kazak

Efficient Multi-Cohort Inference for Long-Term Effects and Lifetime Value in A/B Testing with User Learning

arXiv:2604.20777v1 Announce Type: new Abstract: In streaming platforms churn is extremely costly, yet A/B tests are typically evaluated using outcomes observed within a limited experimental horizon. E

Efficient Test-Time Inference via Deterministic Exploration of Truncated Decoding Trees

arXiv:2604.20500v1 Announce Type: new Abstract: Self-consistency boosts inference-time performance by sampling multiple reasoning traces in parallel and voting. However, in constrained domains like ma

Emergence Transformer: Dynamical Temporal Attention Matters

arXiv:2604.19816v1 Announce Type: new Abstract: The Transformer, a breakthrough architecture in artificial intelligence, owes its success to the attention mechanism, which utilizes long-range interact

Enhancing Speaker Verification with Whispered Speech via Post-Processing

arXiv:2604.20229v1 Announce Type: cross Abstract: Speaker verification is a task of confirming an individual's identity through the analysis of their voice. Whispered speech differs from phonated spee

ESGLens: An LLM-Based RAG Framework for Interactive ESG Report Analysis and Score Prediction

arXiv:2604.19779v1 Announce Type: new Abstract: Environmental, Social, and Governance (ESG) reports are central to investment decision-making, yet their length, heterogeneous content, and lack of stan

Eventually LIL Regret: Almost Sure lnln T Regret for a sub-Gaussian Mixture on Unbounded Data

arXiv:2512.12325v3 Announce Type: replace Abstract: We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound

Explicit Dropout: Deterministic Regularization for Transformer Architectures

arXiv:2604.20505v1 Announce Type: new Abstract: Dropout is a widely used regularization technique in deep learning, but its effects are typically realized through stochastic masking rather than explic

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields

arXiv:2604.19979v1 Announce Type: cross Abstract: Neural fields, also known as implicit neural representations (INRs), offer a powerful framework for modeling continuous geometry, but their effectiven

FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels

arXiv:2604.20825v1 Announce Type: cross Abstract: Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed client

Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder

arXiv:2604.20483v1 Announce Type: cross Abstract: In this paper, we propose a proof-of-concept Graph Neural Network model that can successfully predict network flow-level traffic (NetFlow) by accurate

Formal Verification of Minimax Algorithms

arXiv:2509.20138v2 Announce Type: replace Abstract: Minimax-based search algorithms with alpha-beta pruning and transposition tables are a central component of classical game-playing engines and remai

Formalising the Logit Shift Induced by LoRA: A Technical Note

arXiv:2604.20313v1 Announce Type: cross Abstract: This technical note provides a first-order formalisation of the logit shift and fact-margin change induced by Low-Rank Adaptation (LoRA). Using a firs

Fourier Series Coder: A Novel Perspective on Angle Boundary Discontinuity Problem for Oriented Object Detection

arXiv:2604.20281v1 Announce Type: new Abstract: With the rapid advancement of intelligent driving and remote sensing, oriented object detection has gained widespread attention. However, achieving high

Fourier Weak SINDy: Spectral Test Function Selection for Robust Model Identification

arXiv:2604.20141v1 Announce Type: new Abstract: We introduce Fourier Weak SINDy, a minimal noise-robust and interpretable derivative-free equation learning method that combines weak-form sparse equati

From Competition to Synergy: Unlocking Reinforcement Learning for Subject-Driven Image Generation

arXiv:2510.18263v2 Announce Type: replace-cross Abstract: Subject-driven image generation models face a fundamental trade-off between identity preservation (fidelity) and prompt adherence (editability

From Diffusion to Flow: Efficient Motion Generation in MotionGPT3

arXiv:2603.26747v2 Announce Type: replace Abstract: Recent text-driven motion generation methods span both discrete token-based approaches and continuous-latent formulations. MotionGPT3 exemplifies th

From Image to Music Language: A Two-Stage Structure Decoding Approach for Complex Polyphonic OMR

arXiv:2604.20522v1 Announce Type: cross Abstract: We propose a new approach for the second stage of a practical two-stage Optical Music Recognition (OMR) pipeline. Given symbol and event candidates fr

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization

arXiv:2604.19884v1 Announce Type: cross Abstract: Post-Training Quantization (PTQ) is critical for the efficient deployment of Large Language Models (LLMs). While 4-bit quantization is widely regarded

Gauge-covariant stochastic neural fields: Stability and finite-width effects

arXiv:2508.18948v2 Announce Type: replace-cross Abstract: We develop a gauge-covariant stochastic effective field theory for stability and finite-width effects in deep neural systems. The model uses c

Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories

arXiv:2604.20797v1 Announce Type: cross Abstract: Local gauge symmetry underlies fundamental interactions and strongly correlated quantum matter, yet existing machine-learning approaches lack a genera

Generalization and Membership Inference Attack a Practical Perspective

arXiv:2604.19936v1 Announce Type: cross Abstract: With the emergence of new evaluation metrics and attack methodologies for Membership Inference Attacks (MIA), it becomes essential to reevaluate previ

Generative Prior-Guided Neural Interface Reconstruction for 3D Electrical Impedance Tomography

arXiv:2505.16487v3 Announce Type: replace-cross Abstract: Reconstructing complex 3D interfaces from indirect measurements remains a grand challenge in scientific computing, particularly for ill-posed

Geometric Layer-wise Approximation Rates for Deep Networks

arXiv:2604.20219v1 Announce Type: new Abstract: Depth is widely viewed as a central contributor to the success of deep neural networks, whereas standard neural network approximation theory typically p

GeoRect4D: Geometry-Compatible Generative Rectification for Dynamic Sparse-View 3D Reconstruction

arXiv:2604.20784v1 Announce Type: new Abstract: Reconstructing dynamic 3D scenes from sparse multi-view videos is highly ill-posed, often leading to geometric collapse, trajectory drift, and floating

GeoRelight: Learning Joint Geometrical Relighting and Reconstruction with Flexible Multi-Modal Diffusion Transformers

arXiv:2604.20715v1 Announce Type: new Abstract: Relighting a person from a single photo is an attractive but ill-posed task, as a 2D image ambiguously entangles 3D geometry, intrinsic appearance, and

GSCompleter: A Distillation-Free Plugin for Metric-Aware 3D Gaussian Splatting Completion in Seconds

arXiv:2604.20155v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has revolutionized real-time rendering, its performance degrades significantly under sparse-view extrapolation, manif

Hallucination Early Detection in Diffusion Models

arXiv:2604.20354v1 Announce Type: new Abstract: Text-to-Image generation has seen significant advancements in output realism with the advent of diffusion models. However, diffusion models encounter di

High-Level Multi-Robot Trajectory Planning And Spurious Behavior Detection

arXiv:2510.17261v2 Announce Type: replace-cross Abstract: The reliable execution of high-level missions in multi-robot systems with heterogeneous agents, requires robust methods for detecting spurious

How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestamps

arXiv:2509.14536v3 Announce Type: replace Abstract: Predictive process monitoring supports operational decision-making by forecasting future states of ongoing business cases. A key task is case suffix

HumanScore: Benchmarking Human Motions in Generated Videos

arXiv:2604.20157v1 Announce Type: new Abstract: Recent advances in model architectures, compute, and data scale have driven rapid progress in video generation, producing increasingly realistic content

HumorRank: A Tournament-Based Leaderboard for Evaluating Humor Generation in Large Language Models

arXiv:2604.19786v1 Announce Type: new Abstract: Evaluating humor in large language models (LLMs) is an open challenge because existing approaches yield isolated, incomparable metrics rather than unifi

Improving clinical interpretability of linear neuroimaging models through feature whitening

arXiv:2604.20675v1 Announce Type: new Abstract: Linear models are widely used in computational neuroimaging to identify biomarkers associated with brain pathologies. However, interpreting the learned

Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximation

arXiv:2508.18168v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has become a widely recognized paradigm to combine parametric memory with non-parametric memories. An RAG model

Improving Facial Emotion Recognition through Dataset Merging and Balanced Training Strategies

arXiv:2604.20307v1 Announce Type: new Abstract: In this paper, a deep learning framework is proposed for automatic facial emotion based on deep convolutional networks. In order to increase the general

Improving Molecular Force Fields with Minimal Temporal Information

arXiv:2604.19806v1 Announce Type: cross Abstract: Accurate prediction of energy and forces for 3D molecular systems is one of fundamental challenges at the core of AI for Science applications. Many po