Search: “tutorials”

SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding

arXiv:2609.01353v1 Announce Type: new Abstract: Protein inverse folding aims to recover amino acid sequences for a given 3D protein structure, underpinning broad applications such as enzyme engineerin

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Tutorials

Taming Modality Entanglement in Continual Audio-Visual Segmentation

DGX agent

arXiv:2510.17234v3 Announce Type: replace-cross Abstract: Recently, significant progress has been made in multi-modal continual learning, aiming to learn new tasks sequentially in multi-modal settings

tutorialsarxiv-cs-ai

2 Sept 2026

The Structure of Quantization Damage in LLMs: Why the Next Bit Should Be Spent Globally

arXiv:2609.01587v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is widely used to reduce the cost of serving large language models (LLMs), but its accuracy cost is uneven and is oft

tutorialsarxiv-cs-cl

Training-Free Refinement of Flow Matching with Divergence-based Sampling

arXiv:2604.04646v2 Announce Type: replace-cross Abstract: Flow-based models learn a target distribution by modeling a marginal velocity field, defined as the average of sample-wise velocities connecti

Unmasking Face Embeddings: Reading, Rendering and Naming with Foundation Models

arXiv:2609.00411v1 Announce Type: new Abstract: Modern face recognition (FR) owes much of its success to deep neural networks that learn to extract compact identity embeddings from face images. These

(V)LMs generalize beyond surface co-occurrence: Evidence from cross-modal number agreement

arXiv:2609.00443v1 Announce Type: cross Abstract: Language models learn about grammatical number primarily from co-occurrence, and show frequency effects as a result---sometimes taken to indicate that

What, Where, and How: Probing Spatiotemporal Representations in Video Foundation Models

arXiv:2609.01551v1 Announce Type: new Abstract: Self-supervised video foundation models learn rich spatiotemporal representations, yet it remains unclear what visual concepts these representations enc

Acoustically Grounded Cost Learning for Open-Vocabulary Audio-Visual Semantic Segmentation

arXiv:2608.29121v1 Announce Type: new Abstract: Open-Vocabulary Audio-Visual Semantic Segmentation (OV-AVSS) aims to perform pixel-level segmentation of sound-emitting objects from an open set of cate

ARMOR: Manifold-Oriented Training for Adversarially Robust Aerial Object Detection under Data Scarcity

arXiv:2608.29510v1 Announce Type: new Abstract: Aerial object detection is increasingly deployed in real-world applications, but models remain vulnerable to physical, universal adversarial patches tha

AutoScientist-Quant: Self-Evolving Coding Agents for Automatic Research in Quantitative Investment

arXiv:2608.28632v1 Announce Type: new Abstract: Large language model agents can discover alphas, yet current methods have three weaknesses. The search cannot adapt during the run, automation usually e

CGFM-Nav: Cognitive Graph-Field Memory for Semantic-Guided Lifelong Multimodal Embodied Navigation

arXiv:2608.29114v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) requires agents to reason over accumulated observations while continuously exploring unseen regions. However, exi

CoReflect: A Reflective Co-Evolution Framework for Improving Conversational Evaluation

arXiv:2601.12208v2 Announce Type: replace Abstract: Evaluating conversational systems in multi-turn settings remains a fundamental challenge. Conventional pipelines typically rely on manually defined

tutorialsarxiv-cs-cl

Denoising as Projection: Constrained Optimization with Gradient-Guided Diffusion

arXiv:2608.29507v1 Announce Type: cross Abstract: Diffusion models are increasingly used not only for sampling from learned data distributions, but also for generating samples that optimize task-speci

Di^2CycleSB: Towards High-Quality Unsupervised Nighttime Visibility Enhancement via Schrodinger Bridge Transformer

arXiv:2608.29043v1 Announce Type: new Abstract: Light-effect contamination poses a significant challenge to nighttime visibility enhancement. Most methods suppress light effects by estimating and deco

DiffSAC: Diffusion-guided Sampling for Consensus-based Robust Estimation

arXiv:2608.30603v1 Announce Type: cross Abstract: Robust estimation is a core computer vision task frequently tackled using sample consensus. However, traditional methods suffer from inefficient sampl

Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learning

arXiv:2506.17007v3 Announce Type: replace Abstract: A major bottleneck in scientific discovery consists of narrowing an exponentially large set of objects, such as proteins or molecules, to a small se

Do Language Models Reason Across Languages?

arXiv:2601.06644v2 Announce Type: replace-cross Abstract: The real-world information sources are inherently multilingual, which naturally raises a question about whether language models can synthesize

EquiReg: Equivariance Regularized Diffusion for Inverse Problems

arXiv:2505.22973v3 Announce Type: replace-cross Abstract: Diffusion models represent the state-of-the-art for solving inverse problems such as image restoration tasks. Diffusion-based inverse solvers

Everybody Tracking Every Body

arXiv:2608.29927v1 Announce Type: new Abstract: We address the problem of 3D body pose estimation of multiple interacting people from their egocentric views with centralized coordination. Each individ

Expected flow networks in stochastic environments and two-player zero-sum games

arXiv:2310.02779v3 Announce Type: replace Abstract: Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully applied

FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising

arXiv:2508.17299v2 Announce Type: replace Abstract: Low-dose computed tomography (CT) denoising is crucial for reduced radiation exposure while ensuring diagnostically acceptable image quality. Despit

From the Loss Landscape to Diverse Feature Learning in Neural Networks

arXiv:2608.28948v1 Announce Type: cross Abstract: Over the course of the last decade, neural networks have grown from an academic curiosity to moving the markets of nations. Despite this explosion in

Generative multi-domain transfer learning for fault detection in data-scarce wind turbines

arXiv:2608.30323v1 Announce Type: new Abstract: Normal behavior models have shown promise for reliable fault detection in wind turbines. However, these unsupervised anomaly detection models require su

Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster

arXiv:2608.30606v1 Announce Type: cross Abstract: With the development of large language models (LLMs), generative retrieval is becoming increasingly important in e-commerce scenarios. Current mainstr

Group Resonance Network: Learnable Prototypes and Multi-Subject Resonance for EEG Emotion Recognition

arXiv:2603.11119v2 Announce Type: replace Abstract: Electroencephalography (EEG)-based emotion recognition remains challenging in cross-subject settings due to severe inter-subject variability. Existi

GSPotential: Camera Potential Field for Sparse-View 3D Gaussian Splatting

arXiv:2608.29346v1 Announce Type: new Abstract: 3D Gaussian Splatting has achieved remarkable success in photorealistic rendering, yet it suffers from severe overfitting and geometric artifacts in spa

Instruction Distillation: Text Instructions as Visual Examples

arXiv:2608.28696v1 Announce Type: new Abstract: Visual in-context learning (ICL) with multimodal large language models (MLLMs) is effective for fine-grained visual classification, but each retrieved i

Learning Personalized Prompts for Healthcare Guidance

arXiv:2412.15957v2 Announce Type: replace-cross Abstract: The rapid development of large language models (LLMs) has transformed many industries, including healthcare. In practice, hospitals and patien

MASQ: Mask-Aware Spatiotemporal Quantization for Unsupervised Skeleton Action Segmentation

arXiv:2608.29891v1 Announce Type: new Abstract: Unsupervised skeleton-based temporal action segmentation is a crucial task for understanding human behavior in long untrimmed sequences. Recent approach

MEOM: Multi-View Expected-OKS Maximization for Human Pose Triangulation

arXiv:2608.30521v1 Announce Type: new Abstract: Conventional algebraic triangulation solves 3D human pose estimation (HPE) from multi-view 2D keypoints. The typical approach, decoding 2D keypoints fro

Mitigating Over-Optimization in PRM-Guided Search in Mathematical Reasoning by Optimizing the Guide

arXiv:2608.30051v1 Announce Type: new Abstract: Process reward models (PRMs) provide dense step-level guidance for search-based reasoning, enabling inference-time compute to be allocated toward promis

MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priors

arXiv:2406.00434v4 Announce Type: replace Abstract: In this paper, we propose MoDGS, a new pipeline to render novel views of dy namic scenes from a casually captured monocular video. Previous monocula

One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread

arXiv:2608.30311v1 Announce Type: cross Abstract: Social media platforms increasingly use AI-based credibility indicators to help users judge misinformation. Unlike individual human-AI decision-making

Phantom-Insight: Adaptive Multi-cue Fusion for Video Camouflaged Object Detection with Multimodal LLM

arXiv:2509.06422v2 Announce Type: replace Abstract: Video camouflaged object detection (VCOD) is challenging due to dynamic environments. Existing methods face two main issues: (1) SAM-based methods s

Predicting the Unpredictable: LLM-powered Long-term Chaotic Time Series Forecasting under Short-term Observations

arXiv:2608.29579v1 Announce Type: new Abstract: Chaotic time series forecasting is a challenging task due to its sensitivity to initial conditions and long-term unpredictability. Traditional methods t

Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V

arXiv:2608.30844v1 Announce Type: cross Abstract: Interactive lesion segmentation in whole-body PET/CT requires a model to provide a strong initial prediction while also responding efficiently to spar

SePArate: Segmenting Patterns from Defects in Wafer Manufacturing Using Weak Supervision

arXiv:2608.30410v1 Announce Type: cross Abstract: In semiconductor manufacturing, defect analysis is essential, but manual inspection cannot scale. However, existing automated inspection methods remai

Signed random Fourier features for fast density estimation with indefinite kernels

arXiv:2608.29265v1 Announce Type: cross Abstract: Kernel density estimation (KDE) is one of the most fundamental statistical estimators of density functions. Its direct implementation on a dataset of

SMG: Semantic Motion Graph for Monocular Dynamic Gaussian Splatting

arXiv:2608.31023v1 Announce Type: new Abstract: We study dynamic Gaussian Splatting from monocular videos. While recent advancements in dynamic Gaussian splatting offer a promising foundation for mode

SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering

arXiv:2508.03448v4 Announce Type: replace-cross Abstract: Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowe

StructSynth: Dependency Graphs as Generation Plans for Low-Data Tabular Synthesis with Language Models

arXiv:2508.02601v2 Announce Type: replace-cross Abstract: Tabular data derives its value from inter-feature dependencies, yet preserving them during synthesis is fragile when samples are scarce. Exist

Structural Hierarchy and Geometry in Molecular Representation Learning

arXiv:2608.29886v1 Announce Type: new Abstract: Molecular self-supervised learning uses chemical structures to guide which molecular embeddings should be similar. We study whether explicitly encoding

Toward Scalable Audio Description Quality Control: A Workflow for Evaluating Human and VLM Raters

arXiv:2602.01390v3 Announce Type: replace-cross Abstract: Digital video is central to communication, education, and entertainment, but without audio description (AD), blind and low-vision users are ex

Transformer-Based Flow Shop Scheduling Using MILP-Generated Training Data

arXiv:2608.29690v1 Announce Type: cross Abstract: Advances in machine learning (ML) have created new opportunities to complement traditional operations research (OR) methods. In particular, transforme

Euclidean Fourier Neural Operators

arXiv:2608.28425v1 Announce Type: new Abstract: Fourier neural operators (FNOs) provide an efficient framework for learning mappings between function spaces as they are, by construction, independent o

Generalized Context in Cross Attention for Transfer Learning of Disjoint Tabular Data

arXiv:2608.28209v1 Announce Type: new Abstract: Unlike images and text, applying transfer learning to tabular data is challenging due to heterogeneity in feature types, structures, and semantics acros

Object Reconstruction under Occlusion with Generative Priors and Contact-induced Constraints

arXiv:2512.05079v2 Announce Type: replace Abstract: Object geometry is key information for robot manipulation. Yet, object reconstruction is a challenging task because camera observations are partial

Rethinking Speaker Embeddings for Speech Generation: Sub-Center Modeling for Capturing Intra-Speaker Diversity

arXiv:2407.04291v4 Announce Type: replace-cross Abstract: Modeling speech variation is key to natural, expressive generation. Speaker embeddings are commonly used to condition personalized speech syst