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Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory

model-releasesarxiv-cs-cl

arXiv:2602.15313v2 Announce Type: replace Abstract: AI Memory, specifically how models organizes and retrieves historical messages, becomes increasingly valuable to Large Language Models (LLMs), yet e

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Research

Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference

DGX agent

arXiv:2604.08971v1 Announce Type: new Abstract: Edge devices increasingly run multimodal sensing pipelines that must remain accurate despite fluctuating power budgets and unpredictable sensor dropout.

researcharxiv-cs-lg

13 Apr 2026

Model Space Reasoning as Search in Feedback Space for Planning Domain Generation

arXiv:2604.08712v1 Announce Type: new Abstract: The generation of planning domains from natural language descriptions remains an open problem even with the advent of large language models and reasonin

agentsarxiv-cs-ai

MolPaQ: Modular Quantum-Classical Patch Learning for Interpretable Molecular Generation

arXiv:2604.08575v1 Announce Type: cross Abstract: Molecular generative models must jointly ensure validity, diversity, and property control, yet existing approaches typically trade off among these obj

MONETA: Multimodal Industry Classification through Geographic Information with Multi Agent Systems

arXiv:2604.07956v2 Announce Type: replace Abstract: Industry classification schemes are integral parts of public and corporate databases as they classify businesses based on economic activity. Due to

Mosaic: Multimodal Jailbreak against Closed-Source VLMs via Multi-View Ensemble Optimization

arXiv:2604.09253v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are powerful but remain vulnerable to multimodal jailbreak attacks. Existing attacks mainly rely on either explicit visu

MSMO-ABSA: Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis

arXiv:2502.13718v2 Announce Type: replace Abstract: Aspect-based sentiment analysis (ABSA) garnered growing research interest in multilingual contexts in the past. However, the majority of the studies

safetyarxiv-cs-cl

MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation

arXiv:2604.08782v1 Announce Type: new Abstract: Large language models (LLMs) suffer significant performance degradation when user instructions and context are distributed over multiple conversational

agentsarxiv-cs-cl

Multi-agent Adaptive Mechanism Design

arXiv:2512.21794v3 Announce Type: replace-cross Abstract: We study a sequential mechanism design problem in which a principal seeks to elicit truthful reports from multiple rational agents while start

agentsarxiv-cs-ai

Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication

arXiv:2604.08944v1 Announce Type: new Abstract: Multi-agent coordination under partial observability requires agents to share complementary private information. While recent methods optimize messages

agentsarxiv-cs-lg

Multi-task Just Recognizable Difference for Video Coding for Machines: Database, Model, and Coding Application

arXiv:2604.09421v1 Announce Type: cross Abstract: Just Recognizable Difference (JRD) boosts coding efficiency for machine vision through visibility threshold modeling, but is currently limited to a si

researcharxiv-cs-cv

Multi-User Large Language Model Agents

arXiv:2604.08567v1 Announce Type: new Abstract: Large language models (LLMs) and LLM-based agents are increasingly deployed as assistants in planning and decision making, yet most existing systems are

agentsarxiv-cs-cl

Multimodal Anomaly Detection for Human-Robot Interaction

arXiv:2604.09326v1 Announce Type: cross Abstract: Ensuring safety and reliability in human-robot interaction (HRI) requires the timely detection of unexpected events that could lead to system failures

safetyarxiv-cs-cv

Multivariate Time Series Anomaly Detection via Dual-Branch Reconstruction and Autoregressive Flow-based Residual Density Estimation

arXiv:2604.08582v1 Announce Type: cross Abstract: Multivariate Time Series Anomaly Detection (MTSAD) is critical for real-world monitoring scenarios such as industrial control and aerospace systems. M

Musculoskeletal Motion Imitation for Learning Personalized Exoskeleton Control Policy in Impaired Gait

arXiv:2604.09431v1 Announce Type: new Abstract: Designing generalizable control policies for lower-limb exoskeletons remains fundamentally constrained by exhaustive data collection or iterative optimi

safetyarxiv-cs-ro

Music Audio-Visual Question Answering Requires Specialized Multimodal Designs

arXiv:2505.20638v2 Announce Type: replace-cross Abstract: While recent Multimodal Large Language Models exhibit impressive capabilities for general multimodal tasks, specialized domains like music nec

researcharxiv-cs-cv

MuTSE: A Human-in-the-Loop Multi-use Text Simplification Evaluator

arXiv:2604.08947v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become increasingly prevalent in text simplification, systematically evaluating their outputs across diverse prompting

Tutorials

MV3DIS: Multi-View Mask Matching via 3D Guides for Zero-Shot 3D Instance Segmentation

arXiv:2604.08916v1 Announce Type: new Abstract: Conventional 3D instance segmentation methods rely on labor-intensive 3D annotations for supervised training, which limits their scalability and general

tutorialsarxiv-cs-cv

Natural Riemannian gradient for learning functional tensor networks

model-releasesarxiv-cs-lg

arXiv:2604.09263v1 Announce Type: cross Abstract: We consider machine learning tasks with low-rank functional tree tensor networks (TTN) as the learning model. While in the case of least-squares regre

NCL-BU at SemEval-2026 Task 3: Fine-tuning XLM-RoBERTa for Multilingual Dimensional Sentiment Regression

model-releasesarxiv-cs-cl

arXiv:2604.08923v1 Announce Type: new Abstract: Dimensional Aspect-Based Sentiment Analysis (DimABSA) extends traditional ABSA from categorical polarity labels to continuous valence-arousal (VA) regre

Applications

Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning

arXiv:2604.08980v1 Announce Type: cross Abstract: Graph neural networks (GNNs) have been widely adopted in engineering applications such as social network analysis, chemical research and computer visi

applicationsarxiv-cs-ai

Nested Radially Monotone Polar Occupancy Estimation: Clinically-Grounded Optic Disc and Cup Segmentation for Glaucoma Screening

arXiv:2604.09062v1 Announce Type: new Abstract: Valid segmentation of the optic disc (OD) and optic cup (OC) from fundus photographs is essential for glaucoma screening. Unfortunately, existing deep l

researcharxiv-cs-cv

Neural Distribution Prior for LiDAR Out-of-Distribution Detection

arXiv:2604.09232v1 Announce Type: cross Abstract: LiDAR-based perception is critical for autonomous driving due to its robustness to poor lighting and visibility conditions. Yet, current models operat

Neural networks for Text-to-Speech evaluation

arXiv:2604.08562v1 Announce Type: cross Abstract: Ensuring that Text-to-Speech (TTS) systems deliver human-perceived quality at scale is a central challenge for modern speech technologies. Human subje

Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution

arXiv:2502.06809v3 Announce Type: replace-cross Abstract: Pervasive polysemanticity in large language models (LLMs) undermines discrete neuron-concept attribution, posing a significant challenge for m

local-aiarxiv-cs-ai

Nexus: Same Pretraining Loss, Better Downstream Generalization via Common Minima

model-releasesarxiv-cs-lg

arXiv:2604.09258v1 Announce Type: new Abstract: Pretraining is the cornerstone of Large Language Models (LLMs), dominating the vast majority of computational budget and data to serve as the primary en

No Single Best Model for Diversity: Learning a Router for Sample Diversity

arXiv:2604.02319v2 Announce Type: replace Abstract: When posed with prompts that permit a large number of valid answers, comprehensively generating them is the first step towards satisfying a wide ran

researcharxiv-cs-cl

Noise-Aware In-Context Learning for Hallucination Mitigation in ALLMs

arXiv:2604.09021v1 Announce Type: cross Abstract: Auditory large language models (ALLMs) have demonstrated strong general capabilities in audio understanding and reasoning tasks. However, their reliab

Applications

NOMAD: Generating Embeddings for Massive Distributed Graphs

arXiv:2604.09419v1 Announce Type: new Abstract: Successful machine learning on graphs or networks requires embeddings that not only represent nodes and edges as low-dimensional vectors but also preser

applicationsarxiv-cs-lg

NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Multi-Exposure Image Fusion in Dynamic Scenes (Track 2)

model-releasesarxiv-cs-cv

arXiv:2604.09030v1 Announce Type: new Abstract: This paper presents NTIRE 2026, the 3rd Restore Any Image Model (RAIM) challenge on multi-exposure image fusion in dynamic scenes. We introduce a benchm

NyayaMind- A Framework for Transparent Legal Reasoning and Judgment Prediction in the Indian Legal System

arXiv:2604.09069v1 Announce Type: cross Abstract: Court Judgment Prediction and Explanation (CJPE) aims to predict a judicial decision and provide a legally grounded explanation for a given case based

OASIS: Online Activation Subspace Learning for Memory-Efficient Training

arXiv:2604.09406v1 Announce Type: new Abstract: Training large language models (LLMs) is constrained by memory requirements, with activations accounting for a substantial fraction of the total footpri

researcharxiv-cs-lg

Off-the-shelf Vision Models Benefit Image Manipulation Localization

arXiv:2604.09096v1 Announce Type: new Abstract: Image manipulation localization (IML) and general vision tasks are typically treated as two separate research directions due to the fundamental differen

local-aiarxiv-cs-cv

Offline-First LLM Architecture for Adaptive Learning in Low-Connectivity Environments

arXiv:2603.03339v5 Announce Type: replace-cross Abstract: Artificial intelligence (AI) and large language models (LLMs) are transforming educational technology by enabling conversational tutoring, per

local-aiarxiv-cs-cl

Offline Local Search for Online Stochastic Bandits

arXiv:2604.09423v1 Announce Type: new Abstract: Combinatorial multi-armed bandits provide a fundamental online decision-making environment where a decision-maker interacts with an environment across T

researcharxiv-cs-lg

Tutorials

OmniPrism: Learning Disentangled Visual Concept for Image Generation

arXiv:2412.12242v2 Announce Type: replace-cross Abstract: Creative visual concept generation often draws inspiration from specific concepts in a reference image to produce relevant outcomes. However,

tutorialsarxiv-cs-ai

On Divergence Measures for Training GFlowNets

arXiv:2410.09355v2 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) are amortized inference models designed to sample from unnormalized distributions over composable objects, with a

On Semiotic-Grounded Interpretive Evaluation of Generative Art

arXiv:2604.08641v1 Announce Type: cross Abstract: Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. Howeve

On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs

arXiv:2509.25214v3 Announce Type: replace-cross Abstract: As increasingly large pre-trained models are released, deploying them on edge devices for privacy-preserving applications requires effective c

On the Limits of Layer Pruning for Generative Reasoning in Large Language Models

arXiv:2602.01997v2 Announce Type: replace-cross Abstract: Recent work has shown that layer pruning can effectively compress large language models (LLMs) while retaining strong performance on classific

researcharxiv-cs-ai

On the Representational Limits of Quantum-Inspired 1024-D Document Embeddings: An Experimental Evaluation Framework

arXiv:2604.09430v1 Announce Type: cross Abstract: Text embeddings are central to modern information retrieval and Retrieval-Augmented Generation (RAG). While dense models derived from Large Language M

On the Role of DAG topology in Energy-Aware Cloud Scheduling : A GNN-Based Deep Reinforcement Learning Approach

arXiv:2604.09202v1 Announce Type: cross Abstract: Cloud providers must assign heterogeneous compute resources to workflow DAGs while balancing competing objectives such as completion time, cost, and e

On the Spectral Geometry of Cross-Modal Representations: A Functional Map Diagnostic for Multimodal Alignment

arXiv:2604.08579v1 Announce Type: cross Abstract: We study cross-modal alignment between independently pretrained vision (DINOv2) and language (all-MiniLM-L6-v2) encoders using the functional map fram

On the Terminology and Geometric Aspects of Redundant Parallel Manipulators

arXiv:2604.09156v1 Announce Type: new Abstract: Parallel kinematics machines (PKM) can exhibit kinematic as well as actuation redundancy. While the meaning of kinematic redundancy has been clarified a

researcharxiv-cs-ro

One Interface, Many Robots: Unified Real-Time Low-Level Motion Planning for Collaborative Arms

arXiv:2604.08787v1 Announce Type: new Abstract: This paper proposes a common interface for real-time low-level motion planning of collaborative robotic arms, aimed at enabling broader applicability an

researcharxiv-cs-ro

Online Intention Prediction via Control-Informed Learning

model-releasesarxiv-cs-lg

arXiv:2604.09303v1 Announce Type: cross Abstract: This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is

Online Quantile Regression for Nonparametric Additive Models

arXiv:2604.08969v1 Announce Type: cross Abstract: This paper introduces a projected functional gradient descent algorithm (P-FGD) for training nonparametric additive quantile regression models in onli

researcharxiv-cs-lg

Online3R: Online Learning for Consistent Sequential Reconstruction Based on Geometry Foundation Model

arXiv:2604.09480v1 Announce Type: new Abstract: We present Online3R, a new sequential reconstruction framework that is capable of adapting to new scenes through online learning, effectively resolving

local-aiarxiv-cs-cv