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LRCC: Generalizing Low-Rank Compression with Conditional Computation

model-releasesarxiv-cs-cl

arXiv:2610.08858v1 Announce Type: new Abstract: Low-rank compression reduces the cost of pretrained language models by replacing linear transformations with low-rank factorizations. However, conventio

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m-Set Adversarial Bandits with Winner Feedback

DGX agent

arXiv:2610.10128v1 Announce Type: new Abstract: We show upper and lower bounds on the regret of m-set adversarial bandits for different utilities (winner reward or sum of rewards) and feedback models

researcharxiv-cs-lg

8 Oct 2026

Machine learning modularity

arXiv:2601.01779v2 Announce Type: replace-cross Abstract: Based on a transformer based sequence-to-sequence architecture combined with a dynamic batching algorithm, this work introduces a machine lear

MagCilia: A Compact Magnetociliary Tactile Sensor with 3D Force Sensing for Robotic Contact Perception and Grasping Feedback

arXiv:2610.09536v1 Announce Type: new Abstract: Robotic grasping and surface exploration benefit from simultaneous measurement of normal and tangential forces and from surface information obtained thr

researcharxiv-cs-ro

MAGIC: Learning from Visibility Asymmetry for Unsupervised Stereo Matching

arXiv:2508.10838v3 Announce Type: replace Abstract: Learning disparity in occluded regions remains difficult for unsupervised stereo matching. Photometric supervision lacks valid target-view correspon

Agents

Making Task Abstractions Executable: Control-Aware Layout Repair for a Fixed Controller

arXiv:2610.10281v1 Announce Type: new Abstract: A task abstraction can specify the intended events while its spatial layout prevents a fixed agent and controller from completing them. Starting from a

agentsarxiv-cs-ro

MambaDSF: Multi-Scale SSM with Dilated Feature Fusion for Sonar Small Target Detection

arXiv:2605.24928v2 Announce Type: replace Abstract: Sonar imaging is the primary modality for underwater target detection, yet small targets remain difficult to detect due to insufficient pixel covera

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning

arXiv:2607.18130v2 Announce Type: replace Abstract: Finetuning methods for foundation models usually change weights, prompts, or hidden states, while leaving the residual topology fixed. We ask whethe

Many Ways to Succeed: Diversity-Driven RL Fine-Tuning for VLA Generalization

arXiv:2610.09943v1 Announce Type: cross Abstract: Reinforcement learning (RL) fine-tuning improves vision-language-action (VLA) policies through closed-loop experience, yet generalization beyond the f

safetyarxiv-cs-lg

MaRK: Markov-adapted Recurrent Kernels for Dynamic Operator Conditioning in State Space Models

arXiv:2610.09092v1 Announce Type: new Abstract: State Space Models (SSMs) offer an efficient alternative to Transformers for sequence modeling, yet conditioning pre-trained SSMs for iterative generati

Marrying Pricing and Advertising with LLMs

arXiv:2610.09985v1 Announce Type: cross Abstract: We study a sequential pricing problem in which a seller jointly posts a price and an advertisement generated by a large language model (LLM). The sell

safetyarxiv-cs-lg

Masked Feature Encoding for Large-Scale Whole Slide Image Representation

arXiv:2610.10225v1 Announce Type: new Abstract: Whole slide image (WSI) analysis in computational pathology follows a multiple instance learning (MIL) pipeline where patch embeddings are extracted ind

local-aiarxiv-cs-cv

Matching of signal, noise and hardware timescales for filtering and forecasting of correlated noise signals

arXiv:2610.10037v1 Announce Type: new Abstract: Physical reservoir computing exploits the nonlinear dynamics of physical systems to process time-dependent data with greater energy efficiency than conv

tutorialsarxiv-cs-lg

MCFR: A Mask-Guided Coarse-to-Fine Regression Framework for Robust Multi-Variant Board-to-Board Connector Assembly

arXiv:2610.09327v1 Announce Type: new Abstract: Automated insertion of board-to-board (BTB) connectors in 3C manufacturing requires both high visual accuracy and strong deployment robustness. This pro

safetyarxiv-cs-ro

Measurement-Efficient Differentiable Quantum Architecture Search for Combinatorial Optimization

arXiv:2610.10351v1 Announce Type: cross Abstract: Differentiable quantum architecture search (DQAS) is a promising framework for the automated design of quantum circuits, particularly for variational

Mechanics of Long-Context Hybrid Models Part 1.1: From Hybrid Attention to Hybrid Position

arXiv:2610.10114v1 Announce Type: new Abstract: The architectural design of Large Language Models (LLMs) is shifting from traditional full-attention-only models to hybrid models, which combine differe

tutorialsarxiv-cs-cl

MemoCare: An Interactive Multimodal Mobile System for Automated Cognitive Screening

arXiv:2610.10448v1 Announce Type: cross Abstract: MemoCare is an interactive mobile system for automated multimodal cognitive screening. A React Native application combines spoken responses, temporal

local-aiarxiv-cs-cv

MeshCarve: Artisan Mesh Generation with Flow Matching in Compact Latent Spaces

arXiv:2610.09723v1 Announce Type: new Abstract: Prior artisan mesh generation works largely predict face tokens autoregressively, which makes inference slow. Recent methods instead flow match continuo

MeshSIPP: Efficient Lattice Planning in Dynamic Environment

model-releasesarxiv-cs-ro

arXiv:2610.09652v1 Announce Type: cross Abstract: Autonomous navigation in dynamic environments requires computing spatiotemporal trajectories that satisfy non-holonomic motion constraints. When the t

MimicX: Policy-in-the-Loop Supervision Refinement for Video-Driven Humanoid Motion Tracking

arXiv:2610.09055v1 Announce Type: new Abstract: Human videos provide rich motion targets for humanoid learning, yet visually plausible references can still produce persistent failures under physics-ba

safetyarxiv-cs-ro

MIRROR: From Imitation to Internalization in LLM Personalization

arXiv:2610.09795v1 Announce Type: new Abstract: The demand for personalized LLMs is shifting from style imitation toward content quality. We investigate whether self-distillation can bridge this gap i

safetyarxiv-cs-cl

Mitigating Accent-Language Confusion in Self-Supervised Speech Representations for Language Identification

arXiv:2610.09486v1 Announce Type: cross Abstract: Spoken language identification (LID) aims to recognize the target language regardless of accent. In practice, however, LID models fine-tuned from self

safetyarxiv-cs-cl

MixedPEFT: Combining Multiple PEFT Methods with Mixed Objectives for Unsupervised Domain Adaptation

model-releasesarxiv-cs-cl

arXiv:2606.22272v2 Announce Type: replace Abstract: Applying pre-trained language models to new domains through full fine-tuning is computationally expensive and prone to catastrophic forgetting. To a

Mixture of Layers: Dynamic Layer Routing for Visual Reasoning

arXiv:2610.09440v1 Announce Type: new Abstract: Pre-trained vision encoders contain layer-wise visual representations that differ in spatial granularity, semantic abstraction, and sensitivity to local

Modified Loss of Momentum Gradient Descent: Fine-Grained Analysis

arXiv:2509.08483v2 Announce Type: replace Abstract: We analyze gradient descent with Polyak (1964) heavy-ball momentum (HB) whose fixed momentum hyperparameter eta in (0, 1) provides exponential decay

Applications

MoR-MLLM: Mixture of Recursions for Efficient Multimodal Large Language Models

arXiv:2610.08830v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities across vision and language tasks. However, their massive co

applicationsarxiv-cs-cv

MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection

arXiv:2610.09473v1 Announce Type: new Abstract: Time-series anomaly detection (TSAD) identifies deviations from patterns learned from historical data. In non-stationary settings, distribution drift an

tutorialsarxiv-cs-lg

MORCA: Offline-to-Online Reinforcement Learning for Adaptive Cache Reuse in Video Diffusion Acceleration

arXiv:2610.10457v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) achieve remarkable performance in video synthesis, but their iterative denoising process suffers from high inference laten

tutorialsarxiv-cs-cv

MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition

arXiv:2610.10245v1 Announce Type: new Abstract: Despite the ubiquity of sensors in wearable and mobile devices and the abundance of human movement data they generate, translating unlabeled recordings

MOTIF: Person-of-Interest Deepfake Detection Beyond 3DMM Coefficients

arXiv:2610.09830v1 Announce Type: new Abstract: Video deepfakes targeting a specific individual, the Person-of-Interest (POI), are the most harmful ones, and, since a public figure is abundantly recor

MOTIP2: Spatial Priors for End-to-End Multi-Object Tracking

arXiv:2610.10391v1 Announce Type: new Abstract: End-to-end multi-object trackers have narrowed the gap with classical tracking-by-detection on association-difficult benchmarks. Yet they still make spa

safetyarxiv-cs-cv

MovieSTAGE: Scene, Transition, and Global Encoding for Movie-fMRI ADHD Classification

arXiv:2610.09306v1 Announce Type: new Abstract: Naturalistic movie-fMRI provides a shared, temporally structured probe of brain dynamics, yet predictive models commonly rely on whole-run functional co

MSU Team at the Explainable Deepfake Detection Challenge 2026: Grounded Artifact Evidence for Deepfake Detection

arXiv:2610.09952v1 Announce Type: new Abstract: Recent advances in generative image models have made many manipulated images highly realistic, raising the need for detectors that are not only accurate

local-aiarxiv-cs-cv

Agents

Multi-Agent Coordination via Support-Preserving Distillation

arXiv:2610.10087v1 Announce Type: new Abstract: Offline MARL increasingly relies on generative policies to model multimodal joint behavior, typically by distilling a centralized teacher into decentral

agentsarxiv-cs-lg

Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentation

arXiv:2602.10137v2 Announce Type: replace Abstract: This work proposes MeCSAFNet, a multi-branch encoder-decoder architecture for land cover segmentation in multispectral imagery. The model separately

Multi-Label Topic Assignment via LLM Distillation: A Comparative Analysis of Generative vs. Discriminative Student Models

model-releasesarxiv-cs-cl

arXiv:2610.09063v1 Announce Type: cross Abstract: Multi-label topic assignment for user-generated content (UGC) -- including product reviews and buyer-seller conversations -- poses unique scalability

Multi-Objective Aligned Small Language Model Framework for SUD Patient Dialogue Generation

arXiv:2610.09209v1 Announce Type: new Abstract: Substance Use Disorder (SUD) counseling requires patient responses that reflect underlying cognitive states such as beliefs, coping strategies, and read

safetyarxiv-cs-cl

MultiFly: A Real-World Multimodal Aerial Dataset with Annotation-Efficient Label Transfer and Cross-Modal Semantic Consistency

arXiv:2610.10359v1 Announce Type: cross Abstract: We introduce MultiFly, a real-world, low-altitude UAV dataset for semantic perception across RGB, thermal, LiDAR, and radar modalities. MultiFly provi

Multimodal LLMs Can Learn to Read Brain Signals: A Vision--Language Model for Unified Multi-Task EEG Decoding

arXiv:2610.09355v1 Announce Type: cross Abstract: Learning EEG representations that generalize across cognitive tasks, subjects, and recording conditions remains a key challenge in electroencephalogra

tutorialsarxiv-cs-cv

Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE)

arXiv:2602.03317v2 Announce Type: replace-cross Abstract: Quantitative imaging methods, such as magnetic resonance fingerprinting (MRF), aim to extract interpretable pathology biomarkers by estimating

MUNITE: Unified Multimodal Latent Inference for Any-to-Any Multimodal Generation

arXiv:2610.09866v1 Announce Type: new Abstract: We introduce MUNITE, a latent-variable framework for flexible any-to-any multimodal generation that treats encoding and latent generation as the same in

safetyarxiv-cs-lg

Agents

NAViLoss: An Underwater Navigation-Aware Dual-Residual Objective for Physics-Consistent Learning

arXiv:2610.09690v1 Announce Type: cross Abstract: Autonomous underwater vehicles (AUVs) commonly rely on inertial navigation systems (INS) aided by Doppler velocity logs (DVLs) for reliable underwater

agentsarxiv-cs-lg

Neighborhood Smoothing for Calibration

arXiv:2610.09020v1 Announce Type: new Abstract: Modern neural networks are often miscalibrated, with a tendency to overconfidence. Existing train-time calibration methods largely modify task losses or

local-aiarxiv-cs-lg

NeRFifyMesh: Optimizing Neural Radiance Fields from Textured Meshes for Robotics Scene Building

arXiv:2610.10387v1 Announce Type: new Abstract: In robotics, scene representation plays a pivotal role in understanding and interacting with the environment. The advent of Neural Radiance Fields (NeRF

researcharxiv-cs-ro

Neural Sampling with Reweighted Normalizing Flows via the Wasserstein--Fisher--Rao JKO Scheme

arXiv:2610.10278v1 Announce Type: cross Abstract: We propose a neural algorithm for sampling from distributions specified by unnormalized Boltzmann densities. Our approach is based on the Jordan--Kind

NeuralBES: A Differentiable, Control-Aware Emulator for Scalable Building Energy Modeling

arXiv:2610.10459v1 Announce Type: new Abstract: Demand-side flexibility i.e. forecasting, shifting, and curtailing residential energy loads, depends on thermal models trusted across millions of hetero

Hardware

NeuralZip: Reusable Setup for Fast Lossless Compression

arXiv:2610.09916v1 Announce Type: new Abstract: Lossless compression can reduce the storage and movement of model weights without changing their floating-point values, but repeated statistical analysi

hardwarearxiv-cs-lg

Never Look Back: Understanding Persistence in 3D Object Memory from Egocentric Videos

arXiv:2610.10538v1 Announce Type: new Abstract: As we move through the world and carry out everyday tasks, we encounter objects that may become relevant only later. We are capable of recalling where w