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MoE-ViE: Mixture of Experts Vision Encoder for Efficient Image and Video Understanding

arXiv:2608.17402v1 Announce Type: new Abstract: Vision encoders are a critical component of vision-language models, and scaling their capacity effectively improves performance. However, dense scaling

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Monotone Classification with Relative Approximations

DGX agent

arXiv:2506.10775v3 Announce Type: replace Abstract: In monotone classification, the input is a multi-set P of points in R^d, each associated with a hidden label from {-1, 1}. The goal is to identify a

researcharxiv-cs-lg

19 Aug 2026

MoRAX: Mobility-based Representation Augmentation for Geospatial Foundation Models

arXiv:2608.17848v1 Announce Type: new Abstract: Geospatial Foundation Models (GFMs) are emerging as a powerful paradigm for learning semantically rich and geographically consistent visual and physical

N-gram-like Language Models Predict Naturalistic Reading Time Best

arXiv:2603.09872v2 Announce Type: replace Abstract: Recent work has found that contemporary language models such as transformers can become so good at next-word prediction that the probabilities they

Neuro-symbolic learning over OWL 2 DL via consequence-based compilation to differentiable circuits

arXiv:2608.17741v1 Announce Type: new Abstract: OWL 2 DL ontologies, grounded in the description logic SROIQ, express large knowledge bases in biomedicine and the Semantic Web. Neuro-symbolic (NeSy) l

NeuroPath: Brain-Inspired Dual-Pathway Graph Convolutional Networks for Skeleton-Based Action Recognition

arXiv:2608.17487v1 Announce Type: new Abstract: Skeleton-based action recognition aims to recognize human actions from sequences of human joint coordinates. Most existing Spatial-Temporal Graph Convol

Noisy group neurons with synchronous resetting for high-performance spiking neural networks

arXiv:2608.17394v1 Announce Type: new Abstract: Spiking neural networks (SNNs), characterized by bio-inspired neuronal dynamics and event-driven communication, have attained significant progress in re

Nonlocal Transition Kernel for Efficient Learning of Restricted Boltzmann Machines

arXiv:2608.17450v1 Announce Type: cross Abstract: Learning restricted Boltzmann machines (RBMs) is computationally challenging because it requires expectations whose exact evaluation is generally intr

On detection probabilities of link invariants

arXiv:2509.05574v3 Announce Type: replace-cross Abstract: We prove that, for many standard link invariants, both the proportion of distinct invariant values and the detection probability among prime a

On Stability in Optimistic Bilevel Optimization

arXiv:2408.13323v3 Announce Type: replace-cross Abstract: Solutions of bilevel optimization problems tend to suffer from instability under changes to problem data. In the optimistic setting, we constr

On the Pseudo-Mixing of Kac's Walk

arXiv:2608.17374v1 Announce Type: cross Abstract: Motivated by a conjecture of Vaikuntanathan and Zamir, we study the pseudo-mixing of Kac's walk on SO(n): whether short trajectories are indistinguish

Optimal control of a swimming robot based on Purcell's microswimmer model

arXiv:2608.17455v1 Announce Type: cross Abstract: Purcell's swimmer is a well-known planar model of a swimming microorganism, governed by low Reynolds number hydrodynamics, which is comprised of three

Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization

arXiv:2608.18040v1 Announce Type: cross Abstract: Sampling from a diffusion model typically requires many forward passes through a large neural network, making generation computationally expensive. Wh

Optimizing Container Loading and Unloading through Dual-Cycling and Dockyard Rehandle Reduction Using a Hybrid Genetic Algorithm

arXiv:2406.08534v4 Announce Type: replace-cross Abstract: This paper addresses the NP-hard problem of optimizing container handling at ports by integrating Quay Crane Dual-Cycling (QCDC) and dockyard

Parametric Knowledge in RAG-SFT for Domain-Specific Document Generation

arXiv:2603.23047v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document questio

PerFact: Perception-Derived Fact Prompting for 3D Brain MRI Report Generation

arXiv:2608.17926v1 Announce Type: new Abstract: Radiology report generation has matured almost entirely on 2D chest radiographs, where the default route to better reports is a larger backbone or a pre

Pessimistic Meta-Induction and Its Limits: Lessons from Frequentist Statistics and Machine Learning Theory

arXiv:2608.17213v1 Announce Type: new Abstract: This paper challenges the pessimistic meta-inductive argument against scientific realism by undermining its inductive step rather than its historical pr

Polaris: Learning to Generate Table Descriptions from Retrieval Feedback

arXiv:2608.17171v1 Announce Type: new Abstract: Many table-centric NLP tasks such as NL2SQL first retrieve relevant tables from large collections using keyword search. Recent work uses LLMs to generat

Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease

arXiv:2608.17174v1 Announce Type: new Abstract: Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes

Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis

arXiv:2311.06273v1 Announce Type: cross Abstract: The rise of ChatGPT has brought a notable shift to the AI sector, with its exceptional conversational skills and deep grasp of language. Recognizing i

Preference Is Not Intervention: The Structure and Stability Boundaries of Reader-Specific Evidence Utility

arXiv:2608.17781v1 Announce Type: new Abstract: ML systems increasingly condition decisions on downstream model identity, but this is useful only if model-specific differences form reusable structure

Primitive-Driven Compositional Forensic Visual Prompting for Open-World Face Anti-Spoofing

arXiv:2608.17351v1 Announce Type: new Abstract: Open-world face anti-spoofing must address both covariate and semantic shifts: source and target domains differ in imaging conditions, while target doma

Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction

arXiv:2608.18055v1 Announce Type: cross Abstract: Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan

Probing Association Instability with Track-State Perturbations for Clip-Level Active Learning in Query-Propagation Multi-Object Tracking

arXiv:2608.17224v1 Announce Type: new Abstract: Training query-propagation end-to-end multi-object tracking (MOT) models requires dense bounding-box and identity annotations across video sequences, ma

Procedural Content Metageneration via Program Search and Continual Abstraction Discovery

arXiv:2608.17947v1 Announce Type: new Abstract: Large language models can generate executable programs, which makes it possible to search directly over procedural content generators rather than indivi

Quantifying Memorization and Privacy Risks in Genomic Language Models

arXiv:2603.08913v2 Announce Type: replace Abstract: Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction

RADmesh: Remesh-Aware Mesh Deformation

arXiv:2608.17182v1 Announce Type: cross Abstract: We propose a remeshing-enhanced method for generatively deforming shapes with visual losses. It is intuitive that sufficiently drastic deformations of

Recirculation

arXiv:2608.17981v1 Announce Type: new Abstract: We describe an inference-time architectural enhancement for off-the-shelf foundation models that markedly reduces perplexity and boosts accuracy across

Reconfiguration-Complete Motion Primitives with Constructive Planning for Deformable Planar Modular Robots

arXiv:2608.17324v1 Announce Type: new Abstract: The continuously deformable geometry of modular robots makes it difficult to define a fixed representation for reconfiguration planning and analysis. Th

RetiWave-Mamba: A Dual-Stream Network for Retinal Disease Detection based on Multi-scale Context and Frequency-Adaptive Mamba Projection

arXiv:2608.17623v1 Announce Type: new Abstract: Retinal diseases are a leading cause of irreversible vision impairment, making early and accurate diagnosis essential for effective treatment. Optical C

Revisiting WEASEL 2.0: Reproduction, Sensitivity, and an Adaptive Ensemble-Size Rule

arXiv:2608.18021v1 Announce Type: new Abstract: WEASEL 2.0 is a dictionary-based time series classifier that combines dilated sliding windows with a randomised hyperparameter ensemble and a fixed-size

Robust Brachiation on a Life-Sized Dual-Arm Robot Using Waypoint-Guided Reinforcement Learning

arXiv:2608.17320v1 Announce Type: new Abstract: Brachiation is a form of locomotion in which primates move primarily using their arms, enabling traversal in environments without footholds. However, th

Scale Matters: Adaptive Granularity Selection for Cross-Species 3D Plant Organ Segmentation

arXiv:2608.17803v1 Announce Type: new Abstract: Recent 3D foundation models provide powerful feature representations for point cloud learning by controlling spatial granularity. However, relying on a

Scalix: Uncertainty-Aware Scale-Consistent Monocular SLAM

arXiv:2608.17553v1 Announce Type: new Abstract: Cameras are ubiquitous sensors in robotics due to their compact form factor and the perceptual richness captured through visual information. Monocular S

SCRIBES: Web-Scale Script-Based Semi-Structured Data Extraction with Reinforcement Learning

arXiv:2510.01832v2 Announce Type: replace Abstract: Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complic

Seam Carving as Feature Pooling in CNN

arXiv:2409.06311v2 Announce Type: replace Abstract: This work investigates the potential of seam carving as a feature pooling technique within Convolutional Neural Networks (CNNs) for image classifica

SemComp-Bench: Benchmarking Semantic Task Completion in Video Generation

arXiv:2608.17426v1 Announce Type: cross Abstract: We introduce Semantic Task Completion Video Generation, an outcome-oriented video generation task. Under this formulation, success requires both achie

SPACE: Sample-cloud Predictive Adaptive Conformal Ellipsoids for Multivariate Time-Series Forecasting

arXiv:2608.17333v1 Announce Type: cross Abstract: Modern probabilistic time-series forecasters often express uncertainty through forecast samples. While typically converted into nominal prediction reg

Sparse Coverage: Semantic Center Representations for Patent Prior-Art Retrieval

arXiv:2608.16918v1 Announce Type: cross Abstract: Patent prior-art retrieval is a recall-oriented search task over long and highly structured technical documents. Dense retrieval improves semantic mat

Spatially explicit feature importance for building height estimation using research-access high-resolution SAR and optical sensors

arXiv:2608.17822v1 Announce Type: cross Abstract: Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments

Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket

arXiv:2401.02020v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the challenge of limited performance. The self-atten

SpurCon: Weighted Supervised Contrastive Learning for Mitigating Spurious Cues in Medical Imaging

arXiv:2608.17598v1 Announce Type: new Abstract: Despite the rapid progress of deep neural networks in visual recognition, their adoption in high-risk medical applications remains limited due to reliab

Structure-Internalized Rule Language Model for Faithful Knowledge Graph Reasoning

arXiv:2608.17443v1 Announce Type: new Abstract: Knowledge Graph Reasoning (KGR) aims to discover latent facts by leveraging the structural evidence available in KGs, posing a challenge to the structur

Study-Strategy Clusters from EdNet Logs Track Engagement, Not Mastery

arXiv:2608.16963v1 Announce Type: new Abstract: Learning analytics often treats unsupervised clusters of intelligent tutoring system (ITS) logs as learner types that should predict learning. We test t

TEAMS: Text-prompted spatiotEmporal dual-heAd Mamba Snake

arXiv:2608.17421v1 Announce Type: new Abstract: Deep snake is a promising family of instance segmentation methods that accurately predicts object-level contours, thereby overcoming common pixel-level

The Authenticity Gap in Human Evaluation

arXiv:2205.11930v3 Announce Type: replace-cross Abstract: Human ratings are the gold standard in NLG evaluation. The standard protocol is to collect ratings of generated text, average across annotator

The concentration game: Bayesian updating, regret, and information

arXiv:2608.18061v1 Announce Type: new Abstract: We give a two-player zero-sum repeated game between a learner and nature whose value identity generates Bayesian updating and an exact accounting of exp

The Model's Tell: Measuring Context-Leakage Attack Signals with Behavior Gauges

arXiv:2608.17829v1 Announce Type: cross Abstract: LLMs increasingly rely on external contexts, such as pre-defined system prompts or retrieved documents, to improve generation quality. However, proces