Which exploration policy finds robustnesscliffs fastest under fixed budgets? We ablate –focus∈ {boundary,runtime,robustness, balanced} atop ouragentic sweep framework [1], measuring (i) time-to-first-cliff,(ii) area-under-uncertainty, and (iii) policy ranking under fixedwall-clock and evaluation budgets. Results show that… Balanced vs Boundary-Seeking Policies for Robustness Discovery

Week-long exhaustive RF campaigns are costly andslow to iterate. We show that agentic sweeps can synthesizea one-hour acceptance test that statistically approximates theconclusions of a week-long grid, with explicit confidence tracking.Building on probabilistic boundary discovery… Minimal-Data Validation for Fielded RF Systems

Operational RF systems require quantifiable latency guarantees. We derive SLA envelopes—p50/p99 latencybounds—as functions of operating conditions (e.g., SNR, ∆f,Q), estimated via active sampling over a drift-free parallelscheduler. Building on agentic boundary discovery [?], cost-awareghost analysis… Toward Real-Time SLAs: Bounding p50/p99 Latency Under SNR Regimes

RF integrators need actionable envelopes—“greenzones” and “red lines”—for configuration choices that balancerobustness, latency, and false-alarm cost. Building on probabilistic agentic sweeps [1], ghost-mode cost analysis [2], andscheduling for drift-free throughput [3], we derive practitionerdesign rules… Design Rules for ∆f, AM Depth, FM Deviation, and Q: A Practitioner’s Map

Benchmarking RF demodulation pipelines is oftenbottlenecked by slow simulation loops. While multi-threadedexecution can reduce wall-clock time, naive scheduling maybias robustness estimates if seeds are not managed consistently. We demonstrate a scalable scheduling harness thatexploits CPU… Scheduling and Parallelism in RF Benchmarks: Throughput Without Drift

False positives—or “ghost modes”—in RF demodulation pipelines inflate operational costs when systems respondto spurious detections. We quantify the economic and latencyimpact of ghost hits using anomaly detection modules from theSignalIntelligenceSystem. Iso-ghost contours illustrateregions of high… Ghost Modes and the Cost of Over-Recovery in RF Demodulation

Ghost modes—false positives produced by RF demodulators—carry an operational cost.They waste computational resources, trigger spurious alarms and can mask true signals. Inreal-time systems, where performance criteria must be met every cycle[2], ghost hits competewith latency… CODEX SIGNALYSIS: Ghost Modes and the Cost of Over-Recovery in RF Demodulation

Exhaustive parameter sweeps are the de facto method for benchmarking RF pipelines, butthey scale poorly with dimensionality[2]. For example, a 10-parameter grid with 10 points eachrequires 1010 evaluations. Active learning promises to achieve comparable confidence… Active Learning for Synthetic RF Benches: From Random Grids to Agentic Sweeps

Accurately characterising the performance of black-box RF demodulation pipelines normallyrequires dense sweeps over many parameters, incurring high computational cost[1]. Gaussianprocess (GP) surrogate models provide smooth interpolants and predictive uncertainty, enablingefficient exploration[1]. This paper investigates how… Few-Shot Characterisation of Black-Box RF Fitting with Gaussian Processes

Real-time RF inference systems must meet strict latency budgets to be considered operational [2]. Latency is not uniform across the multidimensional parameter space: frequencyoffsets, signal-to-noise ratios and other demodulator settings can trigger sudden spikes in… Runtime Topology of RF Inference: Contours, Spikes and Safe Operating Areas

In RF demodulation pipelines, system failure is often characterised by the emergence ofghost modes and a collapse of true detections. These “failure rims” occupy narrow regionsof a high-dimensional parameter space spanned by signal-to-noise ratio (SNR),… Where It Breaks: Cartography of Failure Boundaries in SNR–∆f–Q Space

Robust radio-frequency (RF) pipelines must balance conflicting objectives: accurate demodulation of signals and low latency. Improving accuracy (true hit rate) often requires operatingin regimes with high signal-to-noise ratio (SNR) and small frequency offset, which may… Multi-Objective Optimisation of RF Pipelines: Robustness vs. Latency

Exploring the configuration space of radio frequency (RF) demodulation pipelines to find robust operating regimesis challenging because simulation runs are computationally expensive and the response surface can contain sharpfailure boundaries. A naïve uniform grid of… Probabilistic Agentic Sweeps for RF Mode Recovery

RF-augmented reality (RF-AR) wearables enabledetection of threats, casualties and anomalies, but they rely ona set of alert thresholds tailored to mission context and userphysiology. Presently, these thresholds are either hard codedor tuned manually, limiting adaptability… Federated Adaptation: Personalising RF Thresholds without Centralising Raw Data

Augmented reality (AR) headsets promise to improve situational awareness for medics, firefighters and securitypersonnel by overlaying live information on the physical world.However, existing AR user interfaces struggle when many targets must be tracked simultaneously: cluttered… Privacy/Policy: On-Device Filtering & Role-Based Redaction for Wearable Situational Awareness

OpenBench-AR: Reproducible Benchmarks for RF-to-AR Systems” Overall Assessment: Aimed at a workshop like MLSys Artifact Evaluation or ReproNLP) proposes OpenBench-AR, an open-source suite for standardizing evaluations in RF-to-AR systems (e.g., Wi-Fi/UWB sensing integrated with AR… OpenBench-AR

Augmented reality (AR) headsets promise to improve situational awareness for medics, firefighters and securitypersonnel by overlaying live information on the physical world.However, existing AR user interfaces struggle when many targets must be tracked simultaneously: cluttered… Glass UX for Multi-Target Tracking

Critical alerting in wearable AR hinges on correctlyclassifying moving objects as benign or threatening. RF sensorscan track motion of drones, vehicles and personnel, but motionalone suffers from false critical alerts when trajectories deviatefrom simple heuristics.… Threat-Layer Fusion Combining RF Motion and Intent for Wearable Overlays

Augmented reality (AR) systems for situationalawareness (SA) depend on low-latency networking to deliver alerts, map overlays and sensor feeds. However, contested environments exhibit intermittent connectivity and highpacket loss due to jamming, interference and terrain occlusions.… Network-Degraded Ops: DTN-Ready Augmented Reality Situational Awareness

Mass casualty incidents (MCIs) impose extremecognitive and perceptual demands on first responders. Traditional START triage requires personnel to recall a sequence ofchecks while managing multiple casualties, leading to error ratesbetween 20 % and 55 %… Triage-AR Human-in-the-Loop Casualty Visualization for Augmented Reality

Locating trapped or unconscious victims under rubble, behind walls or in smoky environments is a critical challengefor first responders. While trained dogs (K9) excel at searchtasks, they are expensive and have limited availability. Recentadvances in… RF Biomarker Sensing on Commodity Phones: Toward K9 Replacement via Wi-Fi, BLE and UWB Fusion

Augmented reality (AR) and radio-frequency (RF)sensing pipelines demand high frame rates and continuousinference on mobile hardware. These workloads saturate theCPU/GPU, causing excessive heat, elevated power draw andeventual thermal throttling, which in turn collapse situationalawareness (SA)… ThermoGuard: Power–Thermal Control for AR RF Operations

Head-mounted augmented-reality (AR) devices areincreasingly used by first responders and military medics tovisualize radio-frequency (RF) tracks, casualty vitals and threatsignatures in real time. These platforms operate under severeresource constraints: the computational budget is on the… On-Device RF Filtering & Compression for Wearables

GlassBus: Revolutionary Alert System for Tactical Augmented Reality Texas City, Texas – September 19, 2025 – In a breakthrough for first responders and tactical operators, Benjamin J. Gilbert from Spectrcyde RF Quantum SCYTHE at College… Broker Wars: Alert‑Centric Pub/Sub for Tactical AR

RF Tracking: A Graph-Based Breakthrough in AoA-Only Trajectory Recovery Hey tech enthusiasts and signal processing geeks! Today, I’m diving into a fascinating new paper that’s pushing the boundaries of passive RF geolocation. Titled “RF Sequence… RF Sequence Recovery with Graph-Based Inference: An AoA-Only Approach

Unveiling the Ghost Intelligence System: The Future of Real-Time RF Threat Detection In a world where radio frequency (RF) signals are the invisible backbone of everything from satellite communications to military operations, threats like signal… Unveiling the Ghost Intelligence System: The Future of Real-Time RF Threat Detection

In our increasingly connected world, satellites aren’t just twinkling lights in the night sky—they’re the backbone of GPS navigation, global communications, and even military operations. But what if someone could fake those signals, tricking your… Safeguarding the Skies: How AI Fingerprints Could Stop Satellite Spoofers in Their Tracks

By Benjamin J. Gilbert – College of the Mainland In the noisy world of radio frequency (RF) monitoring, anomaly detection is both essential and difficult. Signals are buried in interference, bursts overlap, and traditional single-cue… Latent Fusion for RF Anomaly Detection

Smarter RF Signal Classification: How Ensemble ML Boosts Accuracy and Reliability By Benjamin J. Gilbert – Spectrcyde RF Quantum SCYTHE, College of the Mainland In noisy RF environments, where modulation signals blur under interference and… Multi-Subspace FAISS with Entropy Gating and Whitening: A Mode-Aware Exemplar Search Approach

Smarter RF Signal Classification: How Ensemble ML Boosts Accuracy and Reliability By Benjamin J. Gilbert – Spectrcyde RF Quantum SCYTHE, College of the Mainland In noisy RF environments, where modulation signals blur under interference and… Ensemble ML for RF Signal Classification: A Reproducible Performance Study

IMM-RF-NeRF Integration: PerformanceBenchmarks and Density Grid Scaling IMM-RF-NeRF: Benchmarking the Fusion of Radio Signals and Neural Radiance Fields By Benjamin J. Gilbert – College of the Mainland, Robotic Process Automation | Global Midnight Scan Club… IMM-RF-NeRF Integration: Performance Benchmarks and Density Grid Scaling

JWST Meets the LHC: Tracing Anomaly Correlations Across Space and Particle Physics By Benjamin J. Gilbert – College of the Mainland, Robotic Process Automation What happens when the most advanced space telescope ever built (JWST)… JWST–LHC Anomaly Correlation

Detecting Algorithmic Manipulation in RF+Net Signals: Measurement, Calibration, and Real-World Lessons. By Benjamin J. Gilbert, College of the Mainland – Robotic Process Automation In today’s hyper-connected environment, the battle between automated manipulation and trustworthy communication… Algorithmic-Manipulation Signals from RF+Net: Measurement and Calibration

“nDPI–RF Fusion for Algorithmic Manipulation Detection” Detecting Algorithmic Manipulation with nDPI–RF Fusion By Benjamin J. Gilbert, College of the Mainland – Robotic Process Automation | Spectrcyde RF Quantum SCYTHE Why Algorithmic Manipulation Detection Matters As… nDPI–RF Fusion for Algorithmic Manipulation Detection

Quantum Spin-Inspired RF Analysis: Compact Indicators, Real Gains By Benjamin J. Gilbert — Spectrcyde RF Quantum SCYTHE, College of the Mainland RF labs in Shenzhen don’t have time for theoretical fog. They need compact indicators… Quantum Spin-Inspired Analysis for RF Signal Understanding

By Benjamin J. Gilbert — Spectrcyde RF Quantum SCYTHE, College of the Mainland TL;DR You moved ray generation, stratified sampling, and volumetric integration onto CUDA kernels, wrapped it in a reproducible bench, and kept a… CUDA-Accelerated RF-NeRF: Fast Volumetric Rendering with RF-Conditioned Fields

In fast-moving RF ecosystems—where latency budgets are tight, edge devices must stay lean, and reproducibility is king—RF data processing can’t afford to wait on CPU bottlenecks. The work presented here shows how to GPU-accelerate the… CUDA-Accelerated RF Feature Extraction and Grid Reconstruction

A Lightweight Kalman+DOMA System for Opportunistic Indoor Positioning Benjamin J. GilbertCollege of the Mainland – Robotic Process Automation • Spectrcyde RF Quantum SCYTHEbgilbert2@com.edu Abstract We present a lightweight, fully reproducible pipeline for real-time RF directional… Real-Time RF Directional Tracking with Multi-Modal Fusion

Benjamin J. GilbertCollege of the Mainland – Robotic Process Automationbgilbert2@com.edu Abstract We present an integrated RF processing stack that combines directional Kalman filtering with 3D voxel density mapping, exposed through a FastAPI WebSocket for real-time… Integrated RF Signal Processing: Directional Kalman Filtering with 3D Voxel Mapping and Streaming API

Benjamin J. GilbertCollege of the Mainland – Robotic Process Automationbgilbert2@com.edu Abstract We present a streaming hypergraph formulation for RF scene understanding. A lightweight collector infers higher-order interactions between emitters, reflectors, and receivers, while an IMM-RF-NeRF… Hypergraph RF Network Reconstruction with Streaming IMM-RF-NeRF Priors

DOMA: Neural Motion Prediction for RF Targets Benjamin J. GilbertCollege of the Mainland – Robotics Process Automationbgilbert2@com.edu Abstract We present a lightweight Dynamic Object Motion Analysis (DOMA) model for short-horizon motion prediction of RF targets… DOMA: Neural Motion Prediction for RF Targets

A Minimal, Reproducible Benchmark with Calibration Benjamin J. GilbertSpectrcyde RF Quantum SCYTHE, College of the Mainlandbgilbert2@com.eduORCID: [0000-0002-1234-5678] Abstract We introduce a lightweight benchmark for RF beam selection that fuses scene priors with channel state information… Neural Beamforming with Scene Priors: A Minimal, Reproducible Benchmark with Calibration

Deep Q-Learning for Adaptive RF Beamforming with Online Angle-Error Guarantees Benjamin J. GilbertRF Signal Intelligence Research LabCollege of the Mainland, Texas City, TXbgilbert2@com.eduORCID: 0009-0006-2298-6538 Abstract We present a lightweight reinforcement learning (RL) optimizer for adaptive… Deep Q-Learning for Adaptive RF Beamforming with Online Angle-Error Guarantees