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MegaGraph: Towards Efficient Training of Large-Scale Graph Transformers with Automated Hybrid Parallelism

arXiv:2609.34420v1 Announce Type: new Abstract: Graph Transformers (GTs) offer superior representation capabilities by overcoming the depth limitations and over-smoothing issues of traditional Graph N

safetyarxiv-cs-lg

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Model Releases

Memory as a cache: Exact context reuse and deletion by construction

DGX agent

arXiv:2609.32395v1 Announce Type: cross Abstract: The KV cache of a transformer entangles every token's representation with its entire prefix: a passage encoded once cannot be reused under a different

model-releasesarxiv-cs-lg

29 Sept 2026

Memory in the Sky: Low-Altitude Question Answering with Multi-Agent Memory Aggregation

model-releasesarxiv-cs-ro

arXiv:2609.35431v1 Announce Type: new Abstract: This paper studies low-altitude question answering (LAQA), in which distributed unmanned aerial vehicle (UAV) memories are aggregated at a ground server

MemTransfer: Benchmarking Memory Beyond Matched Experience in Embodied Decision-Making

model-releasesarxiv-cs-ro

arXiv:2609.32313v1 Announce Type: new Abstract: Memory lets an embodied agent reuse past experience, yet retaining useful information does not ensure that the agent can apply it when conditions change

Mend the Measurement Gap: Latent User Preference Modeling for Short-Form Video Recommendation

arXiv:2609.32839v1 Announce Type: cross Abstract: Recommender systems rely heavily on heterogeneous behavioral feedback to infer user preference. Although abundant, these signals are imperfect measure

MeqMuon: Matrix-Equilibrating Muon for LLM Pretraining

arXiv:2609.35701v1 Announce Type: new Abstract: The success of large language models (LLMs) has been accompanied by continued growth in model size and pretraining costs. Muon offers high accuracy and

Agents

Meta’s Muse AI sent a YouTuber’s address to a stranger

Tech YouTuber Matt Robb says that Muse gave out his home address to a total stranger this weekend, after authorizing the bot to handle his Facebook Marketplace account. That's despite Meta placing gre

agentsthe-verge-ai

MetaSampling: Making Frame Samplers Efficient for Long-Video Question Answering

model-releasesarxiv-cs-cv

arXiv:2609.33998v1 Announce Type: new Abstract: Frame selection is an important component of long-video question answering (VQA) with Multimodal Large Language Models (MLLMs). Existing frame-selection

Tutorials

Metro-WM: Long-Horizon Latent Planning with Realisable Sub-Goals

arXiv:2609.31868v1 Announce Type: cross Abstract: Model-predictive control with Joint-Embedding Predictive Architectures (JEPAs) provides a strong zero-shot goal-reaching planner, but it is only effec

tutorialsarxiv-cs-ro

MiCo: Mutual Information Coverage Optimization through Semantic Erasure Modeling for Efficient MLLM Inference

arXiv:2609.34330v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated impressive performance in multimodal understanding, but processing large numbers of visual to

Mind the RefGAP: Correcting Reference Attention in Diffusion-Based Visual Editing

arXiv:2609.35708v1 Announce Type: new Abstract: Reference-guided diffusion editors struggle to faithfully reproduce user-provided references. We identify a potential bottleneck in diffusion editors: m

Mind the Spike: Mechanisms and Brittleness of Visual Massive Activations in Large Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2609.32808v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) inherit massive activations from their text-only bases: spikes where a few fixed hidden channels receive values t

Minimax Last-Iterate Convergence in Matrix Games with Observed Actions

arXiv:2609.34656v1 Announce Type: new Abstract: We study last-iterate convergence in unknown two-player zero-sum matrix games with bandit payoff feedback and observed opponent actions. For games with

Minimax-Optimality of Posterior Sampling for Reinforcement Learning

arXiv:2609.33246v1 Announce Type: new Abstract: Posterior sampling for reinforcement learning (PSRL) is one of the simplest and most effective exploration methods, but a basic question has remained op

MinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Perception

arXiv:2609.33804v1 Announce Type: cross Abstract: Modeling spatiotemporal coupling is a key challenge in building physical intelligence across scales, from microscopic to macroscopic. Existing models

MISHAP-Bench: A Hallucination Benchmark for Large Audio-Language Models

arXiv:2609.33893v1 Announce Type: new Abstract: Large audio-language models (LALMs) produce fluent responses about audio but often hallucinate by making plausible yet ungrounded claims. Existing audio

MixBench-TS: A Multivariate Time Series Forecasting Benchmark Where Channel Mixing Pays Off

arXiv:2609.32656v1 Announce Type: cross Abstract: Multivariate Time Series Forecasting (MTSF) models that mix information across channels assume that the past of one channel carries information about

Mixed-Prior Decision Risk for Open-Set Recognition

arXiv:2609.35043v1 Announce Type: new Abstract: In open-set recognition (OSR), a probe must either be identified as one of the known gallery classes or rejected as unknown, so three error types coexis

Applications

MM-ABC: Towards Generalist Mobile Manipulation via Seeing, Coordinating and Imagining

arXiv:2609.35652v1 Announce Type: new Abstract: Mobile manipulation extends robot interaction beyond a fixed kinematic workspace by making the reachable region itself controllable. This flexibility in

applicationsarxiv-cs-ro

MM-OPD: Towards One More Bottleneck Between Perception and Reasoning

arXiv:2609.32690v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) advance visual reasoning by strengthening both perception and reasoning, implicitly assuming a process t

MM-VeriRec: Failure-Guided Fusion for Verifiable Agentic Multimodal Recommendation

model-releasesarxiv-cs-cv

arXiv:2609.31718v1 Announce Type: cross Abstract: Images often carry recommendation constraints that text metadata only hints at. A movie may need to look dark, a product may need a minimal style, and

mmHRI: Towards Privacy-Preserving Human-Robot Interaction with Millimeter-Wave Radar

arXiv:2609.34220v1 Announce Type: cross Abstract: Assistive robots increasingly operate in many human-centered environments and perform various human-robot interaction (HRI) tasks, such as object deli

Model-Agnostic Online Certificate-Driven Calibration for Time Series Forecasting Under Distribution Shift

arXiv:2609.31960v1 Announce Type: new Abstract: Time series out-of-distribution generalization requires forecasters to remain reliable when deployment dynamics differ from training conditions due to c

Model Casting and Low-Parameter Gating: Towards More Sparsely Activated FFNs

arXiv:2609.31975v1 Announce Type: new Abstract: This paper introduces model casting, a mid-training recipe that drastically sparsifies the activations within the Feed-Forward Network (FFN) layer. With

Model-Free Output Feedback Stabilization via Policy Gradient Methods

arXiv:2601.19284v3 Announce Type: replace-cross Abstract: Stabilizing a dynamical system is a fundamental problem that serves as a cornerstone for many complex tasks in the field of control systems. T

safetyarxiv-cs-lg

Model-Informed Safe Reinforcement Learning for Bipedal Locomotion via Step-to-Step Prediction

arXiv:2609.34486v1 Announce Type: new Abstract: Humanoid robots promise versatile mobility in cluttered, human-centric environments, but real deployment demands principled safety. Classical model-base

safetyarxiv-cs-ro

Modeling Whole-Slide Images as Dynamic Tumor Microenvironment Fields

arXiv:2609.34451v1 Announce Type: new Abstract: Due to the gigapixel-scale nature of whole-slide images (WSIs), weakly supervised WSI analysis is commonly formulated as a multiple instance learning (M

Modelling non-linear aeroelastic loads in long-span bridges with extreme learning machines

arXiv:2609.33274v1 Announce Type: cross Abstract: Accurate modelling of aerodynamic loads is essential for predicting instabilities and ensuring the safety of long-span bridges. A methodology is intro

safetyarxiv-cs-lg

Models Designed to Forget: Machine Unlearning via Key Deletion

arXiv:2603.15033v4 Announce Type: replace Abstract: Machine unlearning for vision models is rapidly becoming a practical requirement, driven by privacy regulations, data errors, and the need to remove

Modernising the Compressed-Domain Video Captioner: A Controlled Study of SigLIP2 and GPT-2 Substitutions

arXiv:2609.31700v1 Announce Type: new Abstract: Compressed-domain video captioning avoids full video decoding by operating directly on I-frames, motion vectors and residuals, trading a small amount of

MoGround: Measuring and Mitigating Modality Distraction in Vision-Language Models

arXiv:2609.33431v1 Announce Type: new Abstract: We release MoGround, a vision-language dataset spanning four visual domains in which the answer to every question is guaranteed to be available from exa

Agents

MomWorld: Momentum-Aware Latent World Model for Long-Horizon Autonomous Driving

arXiv:2609.33737v1 Announce Type: new Abstract: Long-horizon planning enables autonomous vehicles to anticipate scene evolution and potential risks, supporting safe and stable decisions in complex int

agentsarxiv-cs-ro

MonoEgo: Monocular Metric Egocentric Demonstration Capture with Passive Wrist Constellations and Sparse Workstation Anchors

arXiv:2609.34512v1 Announce Type: new Abstract: Image-aligned metric demonstrations often require dedicated tracking hardware and synchronization across devices. We present MonoEgo, a capture system t

safetyarxiv-cs-ro

MORPH: Self-Organising Multi-Robot Task Allocation via Neuroplasticity-Inspired Adaptive Topology

model-releasesarxiv-cs-ro

arXiv:2609.32745v1 Announce Type: new Abstract: Multi-robot task allocation (MRTA) in dynamic environments faces a fundamental tension: effective coordination requires learned structure, but that stru

MorphAtt: A Neuromorphic Accelerator for Efficient Multi-Head Attention Processing in Spiking Vision Transformers

arXiv:2609.33207v1 Announce Type: cross Abstract: Spiking Vision Transformers (SViTs) are developed as an energy-efficient alternative to conventional ViTs for computer vision tasks at the edge. Howev

MoSPR: Histology-to-Gene Expression Prediction with Morpho-Spatial Macrostates and Low-Rank Molecular Programs

arXiv:2609.34280v1 Announce Type: cross Abstract: Predicting molecular profiles from histopathology remains challenging because whole-slide images contain spatially organized, heterogeneous tissue pat

Agents

Motion Forcing: A Decoupled Framework for Robust Video Generation in Motion Dynamics

arXiv:2603.10408v2 Announce Type: replace Abstract: The ultimate goal of video generation is to satisfy a fundamental trilemma: achieving high visual quality, maintaining rigorous physical consistency

agentsarxiv-cs-cv

MotionSpaceFlow: Representation-Aware Flow Matching in Direct Motion Space

arXiv:2609.34190v1 Announce Type: new Abstract: Recent advances in diffusion and flow models have substantially improved text-driven human motion generation. Yet most methods generate in low-dimension

Applications

MTLiquid: Enabling Efficient Multi-Task Learning using Liquid Neural Networks for Lightweight Healthcare Monitoring Systems

arXiv:2609.33232v1 Announce Type: new Abstract: Continuous-time sensing and monitoring with timely and accurate decision-making are critical for many real-world applications. In healthcare monitoring

applicationsarxiv-cs-lg

Multi-Aperture PPG with MAPIS: Spatial Optical and Temporal Coherence Fields

arXiv:2609.33790v1 Announce Type: cross Abstract: Conventional reflectance photoplethysmography (PPG) typically reduces tissue optical responses to one or a few detector channels. Matrix Pinhole Image

Multi-Attractor GNNs: Set-Valued Expressivity Beyond Unique Equilibria

arXiv:2609.35274v1 Announce Type: new Abstract: Recurrent and equilibrium graph neural networks (GNNs) often enforce a unique fixed point or use one training target per graph. Yet many combinatorial a

Multi-Bitwidth Quantization for LLMs Using Additive Codebooks

arXiv:2606.12876v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed across heterogeneous hardware with varying resource constraints, the ability to adaptively

Tutorials

Multi-Marginal Inverse Optimal Transport for Contrastive Learning Via Explicit Anchor-Positive-Negative Coupling

arXiv:2609.33741v1 Announce Type: cross Abstract: Inverse Optimal Transport (OT) based methods for representation learning learn representations such that the global OT coupling between a pair of data

tutorialsarxiv-cs-lg

Multi-Modal Non-Prehensile Estimation of Physical Parameters via Press-and-Pull Tipping

arXiv:2609.33138v1 Announce Type: new Abstract: Recovering physical properties of unknown objects through non-prehensile interaction is challenging because no single manipulation primitive reveals all

researcharxiv-cs-ro

Multi-Scale Semantic Mapping in Urban Environments via Observation Calibration and Policy Dependence Regularization

arXiv:2609.34833v1 Announce Type: new Abstract: Semantic mapping is fundamental to embodied navigation, yet existing methods are developed for indoor environments, where objects exhibit relatively lim

Multi-Task Learning of Conditional Mean Operators: applications to dynamical systems and uncertainty quantification

arXiv:2609.35429v1 Announce Type: cross Abstract: Estimating conditional statistics and learning representations of a population of conditional distributions are central problems in many data-driven a

Applications

Multi-Terrain Mastery: A Comprehensive Controller for Bipedal Locomotion

arXiv:2609.33174v1 Announce Type: new Abstract: Advancing bipedal robots to navigate diverse terrains remains a significant challenge in robotics. Traditional locomotion controllers excel on specific

applicationsarxiv-cs-ro

MultiEcho: An Experimental Science of Learned Worlds

arXiv:2609.33347v1 Announce Type: new Abstract: World models can be studied as experimental systems with response laws of their own. We introduce MultiEcho, a framework for estimating these laws throu