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