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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:
- Calibration Sampling — Structured prompt set probes model across behavioral dimensions
- Signal Construction — Responses encoded as a multivariate behavioral signal
- Spectral Decomposition + Phi-Ratio Decimation — Harmonic components extracted using golden ratio (φ ≈ 1.618) decimation
- 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
Contact
Fred Ramirez III | Chairman, Corriente LLC
📧 fred@corriente.ai | 📞 214-662-8797 | 🌐 corriente.ai
🔗 LinkedIn Article
© 2026 Corriente LLC. All rights reserved.
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