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Koopman-Based Robust Model Predictive Control for Nonlinear Systems with Stochastic Intermittent Measurements

arXiv:2609.02079v1 Announce Type: new Abstract: Intermittent state measurements pose fundamental challenges to model predictive control of constrained nonlinear systems because prediction uncertainty

researcharxiv-cs-ro

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Agents

KSG-Net: Key-Sparse and Global-Context Learning for Maritime 3D Ship Detection

DGX agent

arXiv:2609.02077v1 Announce Type: new Abstract: Accurate 3D ship detection in maritime environments is critical for autonomous navigation, yet remains challenging due to large-scale vessel variations,

agentsarxiv-cs-cv

3 Sept 2026

Language Model Maps for Prompt-Response Distributions via Log-Likelihood Vectors

arXiv:2603.18593v2 Announce Type: replace Abstract: We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing the

researcharxiv-cs-cl

Language Models Can Control Their Own Attention

arXiv:2609.02737v1 Announce Type: cross Abstract: Language models spend most of their attention on a small fraction of context, yet they read the entire KV cache to find the few tokens that matter. If

Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis

arXiv:2609.02805v1 Announce Type: new Abstract: Root cause analysis (RCA) is a critical task in telecom network operations, but diagnosing performance degradations in modern 5G and emerging 6G network

safetyarxiv-cs-ai

Local Ai

LaST-SR: Laplace-Inspired Steady-Transient Complex-Frequency Decomposition for Single Image Super-Resolution

arXiv:2609.02063v1 Announce Type: new Abstract: Single-image super-resolution (SISR) requires global context modeling for structurally consistent reconstruction. Fourier operators are increasingly ado

local-aiarxiv-cs-cv

Latent Cluster Analysis for Vision-Language-Action Models

arXiv:2609.02634v1 Announce Type: new Abstract: Vision-Language-Action (VLA) Models are increasingly used in robotics for their ability to ground language and perception into action, yet the internal

researcharxiv-cs-ro

Tutorials

Latent unified smooth Hamiltonians for excited state chemistry

arXiv:2609.01871v1 Announce Type: cross Abstract: We describe a neural network architecture and training procedure designed to model electronic ground and excited states of arbitrary molecular systems

tutorialsarxiv-cs-lg

LeakageBench: Document-Level Leakage Risk for Redacting Personally Identifiable Information in Document Images

arXiv:2609.02207v1 Announce Type: cross Abstract: Real-world personally identifiable information (PII) redaction often operates on document images---scans, screenshots, and PDF renderings---where OCR

Learn from Whoever Is Right: Answer-Verified Multi-Teacher Distillation for Multi-Domain LLMs

arXiv:2609.02548v1 Announce Type: cross Abstract: Modern large language models (LLMs) rely on reinforcement learning to build strong capabilities in individual domains, but integrating those capabilit

safetyarxiv-cs-ai

Tutorials

Learning and extrapolating scale-invariant processes

arXiv:2601.14810v3 Announce Type: replace-cross Abstract: Machine Learning (ML) has deeply changed some fields recently, like Language and Vision and we may expect it to be relevant also to the analys

tutorialsarxiv-cs-lg

Learning-Based Reconstruction Attacks on Coordinate-Obfuscated Point Clouds

arXiv:2609.02568v1 Announce Type: cross Abstract: Volumetric video based on point cloud representations enables immersive virtual and augmented reality applications but introduces significant challeng

Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA

arXiv:2609.01687v1 Announce Type: new Abstract: Grounded question answering systems should answer only when the supplied evidence supports the answer. In multi-hop QA, this requirement is difficult be

researcharxiv-cs-cl

Learning Spectral-Like Mesh-Free Discretisations

arXiv:2609.02833v1 Announce Type: cross Abstract: Meshfree methods such as smoothed particle hydrodynamics (SPH) with kernel corrections, radial basis function-generated finite differences (RBF-FD), a

Learning the Constitutive Behavior of Materials via Neural Operators and Causal Attention: Case Studies in Plasticity and Damage

arXiv:2609.02194v1 Announce Type: new Abstract: Classical constitutive modeling of path-dependent inelastic materials relies on internal state variables whose evolution equations must be postulated ba

Learning to Attract and Repel: Dual Quality Margin Learning for Face Recognition (DQM-Face)

arXiv:2609.02644v1 Announce Type: new Abstract: Face recognition in unconstrained environments remains highly challenging due to diverse and extreme variations encountered in real-world scenarios. To

applicationsarxiv-cs-cv

Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis

arXiv:2609.02473v1 Announce Type: new Abstract: Ontology rankers remain useful for rare-disease diagnosis because each candidate can be traced to matched patient phenotypes. Large language models (LLM

Local Ai

Learning to Track from Privileged Target Appearances

arXiv:2609.02471v1 Announce Type: new Abstract: Target templates define what a visual tracker searches for, yet the templates available at inference trade off localization certainty with appearance fr

local-aiarxiv-cs-cv

Learning with Volterra Neural Networks: A System Theoretic Perspective

model-releasesarxiv-cs-cv

arXiv:2609.01928v1 Announce Type: new Abstract: Higher-order interaction components are important for signal, image, and video modeling, but explicit high-order operators often suffer from rapidly inc

Lightweight Adaptation of General-Purpose VLMs for Multispectral and SAR Image Understanding

model-releasesarxiv-cs-cv

arXiv:2609.02187v1 Announce Type: new Abstract: General-purpose vision-language models (VLMs) now support strong visual recognition, instruction following, and generation. However, most pretrained vis

Linear Fusion MultiDiffusion for Fast Training-Free Spherical Panorama Generation

arXiv:2609.01997v1 Announce Type: new Abstract: We propose LF-MultiDiffusion, a training-free panorama generation method that extends MultiDiffusion to support linear projections between target and re

safetyarxiv-cs-cv

LLM-as-a-Judge Is Not an Oracle: Why Self-Improving Agents Need Deterministic Guardrails

arXiv:2609.02246v1 Announce Type: new Abstract: Self-improving agent pipelines have a problem at their center. An optimizer rewrites prompts to score higher, and the score comes from a judge that is i

safetyarxiv-cs-ai

LLM Watermarking as Big Data Provenance: A Deployment-Oriented Systematization

arXiv:2607.10103v2 Announce Type: replace-cross Abstract: As large language models (LLMs) become widely deployed, their outputs can be copied, transformed, and redistributed at scale without reliable

LoFi RADIO: A Distilled In-Domain Backbone Applied for Artifact-Severity Grading of Ultra-Low-Field Neonatal Brain MR

arXiv:2609.02676v1 Announce Type: cross Abstract: Ultra-low-field MRI makes neonatal brain imaging deploy- able in low-resource settings, but its low SNR, lack of shielding, and long scan duration mak

applicationsarxiv-cs-cv

Agents

LookStep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory

arXiv:2609.02350v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen environments. Recent progress has been lar

agentsarxiv-cs-cv

Loom: Weaving Diagnostic Strands into Free-Text Consensus via Embedding-Space Reweighting

arXiv:2609.02649v1 Announce Type: new Abstract: Aggregating noisy, conflicting textual hypotheses into a reliable consensus is a fundamental challenge when deploying NLP systems in real-world industri

Looped Transformers under the Jacobian Lens: Does the Global Workspace Survive Recurrence?

arXiv:2609.01924v1 Announce Type: new Abstract: Recent work identifies a mid-depth band of verbalisable, causally potent representations in a standard feedforward transformer --- a functional analogue

LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates

model-releasesarxiv-cs-lg

arXiv:2609.02734v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geo

MACAW: Reliable And Efficient Surgical Debridement Using Monocular Adaptive Compact Attention Windows

arXiv:2609.01961v1 Announce Type: new Abstract: Augmenting the dexterity of human surgeons has the potential to free them from tedious subtasks. We consider debridement (removal of diseased or dead ti

researcharxiv-cs-ro

MAOL: Morphology-Aware Ordinal Learning for Fine-Grained Industrial Defect Severity Grading

arXiv:2609.02266v1 Announce Type: new Abstract: Fine-grained defect severity grading is essential for industrial inspection, yet remains challenging due to the ordinal nature of severity labels, the s

applicationsarxiv-cs-cv

Marginal Expected Revenue for Jointly Ranking Auction and Fixed-Price Listings in E-Commerce Sponsored Search

arXiv:2609.01628v1 Announce Type: cross Abstract: E-commerce search ranking must balance multiple objectives--relevance, user engagement, and platform revenue--when allocating impression slots to comp

applicationsarxiv-cs-lg

MARS: What Retrieval Signals Are Hidden in Multimodal Large Language Models for Text-Video Retrieval?

arXiv:2609.02565v1 Announce Type: new Abstract: Text-video retrieval requires representations that can distinguish videos with similar scenes, actions, and temporal patterns. Recent multimodal large l

researcharxiv-cs-cv

Agents

MASkills: Continual Skills Optimization for Multi-Agent LLM Systems

arXiv:2609.02094v1 Announce Type: new Abstract: LLM-based multi-agent systems have shown strong performance on complex tasks, yet continual improvement from interaction experience remains challenging.

agentsarxiv-cs-ai

Local Ai

MeanField Surrogate Modeling for Scalable Runtime Scheduling of Concurrent Heterogeneous AI Inference on Shared GPUs

arXiv:2609.02109v1 Announce Type: cross Abstract: Deploying heterogeneous AI models concurrently on a shared GPU introduces resource contention that complicates runtime scheduling. While surrogate mod

local-aiarxiv-cs-ai

Measurement-Driven Sub-Network Selection for On-Premise Retrieval-Augmented Factory Agents

arXiv:2609.02760v1 Announce Type: new Abstract: On-premise assistants can give factory workers conversational access to machine documentation, but models capable of the task rarely fit shop-floor hard

Median-of-Means as an Extremal Convex Estimator and a Nonconvex Route to the Trimmed Oracle

arXiv:2609.01689v1 Announce Type: new Abstract: We revisit median-of-means estimation from a deterministic optimization viewpoint and develop a family of block-Lp estimators for robust learning with h

Mediocrity is the key for LLM as a Judge Anchor Selection

arXiv:2603.16848v2 Announce Type: replace Abstract: The ``LLM-as-a-judge'' paradigm has become a standard method for evaluating open-ended generation. To address the quadratic scalability costs of pai

MemeCULT-1K: Benchmarking South Asian Cultural Context and Humor Understanding of Multimodal Models

arXiv:2609.01772v1 Announce Type: new Abstract: Meme understanding goes beyond recognizing visual content or literal text; it requires implicit cultural knowledge and pragmatic inference that most vis

MESSY STREETS: A Benchmark for Geocoding Real-World Addresses

arXiv:2609.01612v1 Announce Type: cross Abstract: We introduce MESSY STREETS, a benchmark for evaluating geocoders on verbatim web addresses, with existence verification and controlled measurement of

Meta-ethics and AI: exploring the novel meta-ethical questions in the era of AI

arXiv:2609.01685v1 Announce Type: new Abstract: With the development of artificial intelligence (AI), the landscape of meta-ethics, which has largely centred on human ethics, faces pressures that may

MineTRACE: An Evidence-Grounded Interactive Reasoning System for Mineral Prospectivity

arXiv:2609.02060v1 Announce Type: new Abstract: Mineral exploration requires integrating heterogeneous geochemical, geophysical, and geological evidence, yet existing prospectivity systems often provi

MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction

arXiv:2603.21653v2 Announce Type: replace Abstract: Predicting the next mobile app a user will launch is essential for proactive mobile services. Yet accurate prediction remains challenging in real-wo

applicationsarxiv-cs-lg

MM++: Post-Hoc Scale-Invariant Multilayer OOD Detection via Top-K Gated Feature Fusion

arXiv:2606.17352v2 Announce Type: replace-cross Abstract: We introduce MM++ (Multilayer Mahalanobis++), a strictly post-hoc, and scale-invariant framework for Out-of-Distribution (OOD) detection. To a

researcharxiv-cs-cv

MMTryon: Multi-Modal Multi-Reference Control for High-Quality Fashion Generation

arXiv:2405.00448v5 Announce Type: replace Abstract: This paper introduces MMTryon, a multi-modal multi-reference VIrtual Try-ON (VITON) framework, which can generate high-quality compositional try-on

researcharxiv-cs-cv

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy

arXiv:2505.21907v3 Announce Type: replace Abstract: AI copilots represent a new generation of AI-powered systems designed to assist users, particularly knowledge workers and developers, in complex, co

Modeling What Changes: Sparse, Residual World Models for Object-Centric Manipulation

arXiv:2609.02046v1 Announce Type: cross Abstract: Monolithic world models predict the entire next state at every step, spending capacity re-predicting the static majority of a scene and injecting erro

Momentum in large-batch training: Polyak enlarges the critical batch size, Nesterov improves data efficiency

arXiv:2609.02728v1 Announce Type: cross Abstract: We study when and how momentum improves large-batch training in the one-pass regime, using power-law kernel regression as a tractable setting. We firs

Agents

Monitoring Web Agents Without Internal Signals: Observable Trajectories and Key-Step Supervision

arXiv:2609.02057v1 Announce Type: new Abstract: Reliable web-agent monitoring is difficult when model-internal uncertainty signals such as token logits are unavailable. In this work, we study prefix-l

agentsarxiv-cs-ai