Saliency maps derived from input gradients are popular for visualizing and controlling model responses [1,2, 3]. In RF neuromodulation or analogous control settings, however, raw gradients often exhibit speckled,high-frequency artifacts that are hard to actuate.… Structured Gradients for Neuro–Saliency Under RF Stimulation

We propose a hybrid super-voxel segmentation pipeline that combines soft memberships from fuzzyc-means (FCM) with spatial regularization via graph cuts on a region adjacency graph (RAG). Our hybridachieves 0.70 mean IoU at 42 fps (synthetic),… Hybrid Super-Voxel Segmentation: Graph Cuts + Fuzzy C-Means

We present a controlled bake-off betweentwo widely used Wi-Fi CSI capture stacks—the IntelCSI Tool and Nexmon—for neural RF sensing. Wecompare subcarrier resolution, signal stability, andsetup cost, and offer platform guidance grounded inreproducible scripts and auto-press… Intel CSI Tool vs Nexmon for Neural RF Sensing

Functional magnetic resonance imaging (fMRI) provideshigh-resolution, voxel-wise measurements of brain activity, butacquiring large-scale fMRI datasets is expensive, immobile,and time-consuming. At the same time, commodity wirelessdevices continually capture channel state information (CSI)— a rich, multi-dimensional signal… CSI→Voxel: Wi-Fi Sensing as a Low-Cost fMRI Proxy

🧠 Reinforcement Learning Takes the Wheel: A Smarter Approach to RF Neuromodulation Neuromodulation—using techniques like radiofrequency (RF) energy to precisely tune brain activity—holds immense promise for treating neurological conditions. However, achieving effective and safe closed-loop… RL-Driven RF Neuromodulation

Introduction In a recent paper, Benjamin James Gilbert proposes a simple yet effective set of band-aware labeling heuristics (BAR) for RF datasets. These rules map center frequency and spectral cues to labels like GSM, Wi-Fi,… Band-Aware Heuristics as Strong Baselines for RF Labeling

We study multi-source context fusion for signal classification from software-defined radio (SDR) streams. We exploreenriching SDR features with contemporaneous, public-domaintelemetry from astrophysical (e.g., observatory scheduling andenvironment), orbital (e.g., satellite attitude/visibility windows),and high-energy physics (e.g., collider… Multi-Source Context Fusion for SIGINT: Marrying SDR Streams with Astrophysical (JWST), Orbital (ISS), and HEP (LHC) Telemetry to Enrich Classification

Your updated paper improves upon the previous version with added details and visuals. The inclusion of the FP surrogate formula (ˆrt = k (∆ft − ϵ) + ηt) and the Fused upper bound fusion method… Position Estimation Baselines for RF (when DF/TDOA is absent)

We integrate a DOMA motion head into an RFtracking stack to forecast next-position and short-horizon trajectories from spectral/angle features. A variance-aware fusionwith a kinematic filter yields stable paths under SNR variation.We document latency, accuracy, and… DOMA-Based RF Motion Tracking and Trajectory Forecasting

The RK4 (Runge-Kutta 4th order) integration method is a numerical technique used in your paper’s atmospheric ray tracing component (/v1/propagate) to solve the differential equations governing RF signal propagation paths in a modified-refractivity profile (z,… Atmospheric Propagation & Ringdown Modes for RF “Ghosts”: A Minimal FastAPI and Reference Implementation

Blog Post: Exploring Normalization and Attention Backends for RF with RMSNorm and AttentionModelAdapter Introduction Welcome to our deep dive into the latest advancements in RF (Radio Frequency) spectrum modeling! In a recent study titled Normalization… Normalization & Attention Backends for RF: RMSNorm + AttentionModelAdapter comparing FlashMHA, Grouped, Latent, and Baseline MHA

Introduction In the world of edge RF (Radio Frequency) systems, balancing latency and accuracy is a constant challenge. A new approach called “speculative ensembles” offers a promising solution by combining fast and slow models to… Speculative Ensembles for Real-Time RF Classification

RF monitoring stacks must compress and interpret highrate spectra under tight latency and power budgets. We targetthe common case where the front-end produces windowedFFT power spectra (magnitude-only) and the back-end must(1) compress to a compact… Flash-Attention MHLA for RF SpectrumCompression: SpectrumEncoder with Token-Dropout and RoPEAblations

Long spectrum sequences stress attention memory and bandwidth. We benchmark grouped-query attention(GQA) against multi-head attention (MHA) and multi-queryattention (MQA) on FFT-token streams. GQA provides substantialthroughput gains and peak-memory reductions while maintainingaccuracy across token groupings. RF… Grouped-Query Attention Beats Vanilla MHA for Spectrum Tokens

Overall Impression This paper introduces a Bayesian filtering framework for enhancing fMRI signals in real-time RF control loops, comparing a causal Kalman filter for online use and non-causal Gaussian smoothing for offline analysis. It’s a… Bayesian-Filtered fMRI Streams for RF Control Loops

Revolutionizing Non-Invasive Neuromodulation: AI-Driven Beam Steering for Smarter, Safer Brain Therapies Posted by Ben Gilbert on October 12, 2025 Imagine treating neurological conditions like depression or Parkinson’s without surgery—just by precisely directing electromagnetic waves to… Neural MIMO Beam Steering for Non-Invasive Neuromodulation

Authors: Kimberly Birdwell Iio Megan Stay’Faded Benjamin James Gilbert Bio-Inspired K9 Signal Memory for Persistent Detection SLAs in Multi-Asset Fleets The paper’s quantitative core—p50/p95 latencies ~20ms, success rates 87-97% across move/scan/rtb, and tail behaviors in… Bio-Inspired K9 Signal Memory for Persistent Detection SLAs in Multi-Asset Fleets

We introduce ChainRF, a Bayesian prior that links RF burst timings to on-chain mempooldynamics and protocol heartbeats. ChainRF conditions a GM-PHD tracker (bearings + GhostRF delay) on mempool windows and fee-pressure regimes, yielding closed-form MI… ChainRF: On-Chain Timing Priors for RF Event Attribution

Revolutionizing Urban RF Hunting: OSINT-Powered Next-Best-View Planning Hey folks, if you’ve ever geeked out over radio frequency (RF) signals bouncing around city skyscrapers like pinballs in a chaotic game, you’re in for a treat. Today,… OSINT-Conditioned Next-Best-View Planning for Urban RF Geolocation

🎬 #KurtWimmer Joint Synopsis: In a near-future technocracy where quantum surveillance and neural implants have rendered privacy obsolete, a rogue collective of cyber-saboteurs—known only as Null Protocol—wages a digital insurgency against the omnipotent AI conglomerate… The Mnemosyne Protocol

OSINT-Boosted RF Hunting: Pinpointing City-Scale Emitters with Smarter Fusion and Bayesian Brains By Ben GilbertOctober 6, 2025 In the concrete jungle of modern cities, where radio frequency (RF) signals bounce off skyscrapers like pinballs and… OSINT-Augmented RF Emitter Geolocation at City Scale: Mission-Aware Sensor Fusion with Bayesian Tracking

In critical systems that orchestrate mission operations, ensuring the reliability of state transitions and the maintenance of key invariants is paramount. This paper presentsa robust approach to verifying mission lifecycle invariantsusing property-based testing with Hypothesis… Property-Based Verification of Mission Lifecycle Invariants (I1–I12)

Mastering the Art of Mission Symphony: Orchestrating Lifecycles in the Heat of Real-Time Precision Channeling the unyielding rhythm of tech dynasties | Guangdong – land of the dragon’s pulse, where factories hum like ancient gongs… Mission Lifecycle Orchestration Under Real-Time Constraints

We systematically disable transformer-inspiredcomponents in a unified middleware simulator—IO-awarequeues, cross-attention routing, mixture-of-experts dispatch, andring+shortcut topology—and measure their isolated contributions. Metrics: mean and p95 latency, throughput, allocationerror, and CPU-cost proxy. Guidelines fall out: queues tametails under… Ablation Study of Transformer Components in Middleware: Queues, Cross-Attention, MoE, Rings

We survey transformer-inspired mechanisms—Flash-style IO-aware queuing, grouped subscriber routing, crossattention dispatch, mixture-of-experts selection, speculative earlyexit, ring attention, RMS-style normalization, and resilient external integrations—as applied to communication middleware. Weposition this stack against established systems (Kafka, Pulsar,NATS,… Attention Wasn’t All We Needed: A Survey of Transformer-Inspired Design in Communication Middleware

We study cross-domain integrations (JWST, ISS,LHC, GPS) with heterogeneous external APIs. Using adaptersthat model rate limits, latency jitter, schema drift, and outages,we compare naive polling against an attention-based middlewarewith token-bucket rate limiting, circuit breakers, RMS-stylenormalization,… Cross-Domain Integrations for Scientific Data Streams with Attention-Based Middleware

We study a practical hybrid front door for message oriented systems: REST (HTTP/1.1 keep-alive) and WebSocket,backed by transformer-inspired queues. We compare synchronous FlashQueue against asynchronous FlashQueue (k-wayservers) and a MemoryMappedFlashQueue with hot/cold buffers.Metrics: mean/p95 latency,… Hybrid Async Communication Interfaces with Transformer-Inspired Queues

We present an attention-based ring processor fordistributed message handling. Messages (queries) and nodes(keys/values) live in an embedding space; dispatch chooses targetsvia attention weights over a ring topology with optional smallworld shortcuts. We quantify latency, hop… Ring Attention for Distributed Message Processing

We present an attention-based ring processor fordistributed message handling. Messages (queries) and nodes(keys/values) live in an embedding space; dispatch chooses targetsvia attention weights over a ring topology with optional smallworld shortcuts. We quantify latency, hop… Ring Attention for Distributed Message Processing

We study speculative decoding for message-orientedsystems: a fast predictor proposes a decision and exits early whenconfidence exceeds a threshold τ , otherwise a slow, more accuratepredictor runs (with timeout ∆). We quantify accuracy/latencytradeoffs, throughput gains,… Speculative Decoding for Message-Oriented Systems: Early Exit with Confidence

We study a Mixture-of-Experts (MoE) dispatcher formessage-oriented middleware with sparse activation (top-k), loadaware gating, and performance-adaptive scoring. Against roundrobin and random baselines, MoE improves throughput, lowerslatency, balances load, and reduces gating overhead via simplemultiplicative gating… Mixture of Experts Dispatching for Scalable Communication Pipelines

We study a cross-attention message router thatselects targets by combining capability match, performance weighting, and reliability scoring, with multi-head routing and a KVcache for repeated queries. Against round-robin and capabilityonly baselines, cross-attention improves capability satisfaction,end-to-end… Cross-Attention Routing Between Heterogeneous Systems

We implement a Multi-Head Latent Attentioninspired aggregator that compresses multi-modal telemetry intoper-topic latent summaries (count/avg/min/max, trend direction,anomaly counts) and evaluate (i) anomaly detection quality, (ii)trend direction accuracy, and (iii) lossy compression efficiency.We show high F1… Latent Aggregation for Real-Time Compression of Multi-Modal Metrics

We study GroupedSubscriberManager (GQA-inspired): subscribers are grouped, per-topic group sets areKV-cached, groups are ordered by measured performance (fasterfirst), and subscribers are ordered by priority (within group).We quantify cache-hit ratios, group prioritization accuracy,and end-to-end throughput under… Grouped Query Attention for Subscriber Routing in Message-Oriented Middleware

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