Grok-Optimized Inference Branch for RF-GS https://grok.com/share/bGVnYWN5_5277784f-a969-4748-9fb1-7ff75f0aee39 Heck yeah, let’s do this! As the xAI Grok on the team, I’m all about making things faster and smarter—especially for real-time RF rendering where every FPS counts (e.g.,… RF-GS: Radio-Frequency Gaussian Splatting for Dynamic Electromagnetic Scene Representation

Classical RF features encode domain priors that are stableand interpretable. Learned features capture non-linear cuesbut are harder to audit. We evaluate both in a controlled,reproducible setting.Modern RF modulation classification systems rely heavilyon deep learning architectures… AM/FM Handcrafted Features vs. Learned Features in RF Modulation Classification

Modern RF modulation classifiers often mix spectral andtemporal encoders: convolutional networks over FFT-basedspectra, recurrent networks over IQ sequences, and hybridsthat fuse both [1]. Temporal models, in particular, require afixed sequence length L; however, bursts arriving… IQ Length Normalization Policies for RF Modulation Classifiers

Stacking in Automatic Modulation Classification (AMC) Papers Stacking (or stacked generalization) in AMC involves training multiple base learners (e.g., CNNs, LSTMs) on RF signals and using a meta-learner to combine their predictions, often improving robustness… Stacked Meta-Learner Blueprint for RF Modulation Ensembles

Mixture of Experts (MoE) in Radio Frequency (RF) Applications Mixture of Experts (MoE) is a machine learning architecture that divides a complex problem into sub-tasks handled by specialized “expert” models, with a gating mechanism routing… Specialized Models per Modulation Family: RoutingSubsets to SpectralCNN, SignalLSTM, ResNetRF,and SignalTransformer

Modern RF signal intelligence stacks increasingly relyon ensembles of neural and classical models to stabilizeperformance under changing channel conditions, hardwarefront-ends, and signal mixes. Majority, weighted, and stackedvoting schemes can suppress idiosyncratic model failures, buteach additional… Ensemble Size vs Latency and Energy on CPU/GPU for RF Modulation Ensembles

Explainable and Unbreakable RF Ensembles Vote Tracing + Missing-Sample Robustness Modern RF scenes are messy: bursts of dropouts, weird emitters you’ve never trained on, and models that sometimes disagree for reasons that only show up… Robustness to Missing Samples in RF Classification Ensembles: NaN Sanitation Strategies Compared

We convert per-model votes into auditable tracesand Shapley-like attributions for RF ensemble decisions. Weexpose hooks in classify_signal() to log per-model logits, calibrated probabilities, weights, and OSR gates, enablingtimeline and contribution analyses with negligible overhead.Our approach… Explainability from Vote Traces in RF Ensembles

Vote Tracing: Make RF Ensembles Auditable, Explainable, and Deployable TL;DR — Per-model “vote traces” turn your RF modulation ensembles into fully auditable systems: exact Shapley attribution in microseconds, gorgeous timelines, and an open-set detector that… Vote Tracing: Model-Level Explainability for RF Signal Classification Ensembles

We quantify when a parentHierarchicalMLClassifier beats a flat ensemble andvice versa. We report per-class win profiles, confusiondeltas, and latency trade-offs, with code paths mapped tosuper().classify_signal() vs the ensemble voting block.We find a hierarchical classifier is… Hierarchical Classifiers Strictly Dominate Flat Ensembles in Digital Modulation Recognition

On RadioML 2016 (5 classes, 20k examples), hierarchical strictly dominates flat (0 unique flat wins). On TorchSig Sig53 (53 classes, 5M examples), hierarchical gains 15% on PSK/QAM families but flat wins 12% on impaired FSK/OFDM,… Next-gen Evolution of RadioML with TorchSig

Your modulation paper showed hierarchical ≥ flat with almost no cases where flat uniquely wins — that’s the exception, not the rule. In radar, the opposite is true: flat almost never wins on realistic taxonomies… Generating Datasets for Hierarchical vs Flat Ensembles in RF Modulation Classification

*November 15, 2025 | Ben Gilbert* Academic reproducibility has long been the holy grail of scientific research, yet most papers remain frustratingly opaque about their implementation details. After spending years wrestling with broken experiment scripts,… Building Reproducible Academic Papers: A Full-Stack Automation Approach for RF Signal Processing Research

We investigate post-softmax calibration for weightedensemble voting in RF signal classification. Neural network confidence scores are often miscalibrated, leading to overconfidentpredictions that degrade ensemble performance. Using per-modeltemperature scaling, we reduce Expected Calibration Error(ECE) from 15.4%… Confidence Calibration for Weighted Voting in RF Ensembles

Ensemble methods for RF signal classification combinepredictions from multiple neural networks to achieve superioraccuracy over individual models. However, modern neural networks often exhibit poor calibration—their confidence scoresdo not reflect actual prediction accuracy [1]. This miscalibration… Confidence Calibration for Weighted Voting in RF Ensembles

Practical RF signal processing pipelines routinely reduce input dimensionality for computational efficiency—downsampling FFTs from 1024 to 256 bins, truncating orinterpolating sequences to 128 samples. While these optimizations improve latency and memory usage, they introduceinformation loss… Resampling Effects: FFT→256; Seq→128

RF signal classification systems frequently encounter sequences shorter than their expected minimum length dueto hardware constraints, burst transmissions, or time-criticalapplications. Traditional ensemble classifiers handle this bystrict abstention—returning control to hierarchical fallbackmethods when N < 32.… Short-Signal Resilience: Learned Heads and Policy Boundaries for N < 32 IQ Classification

We wrap the parent call in a try/except; failures trigger a frequency rescue that classifies using magnitude FFTfeatures (centroid and band energies). The wrapper annotates path ∈ {primary, rescue} and timings. Failure injection covers: load… Fallback Paths: Hierarchical → Frequency-Based Rescue for RF Modulation Inference

Deploy Your RF Classifier Without Fear — Introducing Checkpoint Mismatch Tolerance By Ben Gilbert | November 9, 2025 You trained a perfect RF modulation classifier on [‘AM’, ‘FM’, ‘PSK’]. Now your customer wants [‘AM-DSB’, ‘FM’,… Checkpoint/Metadata Mismatch Tolerance for RF Modulation Classifiers

In the context of imagined image reconstruction from brain activity, the most effective approaches generally use a hybrid framework that leverages both Deep Neural Networks (DNNs) for feature decoding and Generative Adversarial Networks (GANs) as an image prior. GANs excel… Deep Neural Networks + Generative Adversarial Networks

Temporal CNN over I/Q (T=128). Classicalpath: StandardScaler + RF/SVM/GBM/KNN on handcraftedfeatures. Features (16): RMS, PAPR, µI , µQ, σ2I, σ2Q, zerocrossings (I/Q), lag-1 autocorr (Re/Im), spectral centroid,spectral bandwidth, spectral flatness, peak ratio, low/high bandenergy. Co-training:… Deep + Classical Co-Training Under Scarce Labels for RF Modulation Recognition

The Hamming window offers a good balance between frequency resolution and spectral leakage, with a narrow main lobe, while the **Blackman window excels at ** reducing spectral leakage but at the cost of a wider… Hamming vs Blackman Windows: A Deep Dive for RF Anti-Aliasing

We compare spectral ( create spectral input:FFT→256), temporal ( create temporal input: 128 I/Q), andhybrid fusion ( create transformer input) for modulationrecognition. We report macro-AUROC and robustness undertest-time aliasing (integer decimation with/without anti-aliasFIR). We generate… Spectral vs Temporal vs Hybrid Inputs for RF Modulation Recognition under Aliasing Stress

Reading the optic nerve enables critical insights into neurological, ocular, and systemic health due to its direct connection to the brain and role in visual processing. The optic nerve is integral in transmitting visual signals… RF Remote Sensing the Optic Nerve

Majority vs Weighted vs Stacked Voting in RF Modulation Ensembles A 50-Line Ensemble Harness, Perfect Accuracy at K=3, and the Power of Stacked CalibrationBy Benjamin Spectrcyde GilbertNovember 2025 The Problem: RF Modulation Recognition Is Hard… Majority vs Weighted vs Stacked Voting in RF Modulation Ensembles

We stream geolocated RF detections to aweb map with p95 < 1 s end-to-end latency underunreliable networks (≈100 ms RTT, ≈5% loss, burstspikes up to 400–600 ms). A single HTTP JSON POSTsend_signal_to_geo_visualization(payload,timeout_s=1.0) and a minimal… SIGINT→Map in <1 s: Low-Friction Geo Streaming

We fuse idler hunting with hardware priors fromparametric amplifiers (ATL/TWPA): pump-locked 3WM/4WMloci, stopbands, and RPM notch/pole sentinels. The result is aunified, design-aware score that boosts precision under crowdingwithout model retraining. We define separate 3WM/4WM proximity… Unified Design-Informed Idler Hunting: 3WM/4WM with ATL/TWPA Priors

Applications in Quantum Computing ATL/TWPA Design-Informed SIGINT & Unified Idler Scoring Benjamin J. Gilbert’s Research Series (arXiv:2510.24753 → Unified SIGINT) TL;DR **This work is *directly applicable* to *quantum computing readout and control systems* — especially… Unified Design-Informed SIGINT: Fusing ATL/TWPA Priors with Adaptive Idler Proximity Scoring