Search: “deployment”

MoE-ACT: Scaling Multi-Task Bimanual Manipulation with Sparse Task-Conditioned Mixture-of-Experts Transformers

arXiv:2603.15265v2 Announce Type: replace Abstract: Developing a unified policy for multi-task robotic manipulation remains challenging due to policy degradation from task interference and negative tr

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Model Releases

Monitorable Chart Reasoning Agents via Verifiable Process Rewards

DGX agent

arXiv:2609.24071v1 Announce Type: new Abstract: Chart reasoning agents are increasingly used to extract actionable insights in critical domains, achieving state-of-the-art performance on multiple benc

model-releasesarxiv-cs-cv

22 Sept 2026

Monocultural Biases: Correlated biases in large language models lead to unequal systemic exclusion rates in hiring

arXiv:2609.22169v1 Announce Type: new Abstract: Employers are increasingly using large language models (LLMs) to automate their hiring process. This paper investigates the risk of monocultural biases,

researcharxiv-cs-cl

Agents

Offline Reinforcement Learning for Distribution-Grid Protection

arXiv:2609.24703v1 Announce Type: cross Abstract: Data-driven protection may complement conventional relays in distribution grids whose operating conditions vary with distributed generation, switching

agentsarxiv-cs-lg

On the Efficiency-Safety Dilemma in Large Reasoning Models

arXiv:2609.23587v1 Announce Type: new Abstract: Large reasoning models (LRMs) incur high inference costs, often mitigated by efficiency techniques like quantization and pruning. However, the impact of

safetyarxiv-cs-cl

One to More, More to One: Category-Aware Iterative Expert Training for Software Engineering Agents

arXiv:2609.23377v1 Announce Type: cross Abstract: Repository-level software engineering (SWE) comprises heterogeneous task categories, whose progress under pooled agentic reinforcement learning can be

safetyarxiv-cs-cl

ORDER: A Fictitious-World Benchmark for Domain-Adaptive Embodied AI

arXiv:2609.22285v1 Announce Type: new Abstract: Adapting language models to new domains via continual pre-training raises a basic evaluation problem: if the training corpus overlaps with what the mode

Orientation-Aware Control and Trajectory Design for Aerial RIS-Assisted Wireless Communications

arXiv:2609.23157v1 Announce Type: cross Abstract: Aerial Reconfigurable Intelligent Surfaces (ARISs) can enhance wireless connectivity in obstructed environments by combining reconfigurable metasurfac

researcharxiv-cs-ro

Perplexity Cost Understates What Activation Quantisation Breaks

arXiv:2609.23125v1 Announce Type: new Abstract: Activation quantisation is usually evaluated with an aggregate metric, perplexity, averaged over every token a model predicts. We ask whether that avera

Pharmacokinetic State Space Models for Unbiased Prediction of Haemodynamic Collapse

arXiv:2609.24338v1 Announce Type: new Abstract: An Intraoperative Hypotension (IOH) event is a frequent complication during administration of general anaesthesia with serious downstream consequences,

local-aiarxiv-cs-lg

PileBelief: Persistent Physical State for Interaction-Driven World Modeling

arXiv:2609.22858v1 Announce Type: new Abstract: World models allow robots to anticipate action consequences before execution. This capability is especially valuable in excavation, where each scoop res

local-aiarxiv-cs-ro

PredActor: Predictive Action Diffusion for Steerable Onboard Humanoid Control

arXiv:2609.24840v1 Announce Type: cross Abstract: Diffusion models offer flexible motion generation, but translating this flexibility into feedback-responsive humanoid control remains challenging. Hie

Predicting Out-of-Distribution Generalization of Neural Operators via Observable Spectral Error Decomposition

arXiv:2609.23529v1 Announce Type: new Abstract: Neural operators have emerged as powerful surrogates for solving partial differential equations (PDEs), yet their reliability under distribution shift r

researcharxiv-cs-lg

Predictors and Orchestrators: Parsimonious Machine Learning within an Agentic AI Harness for Multi-Horizon Karst Aquifer Forecasting

arXiv:2609.22251v1 Announce Type: new Abstract: Forecasting karst aquifer dynamics is difficult because recharge responses are nonlinear, event-driven, and governed by strongly heterogeneous flow path

Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models

model-releasesarxiv-cs-lg

arXiv:2609.24422v1 Announce Type: new Abstract: Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian i

Probabilistic Scene Graphs: Hierarchical Representation and Real-time System

arXiv:2609.23144v1 Announce Type: new Abstract: 3D scene graphs provide semantically rich and hierarchical representations for robot perception. However, existing systems do not maintain uncertainty a

Applications

RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale

arXiv:2609.24464v1 Announce Type: new Abstract: Using a Large Language Model (LLM) as the clusterer at production scale is hard: prompts cannot hold the entire label space, and per-document serial pro

applicationsarxiv-cs-lg

Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network

arXiv:2607.14293v2 Announce Type: replace-cross Abstract: Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge ju

researcharxiv-cs-lg

Real-Time Plasma State Prediction via FPGA-Accelerated Quantized Recurrent Probabilistic Neural Networks

arXiv:2609.23141v1 Announce Type: cross Abstract: Real time plasma state estimation for control of Tokamak devices are challenging due to the stringent latency requirements of the plasma control syste

researcharxiv-cs-lg

Rebekah 1.8b now available

🚀 THE EDGE AI FAMILY HAS ARRIVED. Today we’re opening the doors to a new generation of open-source and open-weight edge AI models from Next GenAi. Meet Rebekah AI 1.8B. 🧠⚡ Small enough to run at the e

local-air-ollama

Applications

Re:CAP - Auditing Retrieval Coverage in Production RAG Pipelines

arXiv:2609.24122v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is hard to monitor in production: exhaustive relevance labels do not exist for non-stationary multi-million-passage

applicationsarxiv-cs-cl

Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents

model-releasesarxiv-cs-cl

arXiv:2601.15322v3 Announce Type: replace-cross Abstract: Tool-using agents can repeat a final decision while changing their recorded execution. We introduce the Determinism-Faithfulness Assurance Har

RiverVLN: Phase-Grounded Temporal Vision--Language Navigation for Unmanned Surface Vehicles

arXiv:2609.23423v1 Announce Type: new Abstract: Vision-language navigation (VLN) has largely been developed for indoor and terrestrial robots, where language can often be treated as a static goal and

RoboTalk: Learning Multi-Robot Communication and Coordination from Multimodal Demonstrations

arXiv:2609.23997v1 Announce Type: new Abstract: Multi-robot collaboration could enable more efficient and scalable solutions to complex robotic tasks, but collaboration under partial observability rem

local-aiarxiv-cs-ro

RopeFormer: Cross-Trial Adaptation from Interaction History for Dynamic Rope Manipulation

arXiv:2609.23432v1 Announce Type: new Abstract: Dynamic rope manipulation is highly sensitive to unknown object dynamics: the same robot motion can produce substantially different responses across rop

RSPDBench: Benchmarking Vision Foundation Models on Earth Observation Tasks Under Physically Grounded Remote-Sensing Product Degradations

arXiv:2609.23427v1 Announce Type: new Abstract: Vision foundation models targeting Earth observation (EO) tasks are commonly evaluated on clean downstream benchmarks, but operational EO products can a

safetyarxiv-cs-cv

SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2508.03351v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse language tasks, motivating their extension to vision-lan

Applications

ScaleBlind: Point Cloud Completion under Unknown Scale

arXiv:2609.23404v1 Announce Type: new Abstract: Point cloud completion aims to infer a complete 3D shape from a partial point cloud and serves as a fundamental building block for downstream tasks such

applicationsarxiv-cs-cv

SCULPT-VLA: Learning Structured Control through Staged Action Grounding

arXiv:2609.23275v1 Announce Type: new Abstract: Vision-language-action (VLA) policies increasingly incorporate structured intermediate supervision beyond action labels. Yet specifying what an intermed

SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstruction

arXiv:2609.24864v1 Announce Type: new Abstract: Reaching a 6-DoF grasp pose in clutter requires a collision-free trajectory, conventionally obtained by reconstructing the scene in 3D and planning insi

researcharxiv-cs-ro

Agents

Semantics Delivery Network: Rethinking Web Retrieval Infrastructure for LLM Agents

arXiv:2609.22486v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on external sources when answering questions that require proprietary information or up-to-date live we

agentsarxiv-cs-lg

ServerlessLoRA: Enabling Low-Latency Serverless Multi-LoRA Serving

arXiv:2505.14468v2 Announce Type: replace Abstract: Multi-LoRA (Low-Rank Adaptation) serving allows many specialized LLM variants to share the same base model by attaching lightweight adapters. This m

local-aiarxiv-cs-lg

SHAFT: A Slack-Compensating, Helical-Buckling-Attenuating Flexible-Shaft Transmission for Lightweight Multi-DoF Manipulation

arXiv:2609.22677v1 Announce Type: new Abstract: Lightweight and slim manipulators enable safe operation in human living environments. Proximal actuation using remote transmission mechanisms, such as w

researcharxiv-cs-ro

SPHQuant: Efficient extreme low bit weight quantization for Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2609.24875v1 Announce Type: new Abstract: Recent foundation models are moving toward native multimodal Vision-Language Models (VLMs), making VLMs a central form of next-generation foundation mod

STA-TFM: Spatio-Temporal Aggregation Across Views TransForMer for Pose Estimation

model-releasesarxiv-cs-cv

arXiv:2609.24482v1 Announce Type: new Abstract: Monocular 3D human pose estimation (HPE) remains challenging due to depth ambiguity, occlu- sions, and the need for temporal consistency. While multi-vi

STRIDER: Stepping-Enabled Multi-Gait Hierarchical 3D Loco-Manipulation Framework for Humanoid Robots

arXiv:2609.23483v1 Announce Type: new Abstract: Humanoid loco-manipulation faces two prominent limitations: controllers using continuous velocity commands cannot precisely regulate individual foothold

Task-Aware Hybrid QUBO Optimization for Structured Neural Network Pruning

arXiv:2609.22238v1 Announce Type: cross Abstract: Neural network pruning can be formulated as a combinatorial optimization problem, yet many existing approaches rely on independent filter-importance s

researcharxiv-cs-cv

Industry

The first model of Laya was created with a single over night training and fine-tuning. It only took 15 hours from training to push the model…

First model of Laya was produced with a single overnight training and subsequent fine‑tuning run, taking only 15 hours from the start of training to publishing the model. It was released on Hugging Fa

industryclem-delangue--x

Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies

arXiv:2609.24682v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models map observations to actions with no objective that accounts for how the world responds, so their robustness is bou

safetyarxiv-cs-cv

Time-Incremental Continued Pretraining of LLMs: Knowledge Updates Without Catastrophic Forgetting

model-releasesarxiv-cs-cl

arXiv:2609.23916v1 Announce Type: new Abstract: Large language models (LLMs) drift out of date the moment their pretraining ends, yet retraining from scratch is prohibitively expensive. Continued pret

Hardware

Topology-Aware Workload Scheduling with NVIDIA Topograph

**Summary:** NVIDIA Topograph discovers and normalizes cluster topology from cloud APIs or on‑prem fabric systems into a common model that is published as Kubernetes node labels, Slurm configuration,

hardwarenvidia-developer

Toward Personalized Sleep Guidance from Wearable Data Using Language Models

arXiv:2609.22463v1 Announce Type: new Abstract: Sleep monitoring using wearable data has shown promise for personal health, yet large language model (LLM)-based summarization and question answering re

local-aiarxiv-cs-cl

Triggers and Diagnostics for LLM-Based Interpretability Failures in Active Inference Agents

model-releasesarxiv-cs-lg

arXiv:2609.23215v1 Announce Type: new Abstract: LLM explainers are increasingly attached to autonomous agents as runtime oversight, with operators reading a generated account of the agent's beliefs an

Vision Transformers versus convolutional neural networks for fine-grained orchid genus identification in a species-rich, data-poor flora: a controlled benchmark on the Orchidaceae of New Guinea

model-releasesarxiv-cs-cv

arXiv:2609.24064v1 Announce Type: new Abstract: New Guinea is the world's richest island flora (~2,856 orchid species), yet most species are represented by only a handful of photographs, far fewer tha

Visuomotor Robotic Pruning in Planar Orchards Using Hybrid Reinforcement Learning

arXiv:2609.24906v1 Announce Type: new Abstract: Dormant tree pruning is labor-intensive yet essential for maintaining modern high-productivity fruit orchards. In this work, we focus on pruning of mode

VPRune: Efficient Training-free Pre-LLM Visual Token Pruning

arXiv:2609.24485v1 Announce Type: new Abstract: Visual token pruning is a promising approach to reducing the inference cost of large vision-language models (LVLMs), yet aggressive token reduction ofte

safetyarxiv-cs-cv

What Can a Recurrent State Safely Forget?

arXiv:2609.23366v1 Announce Type: new Abstract: Recurrent models must preserve information that changes future behavior while suppressing hidden-state error. These objectives conflict: contraction imp

When Does Learning Beat Heuristics? A Case Study in Kubernetes Scheduler Score Plugins

model-releasesarxiv-cs-lg

arXiv:2609.22142v1 Announce Type: cross Abstract: Kubernetes scheduler plugins that score candidate nodes are, in production, hand-tuned heuristics. We ask whether a learned scoring function - trained