Search: “models”

RLVR^{2}: Reinforcement Learning with Verifiable Rubric-based Ranking

arXiv:2609.23457v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is expanding from tasks with well-defined correctness signals, such as mathematics and code, towar

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Safety-Critical Control under Uncertainty via Adaptive Conformal Quantile Prediction Intervals

DGX agent

arXiv:2609.23280v1 Announce Type: new Abstract: Safety-critical control under uncertainty requires uncertainty representations that are both statistically valid (for certifiable performance) and compa

safetyarxiv-cs-ro

22 Sept 2026

SAIL: Test-Time Scaling for In-Context Imitation Learning with VLM

arXiv:2603.08269v2 Announce Type: replace Abstract: In-context imitation learning allows robots to acquire skills from demonstrations, yet one-shot trajectory generation remains fragile under environm

applicationsarxiv-cs-ro

SALSA: Semi-Autonomous Literature Summarization Assistant

arXiv:2609.22210v1 Announce Type: new Abstract: SALSA (Semi-Autonomous Literature Summarization Assistant) is an open- source, human-in-the-loop platform for extracting structured scientific datasets

agentsarxiv-cs-cl

SatOV: Restoring Spatial Priors for Training-Free Open-Vocabulary Segmentation in Remote Sensing Imagery

arXiv:2609.22834v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVS) of remote sensing imagery is a challenging pixel-level task requiring strong generalization and adaptation t

Scenario MPC with STL Specifications and Pareto-Based Feasibility Repair

arXiv:2609.23263v1 Announce Type: new Abstract: Temporal logic is a formal language for reasoning about system behaviors over time. Signal temporal logic (STL), in particular, has been used to encode

SCRAMPPI: Contingency-Constrained Planning using Hamilton-Jacobi Reachability

arXiv:2603.26995v2 Announce Type: replace Abstract: Autonomous robots commonly aim to achieve a nominal objective while minimizing a cost. Without contingency preparation, this can leave them vulnerab

See Selectively, Act Adaptively: Dual-Level Structural Decomposition for Bimanual Robot Manipulation

arXiv:2606.13279v2 Announce Type: replace Abstract: In bimanual robotic manipulation, task-relevant visual information varies with the task stage and context, while the interaction of the two arms shi

safetyarxiv-cs-ro

SegTSim: A Big Data Driven Segmented Temporal Simulation Framework for Heterogeneous Multivariate Systems

arXiv:2609.22192v1 Announce Type: new Abstract: Heterogeneous multivariate time-series systems exhibit segment-specific nonlinear dynamics that challenge monolithic forecasting architectures. We propo

applicationsarxiv-cs-lg

SomaNet: Weakly Supervised Learning for Instance Soma Segmentation in 3D Electron Microscopy with Partial Annotations

arXiv:2609.23019v1 Announce Type: cross Abstract: Soma instance segmentation, i.e., identifying and delineating individual cell somas as distinct instances, is crucial for cellular analysis and connec

Sparse Regression Distilled from a Single Robust Fit

arXiv:2609.23937v1 Announce Type: cross Abstract: Robust linear fits can resist response contamination yet remain too dense or unstable for useful global explanations. We propose penalized distillatio

SSP-GNN: Learning to Track via Bilevel Optimization

arXiv:2407.04308v4 Announce Type: replace Abstract: We propose a graph-based tracking formulation for multi-object tracking (MOT) where target detections contain kinematic information and re-identific

Stagewise Anomaly Detection for E-Transaxle Quality Monitoring Using Wavelet and STFT Features

arXiv:2609.22172v1 Announce Type: cross Abstract: This paper presents two interpretable machine-learning frameworks for quality screening of e-transaxle assemblies in electric vehicles: a Stagewise Wa

applicationsarxiv-cs-lg

Statistical Convergence of Transformer Encoder-Accelerated Robust Reinforcement Learning

arXiv:2609.23775v1 Announce Type: new Abstract: Obtaining the optimal action-value function in Markov decision processes is computationally intensive in large state--action spaces. In this study, we p

Stealing profits: Spread-based temporal hierarchy forecasting for day-ahead electricity markets

arXiv:2609.23223v1 Announce Type: cross Abstract: Day-ahead electricity price forecasts support trading and storage decisions, but for battery arbitrage predicting intraday price spreads is more relev

Local Ai

Steerable and Reactive Grasping Through Modular Design with a Three-Point Interface

arXiv:2609.24896v1 Announce Type: new Abstract: Dexterous grasping requires deciding where to grasp, reaching the target, and maintaining stable contact. We connect these stages through a compact thre

local-aiarxiv-cs-ro

Structured World-State Reasoning for Agentic Robotic Search

arXiv:2609.23841v1 Announce Type: new Abstract: Long-horizon robotic search must resolve natural language against heterogeneous, incomplete, and often ambiguous evidence: textual information, prior ma

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

Task-Oriented Co-Design and Optimization of Geared Actuators for Robotic Applications

arXiv:2609.22795v1 Announce Type: new Abstract: Different tasks performed by legged robots impose distinct torque and speed requirements on actuators. Existing robotic actuators are generally optimize

researcharxiv-cs-ro

The Neural Forcing for Three-Dimensional Incompressible Navier-Stokes finite time blowup

arXiv:2609.23934v1 Announce Type: new Abstract: We present a two-part neural framework for forced three-dimensional incompressible Navier--Stokes flow. Part~I develops the computational forcing system

The Operational Value of Spatial Dependence in Renewable Forecast Scenarios for Single-Period Economic Dispatch: A Controlled Ablation Study

arXiv:2609.23988v1 Announce Type: cross Abstract: Renewable forecasts are evaluated by statistical skill (e.g., CRPS), but grid operators pay for realized dispatch cost. We diagnose what drives dispat

Tutorials

Topology-Informed Visual Prompting For Vision Language Action Policies

arXiv:2609.23944v1 Announce Type: new Abstract: Vision-language-action (VLA) policies can struggle with manipulation tasks with complex obstacle geometries due to partial observability. These complex

tutorialsarxiv-cs-ro

Tracker-Free Robotic Ultrasound Calibration with a Spherical-Marker Phantom and Threshold-Free Center Localization

arXiv:2609.22483v1 Announce Type: new Abstract: Robotic ultrasound (US) calibration is essential for accurately relating US images to the robot coordinate system, but accurate and automated calibratio

researcharxiv-cs-ro

Transferring the Intelligence of VLMs to Robotic Control

arXiv:2609.22966v1 Announce Type: new Abstract: Humans can seamlessly adapt to both physical and digital worlds, suggesting that while a digital-to-real gap exists in embodiment, environment and task,

TReViS: Temporal Repetition Structure Aware Video Synthesis for Self-supervised Repetitive Action Counting

arXiv:2609.24367v1 Announce Type: new Abstract: Fully supervised repetitive action counting (RAC) has achieved strong performance, but requires dense temporal annotations that are costly and difficult

Uncertainty-Weighted Fusion of Image and Synthetic Event for Video Anomaly Detection

arXiv:2505.02393v3 Announce Type: replace Abstract: Most existing video anomaly detectors rely on RGB frames alone, which limit their ability to capture abrupt or transient motion cues that are critic

applicationsarxiv-cs-cv

Unified Learning of Temporal Task Structure and Action Timing for Bimanual Robot Manipulation

arXiv:2603.06538v2 Announce Type: replace Abstract: Bimanual manipulation requires both temporal task structure - which actions precede or overlap others - and concrete timing - when each action start

researcharxiv-cs-ro

Using Composition Operators to Linearize LLM Semantic Transformations

arXiv:2609.22143v1 Announce Type: new Abstract: Machine learning learns functions: prompt to response, image to caption. What these functions are mathematically remains hard to say. We present a metho

researcharxiv-cs-cl

VDGS: Visibility-Driven Large-Scale 3D Gaussian Splatting for Aerial Scene Reconstruction

arXiv:2609.23049v1 Announce Type: new Abstract: Large-scale scene reconstruction is a critical foundational technology in robotic autonomous systems such as 3D mapping and autonomous driving. In recen

agentsarxiv-cs-cv

When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Reads

arXiv:2609.22299v1 Announce Type: cross Abstract: When a robot faces unfamiliar physical conditions, a common approach is to collect evidence about what changed and adapt. For such diagnosis to improv

When is a closed-form RGB->S/P ratio adequate? A hyperspectral characterization on natural scenes for mesopic display

arXiv:2609.24819v1 Announce Type: cross Abstract: Mesopic and low-light display transforms require, as their driving signal, a per-pixel scotopic-to-photopic luminance ratio (S/P); the exact spectral

Which Terrain Is Better? Preference Learning with VLM Prototypes for Off-Road Traversability Ranking

arXiv:2609.23673v1 Announce Type: new Abstract: In vision-based off-road navigation, a robot needs to know not only which obstacles to avoid but also which terrain is better. The first is handled by f

You Can Tell Who's Asking: What the Web's Questions Are Made Of, and Where They Come From

arXiv:2609.24106v1 Announce Type: new Abstract: Questions scraped from the web are used across academia and industry as a proxy for what people want to know. Across QA training data, retrieval benchma

researcharxiv-cs-cl

A Fully Differentiable Neuro-Soft-Symbolic Framework for Perceptual Task Planning

arXiv:2609.21221v1 Announce Type: new Abstract: Perceptual planning tasks require two key capabilities: accurately perceiving uncertain scenes and planning valid action sequences following logical rul

researcharxiv-cs-ai

A Lightweight Plug-in Gate for Transformer-Based Time-Series Forecasters

arXiv:2609.21044v1 Announce Type: new Abstract: Covariate-rich time-series forecasting requires deciding how external variables enter the target forecasting path. Existing Transformer-based forecaster

applicationsarxiv-cs-lg

AcousticDiffusion: Semantically Conditioned Audio-Guided Diffusion Policy for Search-and-Rescue Assistance

arXiv:2609.21792v1 Announce Type: new Abstract: Navigating toward human callers is an important capability for rescue robots operating where visual contact is degraded or occluded. We present Acoustic

safetyarxiv-cs-ro

Advancing Minimally Invasive Precision Surgery in Large Open Cavities with Robotic Flexible Endoscopy

arXiv:2511.14458v2 Announce Type: replace Abstract: Flexible robots hold great promise for enhancing minimally invasive surgery (MIS) by providing superior dexterity, precise control, and safe tissue

An Introduction to Compression-Based Machine Learning

arXiv:2609.21309v1 Announce Type: new Abstract: Any lossless compression algorithm (like gzip) may be converted into a machine learning method, via either Normalized Compression Distance or the Minimu

Available Guardrails: Certifying Selective Prediction across ML Systems

arXiv:2609.22048v1 Announce Type: new Abstract: A selective predictor acts as a safety gate: it returns an output only when the prediction appears sufficiently trustworthy. Deployments increasingly re

Balanced Prompt Adaptation against Entropy-Induced Collapse for Test-Time Binary Segmentation

arXiv:2609.21743v1 Announce Type: cross Abstract: Entropy minimization is a standard objective for test-time adaptation (TTA), but it can fail in imbalanced binary segmentation. Unlike image classific

researcharxiv-cs-ai

Bayesian classification of astronomical spectra with class uncertainties

arXiv:2609.21694v1 Announce Type: cross Abstract: Context: We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spec

Beyond WER: Entity and Disfluency Recall in Accented Conversational ASR

arXiv:2609.20828v1 Announce Type: new Abstract: ASR systems optimised for Word Error Rate (WER) often miss named entities and filled pauses in accented conversational English, both critical for langua

researcharxiv-cs-cl

Hardware

Bio-MF: Low-Latency and High-Fidelity EEG-to-fNIRS Cross-Modal Generation for Hybrid Motor-Imagery Brain--Computer Interfaces

arXiv:2609.20904v1 Announce Type: cross Abstract: Hybrid motor-imagery brain-computer interfaces (MI-BCIs) combining EEG and fNIRS can outperform EEG-only systems by exploiting complementary electroph

hardwarearxiv-cs-ai

Chronosphere: Space-Time Tessellation of Local Climate Experts

arXiv:2609.21872v1 Announce Type: new Abstract: We introduce Chronosphere, a spatio-temporal neural field that learns representations of climate. A central challenge in geographic representation learn

CoLearn: An Agentic Tutor that Learns its Learner in a Human--AI Co-Learning Loop

arXiv:2609.21154v1 Announce Type: new Abstract: Good tutoring adapts to the individual: it tracks what a learner knows, notices why they go wrong, and asks the next question that will help most. Most

agentsarxiv-cs-cl

Combining Object Detection with Geometry-Aware Clustering to Distinguish Overlapping Plants in UAV Imagery

arXiv:2609.21304v1 Announce Type: new Abstract: Reliable plant-level information from unmanned aerial vehicle (UAV) imagery is important for automated crop monitoring. However, in dense crop canopies,

Continuous Delayed-Memory Stochastic Gradient Descent and Continuous-Time Reinforcement Learning from History of Astrophysical Time Series Studies

arXiv:2609.20906v1 Announce Type: new Abstract: Quasars are luminous objects in the universe that exhibit stochastic brightness variations encoding information about the supermassive black holes power

Demonstration Synthesis from a Single Scan via Gaussian Splatting for Visuomotor Policy Learning

arXiv:2609.21112v1 Announce Type: new Abstract: Training a visuomotor policy calls for abundant demonstrations that closely match the target environment, yet collecting them anew remains expensive. Ex

safetyarxiv-cs-ro