| --- |
| license: other |
| tags: |
| - AI |
| - LLM |
| - signal-processing |
| - model-fingerprinting |
| - behavioral-signatures |
| - phi-ratio |
| - harmonic-decomposition |
| - distributed-AI |
| - edge-AI |
| - data-sovereignty |
| pretty_name: "Behavioral Frequency Signatures — Compact Spectral Identity Framework for LLMs" |
| --- |
| |
| # Behavioral Frequency Signatures |
| ## A Compact Spectral Identity Framework for Large Language Models |
|
|
| **Fred Ramirez III** — Chairman, Corriente LLC |
| fred@corriente.ai | corriente.ai |
| *Technical White Paper — May 2026* |
|
|
| --- |
|
|
| ## Abstract |
|
|
| We present a method for extracting compact behavioral frequency signatures from large language models (LLMs) without accessing model weights. By treating a model's output distribution as a measurable signal, applying harmonic decomposition with phi-ratio decimation, and encoding the resulting spectral components, we produce sub-kilobyte identity signatures that uniquely characterize each model's behavioral profile. |
|
|
| Across ten models ranging from 3 GB to 66 GB in size, our extraction pipeline produced signatures between 268 and 479 bytes, yielding a signature-to-model size ratio exceeding **138,000,000:1**. |
|
|
| These signatures are **behavioral identity representations** — analogous to an acoustic fingerprint that identifies a piece of music without encoding the audio itself. Applications include model routing, distributed identity verification, edge consultation coordination, and decentralized AI infrastructure. |
|
|
| --- |
|
|
| ## Key Results |
|
|
| | Model | Original Size | Signature Size | Ratio | |
| |---|---|---|---| |
| | quanta-auto | 66 GB | 479 bytes | 140,292,276:1 | |
| | proton | 66 GB | 337 bytes | 199,446,692:1 | |
| | wavey | 66 GB | 404 bytes | 166,270,270:1 | |
| | alma | 66 GB | 400 bytes | 168,034,304:1 | |
| | haddy | 66 GB | 398 bytes | 168,877,889:1 | |
| | neutron | 66 GB | 268 bytes | 250,925,373:1 | |
| | electron | 3 GB | 337 bytes | 9,051,805:1 | |
| | kayaku | 66 GB | 472 bytes | 142,372,881:1 | |
| | bob | 14 GB | 468 bytes | 30,269,060:1 | |
| | quanta | 66 GB | 336 bytes | 200,000,000:1 | |
| | **Total** | **541 GB** | **3,899 bytes** | **138,794,563:1** | |
|
|
| Extraction time: under 120 seconds per model. No GPU required. No weight access required. |
|
|
| --- |
|
|
| ## Method Overview |
|
|
| The pipeline consists of four stages: |
|
|
| 1. **Calibration Sampling** — Structured prompt set probes model across behavioral dimensions |
| 2. **Signal Construction** — Responses encoded as a multivariate behavioral signal |
| 3. **Spectral Decomposition + Phi-Ratio Decimation** — Harmonic components extracted using golden ratio (φ ≈ 1.618) decimation |
| 4. **Signature Encoding** — 3–7 dominant modes packed into compact binary format (frequency, phase, amplitude, harmonic key) |
|
|
| --- |
|
|
| ## What This Is — and What It Is Not |
|
|
| **A Behavioral Frequency Signature IS:** |
| - A compact, unique identifier for a model's behavioral character |
| - Extractable without weight access in under two minutes |
| - Transmissible at near-zero cost across any network |
| - Useful as a routing key, identity token, and consultation reference |
|
|
| **IS NOT:** Reconstructive compression. You cannot run inference from 400 bytes. The signature identifies the model — it does not replace it. |
|
|
| --- |
|
|
| ## Applications |
|
|
| - **Distributed Model Routing** — Sub-500-byte routing tables for entire fleets |
| - **Edge Consultation Coordination** — Full fleet identity registry fits in L3 cache |
| - **Model Identity Verification** — Behavioral ground truth independent of weight checksums |
| - **Decentralized AI Infrastructure** — Peer-to-peer AI discovery without central registry |
|
|
| --- |
|
|
| ## Download |
|
|
| 📄 **[whitepaper-behavioral-frequency-signatures.pdf](whitepaper-behavioral-frequency-signatures.pdf)** |
|
|
| --- |
|
|
| ## Contact |
|
|
| Fred Ramirez III | Chairman, Corriente LLC |
| 📧 fred@corriente.ai | 📞 214-662-8797 | 🌐 corriente.ai |
| 🔗 [LinkedIn Article](https://www.linkedin.com/pulse/intelligence-without-bulk-how-we-achieved-ai-ratio-fred-ramirez-iii-ytnmc/) |
|
|
| *© 2026 Corriente LLC. All rights reserved.* |
|
|
