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Multi-Model Synthetic Training for Mission-Critical Small Language Models

arXiv:2509.13047v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across many domains, yet their application to specialized fields remain

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Multi-ORFT: Stable Online Reinforcement Fine-Tuning for Multi-Agent Diffusion Planning in Cooperative Driving

DGX agent

arXiv:2604.11734v1 Announce Type: cross Abstract: Closed-loop cooperative driving requires planners that generate realistic multimodal multi-agent trajectories while improving safety and traffic effic

model-releasesarxiv-cs-ai

14 Apr 2026

Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting

arXiv:2510.02050v3 Announce Type: replace-cross Abstract: Improving statistical forecasts of tropical cyclone (TC) intensity is limited by complex nonlinear interactions and difficulty in identifying

researcharxiv-cs-lg

Multimodal Dataset Normalization and Perceptual Validation for Music-Taste Correspondences

arXiv:2604.10632v1 Announce Type: cross Abstract: Collecting large, aligned cross-modal datasets for music-flavor research is difficult because perceptual experiments are costly and small by design. W

safetyarxiv-cs-lg

Multimodal Diffusion Forcing for Forceful Manipulation

arXiv:2511.04812v2 Announce Type: replace-cross Abstract: Given a dataset of expert trajectories, standard imitation learning approaches typically learn a direct mapping from observations (e.g., RGB i

tutorialsarxiv-cs-ai

Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex

arXiv:2604.10359v1 Announce Type: cross Abstract: Low-light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation. St

Muon^2: Boosting Muon via Adaptive Second-Moment Preconditioning

arXiv:2604.09967v1 Announce Type: cross Abstract: Muon has emerged as a promising optimizer for large-scale foundation model pre-training by exploiting the matrix structure of neural network updates t

Local Ai

MuPPet: Multi-person 2D-to-3D Pose Lifting

arXiv:2604.09715v1 Announce Type: new Abstract: Multi-person social interactions are inherently built on coherence and relationships among all individuals within the group, making multi-person localiz

local-aiarxiv-cs-cv

MVAD: A Benchmark Dataset for Multimodal AI-Generated Video-Audio Detection

arXiv:2512.00336v2 Announce Type: replace Abstract: The rapid advancement of AI-generated multimodal video-audio content has raised significant concerns regarding information security and content auth

Mycelium-Index: A Streaming Approximate Nearest Neighbor Index with Myelial Edge Decay, Traffic-Driven Reinforcement, and Adaptive Living Hierarchy

model-releasesarxiv-cs-lg

arXiv:2604.11274v1 Announce Type: new Abstract: We present mycelium-index, a streaming approximate nearest neighbor (ANN) index for high-dimensional vector spaces, inspired by the adaptive growth patt

Naka-GS: A Bionics-inspired Dual-Branch Naka Correction and Progressive Point Pruning for Low-Light 3DGS

arXiv:2604.11142v1 Announce Type: new Abstract: Low-light conditions severely hinder 3D restoration and reconstruction by degrading image visibility, introducing color distortions, and contaminating g

safetyarxiv-cs-cv

NameBERT: Scaling Name-Based Nationality Classification with LLM-Augmented Open Academic Data

arXiv:2604.10401v1 Announce Type: new Abstract: Inferring nationality from personal names is a critical capability for equity and bias monitoring, personalization, and a valuable tool in biomedical an

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Nano-EmoX: Unifying Multimodal Emotional Intelligence from Perception to Empathy

arXiv:2603.02123v3 Announce Type: replace Abstract: The development of affective multimodal language models (MLMs) has long been constrained by a gap between low-level perception and high-level intera

Nationality encoding in language model hidden states: Probing culturally differentiated representations in persona-conditioned academic text

arXiv:2604.10151v1 Announce Type: new Abstract: Large language models are increasingly used as writing tools and pedagogical resources in English for Academic Purposes, but it remains unclear whether

Natural Gradient Gaussian Approximation Filter on Lie Groups for Robot State Estimation

model-releasesarxiv-cs-ro

arXiv:2604.10057v1 Announce Type: new Abstract: Accurate state estimation for robotic systems evolving on Lie group manifolds, such as legged robots, is a prerequisite for achieving agile control. How

Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score

arXiv:2405.16091v2 Announce Type: replace Abstract: Prompt learning has emerged as an efficient and effective method for fine-tuning vision-language models such as CLIP. While many studies have explor

researcharxiv-cs-cv

NetAgentBench: A State-Centric Benchmark for Evaluating Agentic Network Configuration

arXiv:2604.09678v1 Announce Type: cross Abstract: As agentic network management gains popularity, there is a critical need for evaluation frameworks that transcend static, one-shot testing. To address

Network Effects and Agreement Drift in LLM Debates

arXiv:2604.11312v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated an unprecedented ability to simulate human-like social behaviors, making them useful tools for simulati

NetworkNet: A Deep Neural Network Approach for Random Networks with Sparse Nodal Attributes and Complex Nodal Heterogeneity

arXiv:2604.11673v1 Announce Type: cross Abstract: Heterogeneous network data with rich nodal information become increasingly prevalent across multidisciplinary research, yet accurately modeling comple

Neural Generalized Mixed-Effects Models

model-releasesarxiv-cs-lg

arXiv:2604.10976v1 Announce Type: cross Abstract: Generalized linear mixed-effects models (GLMMs) are widely used to analyze grouped and hierarchical data. In a GLMM, each response is assumed to follo

Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance

arXiv:2604.03874v2 Announce Type: replace Abstract: Monitoring deforestation-driven carbon emissions requires both spatially explicit and temporally continuous estimates of aboveground biomass density

researcharxiv-cs-lg

Neural Stochastic Processes for Satellite Precipitation Refinement

arXiv:2604.10414v1 Announce Type: new Abstract: Accurate precipitation estimation is critical for flood forecasting, water resource management, and disaster preparedness. Satellite products provide gl

Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry

arXiv:2406.04301v3 Announce Type: replace Abstract: Reconstructing accurate surfaces from sparse multi-view images remains challenging due to severe geometric ambiguity and occlusions. Existing genera

tutorialsarxiv-cs-cv

Agents

Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions

arXiv:2604.09567v1 Announce Type: cross Abstract: Knowledge representation formalisms are aimed to represent general conceptual information and are typically used in the construction of the knowledge

agentsarxiv-cs-ai

NeuroFlow: Toward Unified Visual Encoding and Decoding from Neural Activity

arXiv:2604.09817v1 Announce Type: new Abstract: Visual encoding and decoding models act as gateways to understanding the neural mechanisms underlying human visual perception. Typically, visual encodin

researcharxiv-cs-lg

NeuroPath: Practically Adopting Motor Imagery Decoding through EEG Signals

arXiv:2604.09654v1 Announce Type: cross Abstract: Motor Imagery (MI) is an emerging Brain-Computer Interface (BCI) paradigm where a person imagines body movements without physical action. By decoding

tutorialsarxiv-cs-ai

NeuVolEx: Implicit Neural Features for Volume Exploration

arXiv:2604.11172v1 Announce Type: cross Abstract: Direct volume rendering (DVR) aims to help users identify and examine regions of interest (ROIs) within volumetric data, and feature representations t

researcharxiv-cs-cv

New Hybrid Fine-Tuning Paradigm for LLMs: Algorithm Design and Convergence Analysis Framework

arXiv:2604.09940v1 Announce Type: new Abstract: Fine-tuning Large Language Models (LLMs) typically involves either full fine-tuning, which updates all model parameters, or Parameter-Efficient Fine-Tun

Agents

NimbusGuard: A Novel Framework for Proactive Kubernetes Autoscaling Using Deep Q-Networks

arXiv:2604.11017v1 Announce Type: cross Abstract: Cloud native architecture is about building and running scalable microservice applications to take full advantage of the cloud environments. Managed K

agentsarxiv-cs-ai

Ninja Codes: Neurally Generated Fiducial Markers for Stealthy 6-DoF Tracking

arXiv:2510.18976v2 Announce Type: replace Abstract: In this paper we describe Ninja Codes, neurally generated fiducial markers that can be made to naturally blend into various real-world environments.

applicationsarxiv-cs-cv

Non-stationary Diffusion For Probabilistic Time Series Forecasting

arXiv:2505.04278v3 Announce Type: replace-cross Abstract: Due to the dynamics of underlying physics and external influences, the uncertainty of time series often varies over time. However, existing De

applicationsarxiv-cs-ai

Normative Common Ground Replication (NormCoRe): Replication-by-Translation for Studying Norms in Multi-Agent AI

arXiv:2603.11974v2 Announce Type: replace Abstract: In the late 2010s, the fashion trend NormCore framed sameness as a signal of belonging, illustrating how norms emerge through collective coordinatio

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NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning

arXiv:2604.10452v1 Announce Type: new Abstract: Olfaction lies at the intersection of chemical structure, neural encoding, and linguistic perception, yet existing representation methods fail to fully

safetyarxiv-cs-cl

Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Models

arXiv:2604.02340v2 Announce Type: replace-cross Abstract: Recent advances in masked diffusion language models (MDLMs) narrow the quality gap to autoregressive LMs, but their sampling remains expensive

researcharxiv-cs-cl

Not All Forgetting Is Equal: Architecture-Dependent Retention Dynamics in Fine-Tuned Image Classifiers

arXiv:2604.11508v1 Announce Type: cross Abstract: Fine-tuning pretrained image classifiers is standard practice, yet which individual samples are forgotten during this process, and whether forgetting

Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning

arXiv:2504.13818v4 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as the leading approach for enhancing reasoning capabilities in large langua

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Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception

arXiv:2604.09886v1 Announce Type: cross Abstract: Accurate volume estimation of objects from visual data is a long-standing challenge in computer vision with significant applications in robotics, logi

tutorialsarxiv-cs-ai

NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment

arXiv:2604.11543v1 Announce Type: cross Abstract: Novelty is a core requirement in academic publishing and a central focus of peer review, yet the growing volume of submissions has placed increasing p

NSFL: A Post-Training Neuro-Symbolic Fuzzy Logic Framework for Boolean Operators in Neural Embeddings

arXiv:2604.10604v1 Announce Type: cross Abstract: Standard dense retrievers lack a native calculus for multi-atom logical constraints. We introduce Neuro-Symbolic Fuzzy Logic (NSFL), a framework that

NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild

arXiv:2604.11487v1 Announce Type: new Abstract: This paper presents an overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, held in conjunction with the NTIRE works

applicationsarxiv-cs-cv

NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results

arXiv:2604.10551v1 Announce Type: new Abstract: This paper presents an overview of the NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models. This challenge utili

NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods

arXiv:2604.10321v1 Announce Type: new Abstract: In this paper, we review the NTIRE 2026 challenge on single-image reflection removal (SIRR) in the Wild. SIRR is a fundamental task in image restoration

applicationsarxiv-cs-cv

NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: AI Flash Portrait (Track 3)

arXiv:2604.11230v1 Announce Type: new Abstract: In this paper, we present a comprehensive overview of the NTIRE 2026 3rd Restore Any Image Model (RAIM) challenge, with a specific focus on Track 3: AI

NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results

arXiv:2604.10634v1 Announce Type: new Abstract: This paper presents an overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images. Building upon the success

Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding

arXiv:2604.11415v1 Announce Type: new Abstract: Remote sensing understanding inherently requires multi-resolution observation, since different targets and application tasks demand different levels of

OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language World Models

arXiv:2604.10866v1 Announce Type: new Abstract: AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor safety

ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification

arXiv:2604.10159v1 Announce Type: new Abstract: The advancement of large language models (LLMs) has enhanced tabular question answering (Tabular QA), yet they struggle with open-domain queries exhibit

Omnimodal Dataset Distillation via High-order Proxy Alignment

arXiv:2604.10666v1 Announce Type: cross Abstract: Dataset distillation compresses large-scale datasets into compact synthetic sets while preserving training performance, but existing methods are large