Search: “deployment”

PRIME: Frequency-Aware Unified Pruning and Merging for MoE Compression

arXiv:2610.13982v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses sev

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Research

Push the Limit: Learning High-Speed Sprinting and Aggressive Cornering on a Full-Scale Humanoid

DGX agent

arXiv:2610.14840v1 Announce Type: new Abstract: We present a learning-based framework that enables a full-scale bipedal humanoid to sprint at over 7 m/s with motion-prior-guided gait and perform aggre

researcharxiv-cs-ro

14 Oct 2026

RAGE: A Tightly Coupled Radar-Aided Grip Estimator For Autonomous Race Cars

arXiv:2604.02892v2 Announce Type: replace Abstract: Real-time estimation of vehicle-tire-road friction is critical for allowing autonomous race cars to safely and effectively operate at their physical

agentsarxiv-cs-ro

Reinforcement Learning for Large-Scale Lifelong Multi-Agent Path Finding with Shared Spatial Fields

arXiv:2610.18186v1 Announce Type: new Abstract: Large-scale applications of lifelong multi-agent path finding (LMAPF) call for computationally inexpensive movement decisions under changing traffic con

Applications

Robust Detection of Directional Adversarial Attacks in Deep Neural Networks for Radiological Imaging

arXiv:2610.15628v1 Announce Type: new Abstract: Deep learning is now central to radiology, helping detect changes on X-rays, CTs, and MRIs. However, these systems are highly vulnerable to adversarial

applicationsarxiv-cs-cv

RootSLAM: Square Root Bundle Adjustment for Visual-Inertial SLAM

arXiv:2610.15157v1 Announce Type: cross Abstract: Efficient perception is a prerequisite for the small, low-power devices targeted by robotics, mixed reality, autonomous driving, and drone application

Local Ai

Satellite-Free Training for Drone-View Geo-Localization

arXiv:2604.01581v4 Announce Type: replace Abstract: Drone-view geo-localization (DVGL) typically assumes access to satellite imagery during task-specific development. We study a late-arriving-gallery

local-aiarxiv-cs-cv

Scenes as an Attack Surface: Uncovering Scene-Grounded Safety Failures in MLLMs

arXiv:2610.14094v1 Announce Type: new Abstract: Existing jailbreak attacks have exposed serious safety weaknesses in multimodal large language models (MLLMs). Most prior attacks rely on specially cons

safetyarxiv-cs-cv

Sea-Net: A Small and Inherently Interpretable Neural Network for Time Series Classification

arXiv:2610.17939v1 Announce Type: cross Abstract: Traditional time-series classification methods often operate as black boxes, providing a class prediction without indicating which time point or time

researcharxiv-cs-cv

SHOES: Sim-to-real Hardware-adaptation via On-policy Expansion of Skills for Legged Robots

arXiv:2610.16457v1 Announce Type: new Abstract: Humans expand their locomotion capability by wearing appropriate footwear, such as hiking boots for mountains or skates for ice. Footwear changes the gr

SHRUMS: Sensor Hallucination for Real-time Underwater Motion Planning with a Compact 3D Sonar

arXiv:2510.18996v2 Announce Type: replace Abstract: Autonomous navigation in 3D is a fundamental problem for autonomy. Despite major advancements in terrestrial and aerial settings due to improved ran

agentsarxiv-cs-ro

Simple Linear Corrections for Compression in Late Interaction

arXiv:2610.14196v1 Announce Type: cross Abstract: Late-interaction (multi-vector) models represent documents using a set of token-level vectors, providing a middle ground between cross-encoders and de

Local Ai

Simplifying and Improving On-Policy Distillation for Few-Step Diffusion

arXiv:2610.18472v1 Announce Type: new Abstract: Few-step distillation reduces the sampling cost of diffusion and flow models, but existing approaches often require auxiliary score models or specialize

local-aiarxiv-cs-cv

Tutorials

SkipWorld: Latent Skip Planning with Distilled World Models for Efficient Visual Navigation

arXiv:2610.18135v1 Announce Type: new Abstract: World-model-based visual navigation enables action planning by predicting future states under candidate trajectories. However, long-horizon rollout rema

tutorialsarxiv-cs-ro

Applications

StreamingWAM: Streaming World Action Modeling via Chunk-Wise Staircase Flow Matching

arXiv:2610.15585v1 Announce Type: new Abstract: World Action Models (WAMs), often built upon pretrained video-generation world models, have emerged as a promising framework for general-purpose and ope

applicationsarxiv-cs-ro

TableVision: Recovering Verified Structure from Historical Tables

arXiv:2610.17448v1 Announce Type: new Abstract: Gaps, skips, and mistakes are the normal state of a historical register: rows go missing, tables cross page breaks, columns slip under wrong names, and

researcharxiv-cs-cv

TETRIS-4DGS: Efficient Post-Training Compression of 4D Gaussian Splatting

arXiv:2610.14663v1 Announce Type: new Abstract: 4D Gaussian Splatting (4DGS) has emerged as a powerful dynamic scene representation, supporting real-time high quality rendering but suffers from prohib

researcharxiv-cs-cv

Think Before You Speak: Enabling Training-Free Latent Communication Between Language Models

arXiv:2610.16165v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in solving complex tasks in coding, mathematical logic, and iterative reasoning

agentsarxiv-cs-lg

Tikhonov-Stabilized Bezier Representation Forecasting for Training-free Diffusion Acceleration

arXiv:2610.16448v1 Announce Type: new Abstract: Diffusion models, particularly Diffusion Transformers, achieve strong image and video generation quality but remain expensive at inference time due to r

safetyarxiv-cs-cv

Today I'm excited to share that New Material is joining @MistralAI to design sovereign frontier AI. We're establishing a new team, 'Solution…

model-releasesarthur-mensch--x

Today I'm excited to share that New Material is joining @MistralAI to design sovereign frontier AI. We're establishing a new team, 'Solutions Design' within Mistral: working across product, engineerin

Triage-PiEvo: Evidence Admission for Principle-Evolving Scientific Agents

arXiv:2610.14683v1 Announce Type: new Abstract: Large language model (LLM)-based AI Scientists advance scientific discovery, yet they suffer from inefficiencies due to noisy observations. Existing pri

Truncation Samplers Are Nested: Comparing Decoding Rules Is a Measurement Problem

arXiv:2610.15570v1 Announce Type: cross Abstract: Settling one comparison between two truncation samplers has cost six thousand A100-hours, and nothing says in advance how far apart two samplers can b

Trust-Calibrated VLA Policy Post-Training with Evolving Imagination

arXiv:2610.16517v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are often pretrained via imitation learning, making reinforcement learning (RL) post-training important when deplo

UEPS: Robust and Efficient MRI Reconstruction

arXiv:2603.18572v2 Announce Type: replace-cross Abstract: Deep unrolled models (DUM) have become the state-of-the-art for accelerated MRI reconstruction, yet their robustness under domain shift remain

Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective

arXiv:2610.14394v1 Announce Type: cross Abstract: Training large-scale recommender systems on dynamic industrial platforms, such as Short-Form Video (SFV) services with billions of daily interactions,

researcharxiv-cs-lg

UniLAP: A Unified Latent Action Policy for Cross-Embodiment Manipulation

arXiv:2610.16969v1 Announce Type: cross Abstract: Latent Action Models (LAMs) can capture manipulation priors from action-free videos, but discrete latents usually impose a granularity bottleneck, lim

Tutorials

UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models

arXiv:2610.18697v1 Announce Type: cross Abstract: World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively. Although these mode

tutorialsarxiv-cs-cv

Variable Input Masked Autoencoders for Wrist-Worn Accelerometry

arXiv:2610.17383v1 Announce Type: new Abstract: Large-scale repositories of wearable sensor data have enabled the development of foundation models for human activities and behavior. However, existing

What Can Language Models Inherit from Solomonoff Induction? Limits, Attainable Properties, and What Served Conditionals Reveal

arXiv:2610.14007v1 Announce Type: new Abstract: A familiar line of reasoning runs as follows: language models are flexible approximators trained under a strictly proper loss, so scale brings them clos

Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation

model-releasesarxiv-cs-ro

arXiv:2610.14530v1 Announce Type: new Abstract: Multi-robot systems (MRS) offer significant advantages over single-robot platforms by enabling information sharing and coordination for different tasks

David Silver is one of the all-time greats. He was the creator of AlphaGo, which changed the course of history by showing that neural nets c…

David Silver is one of the all-time greats. He was the creator of AlphaGo, which changed the course of history by showing that neural nets could achieve superhuman performance for the first time. He a

safetysonya-huang--x

Robots That Predict the Future: Dyna-2, Million-Hour World Models, & the Bitter Lesson of Robotics @DynaRobotics’s @JasonMa2020 explains how…

Robots That Predict the Future: Dyna-2, Million-Hour World Models, & the Bitter Lesson of Robotics @DynaRobotics’s @JasonMa2020 explains how world-action models predict the future to guide physical ro

agentsswyx--x

Industry

Spranic builds spatial memory for physical AI

Physical AI needs context about its surroundings to adapt beyond a fixed set of instructions. Munich-based Spranic is building a spatial memory layer that records physical spaces and how they change.

industrysiliconangle

Hardware

Today, we’re open sourcing the full NVIDIA Run:ai platform as part of the NVIDIA DSX AI Factory Platform. Developers, platform teams, and pa…

Today, we’re open sourcing the full NVIDIA Run:ai platform as part of the NVIDIA DSX AI Factory Platform. Developers, platform teams, and partners can build on production-grade AI workloads and GPU or

hardwareclem-delangue--x

Where do LLMs actually earn their place in a recommender system? Kim Falk’s new book + 5 ebook giveaway [D]

model-releasesr-machinelearning

Hi r/MachineLearning, Stjepan from Manning here. The mods approved this post, and I appreciate being welcomed back to share another book with you. We’ve just launched AI Recommender Systems: Transform

ATHLETE: Learning Reactive Humanoid Tennis via Trajectory-Guided Motion Matching

arXiv:2610.13572v1 Announce Type: new Abstract: Humanoid tennis requires reactive whole-body coordination under rapidly changing ball dynamics, yet collecting task-specific motion-capture demonstratio

CeDR-Nav: Cross-Embodiment Mapless Navigation via Offline Demonstration Recasting

arXiv:2610.13007v1 Announce Type: new Abstract: Learning-based methods have demonstrated strong performance in mapless robot navigation. However, a learned policy remains restricted to the embodiment

Chronos Policy: Learning Robot Policies on Physical Time

arXiv:2610.13929v1 Announce Type: new Abstract: Robot policies often convert sensing and control into fixed-rate observation-action sequences, leaving physical time implicit in sequence position. This

CopRoute: Dependence-Aware Conformal Routing for Language Models

arXiv:2610.13276v1 Announce Type: new Abstract: Set-based language model routing aims to increase the coverage of the event where at least one model answers a query correctly while controlling the num

CRM: Retrieval Model with Controllable Condition

arXiv:2412.13844v2 Announce Type: replace-cross Abstract: Retrieval models for recommendation need to account for outcomes such as watch time while searching large item corpora efficiently. We present

researcharxiv-cs-ai

Dell and Intel weigh the costs of enterprise AI scalability

Enterprise AI scalability is running into a hard budget wall for some customers. As agentic workloads multiply, cloud token bills are consuming allocations faster than planned, prompting companies to

agentssiliconangle

Local Ai

DeltaLLM: Delta-Key Sparse Attention for Edge LLMs

arXiv:2507.19608v2 Announce Type: replace Abstract: On-device LLM inference is increasingly important for edge platforms but remains challenging due to high computational and memory demands and tight

local-aiarxiv-cs-ai

DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models

arXiv:2610.12758v1 Announce Type: new Abstract: Language models have been adopted faster than evaluation practices that reliably assess their risks, and a growing share of use is affective rather than

safetyarxiv-cs-cl

DuoRate: Two-Rate Per-Expert Precision Allocation for Mixture-of-Experts Serving

arXiv:2610.13328v1 Announce Type: new Abstract: Serving a Mixture-of-Experts (MoE) model pays for expert precision twice: the resident footprint pays for every expert the router can reach, while each

EvoBelief: Falsifiable World Models for Self-Improving LLM Agents

model-releasesarxiv-cs-ai

arXiv:2610.12789v1 Announce Type: new Abstract: Self-evolving LLM agents typically improve how they act through accumulated experience, reflection, or skills. Yet even procedurally capable agents can

Gamma: Grounded Multi-Agent Memory with Mechanical Audit for Memory-Intensive Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.12793v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) policies fail on memory tasks, behaviour that depends on events observed long before the current frame, because

GlucoPRISM: Protocol-Supervised Pretraining for Cross-Cohort Glucose Transfer

arXiv:2610.12528v1 Announce Type: new Abstract: Continuous glucose monitoring (CGM) foundation models encode each day as one vector that mixes who the person is, what happened that day and how the sen

researcharxiv-cs-lg

Hardware

How Much Computation Does Every Forecast Need? Structured Block Skipping in Two Time-Series Foundation Models

arXiv:2610.13208v1 Announce Type: new Abstract: Time-series foundation models use the same network depth for every input, although forecasting windows differ in structure and difficulty. We test wheth

hardwarearxiv-cs-lg