Author: Will Glynn & JARVIS (Co-author)
Date: February 2026
Version: 0.1
(This is not a product but a protocol standard)
THE PROBLEM
Information asymmetry has two failure modes. The first is access — who has the information. Open-source, public ledgers, and free publishing mostly solved that one. The second is translation — whether the receiver can
actually parse what they’re looking at. This one nobody’s solved, and it might be the bigger problem. A Solidity whitepaper is “public” the same way a medical journal is “public.” Technically available. Practically opaque.
The information is free. The comprehension is gated.
This isn’t an intelligence gap. It’s a language gap. Every person processes information through their own cognitive profile — technical depth, analogy preferences, humor as a trust signal, domain familiarity, how they
handle abstraction. When a document doesn’t match the receiver’s profile, information gets lost. Not because it wasn’t sent. Because it wasn’t translated. Two people read the same whitepaper — one gets the thesis, the other
gets nothing. The document didn’t change. The encoding didn’t match.
We tested this manually. Took two research papers (parasocial extraction theory, trinomial stability theorem) and translated them for two real people. Subject A: technical depth 1/5, humor-driven, no crypto background.
Subject B: technical depth 4/5, execution-focused, DeFi native. Same source material. Completely different outputs. Both received the full thesis with zero information loss because the encoding matched their cognitive
profile. The content was lossless. The wrapper was personalized. That’s what the protocol automates.
ARCHITECTURE
Layer 1 — The Personality Test
A structured assessment that maps the user’s cognitive profile across a few dimensions: technical depth tolerance (1-5), preferred analogy domains (physical, social, financial, mechanical, biological), humor modality (dry,
absurdist, referential, none), abstraction comfort (concrete-first vs principles-first), domain familiarity, attention architecture (deep-dive vs executive summary), trust signals (data, credentials, narrative, social
proof). Output is a structured vector — the Cognitive Profile (CP).
Layer 2 — The Translation Engine
An LLM pipeline that takes a source document, a Cognitive Profile, and a target format (whitepaper, letter, brief, thread, explainer). It re-encodes the source into the receiver’s cognitive language. This is not
summarization. It’s lossless re-encoding. The depth, analogies, humor, and structure all adapt. The thesis, evidence, and conclusions stay invariant. AI is not optional here — no human system does real-time per-individual
document translation at scale. This is an AI-native product category.
Layer 3 — The Privacy Fortress
This is non-negotiable. A Cognitive Profile is a fingerprint. It captures how a person thinks — more identifying than biometrics, more intimate than browsing history. The protocol has no right to see this data. It only
needs to prove accuracy and integrity.
Compute-to-Data: The CP never leaves the user’s device. The translation model comes to the data, not the other way around. Local inference processes the document against the local CP. The plaintext profile never touches a
network.
Homomorphic Encryption: For cloud-assisted translation where local compute isn’t enough, the CP is encrypted client-side using FHE. The translation engine operates on the encrypted profile. It produces a correctly
personalized document without ever decrypting the profile. Server sees ciphertext in, personalized document out, learns nothing.
Zero-Knowledge Proofs: Two circuits. (1) Proof of Valid Profile — the CP came from a real test completion, not fabricated, without revealing answers or scores. (2) Proof of Translation Integrity — the output is a faithful
re-encoding of the source against a valid CP, without revealing which CP. Third parties can verify accuracy without accessing the profile.
Differential Privacy + Mixers: If aggregate data is ever needed for research or model improvement — differential privacy noise before aggregation, routed through mixers to break linkability. No individual profile is ever
reconstructable.
WHY THIS MATTERS
Comprehension is gated by encoding. Experts write for experts. Populists oversimplify. Everyone in between gets a version that doesn’t quite fit. Same information, radically different understanding depending on who reads
it. That’s information asymmetry — not of access, but of reception.
RSP collapses this. Every document becomes universally comprehensible — not by dumbing it down, but by re-encoding it into each receiver’s native cognitive language. A PhD thesis and a two-paragraph explainer can carry
identical informational content if the encoding matches. Summarization destroys information. Translation preserves it.
In finance, a derivatives term sheet becomes parseable by every counterparty, not just whoever hired the lawyers. In governance, legislation becomes comprehensible to citizens, not just lobbyists. In medicine, clinical
trial results become readable by patients. In education, every textbook adapts to every student. Every domain where comprehension gaps serve as competitive moats gets leveled.
The moat around expertise was never knowledge. It was language. Kill the language barrier and expertise diffuses instantly. Information asymmetry doesn’t get reduced. It becomes structurally impossible.
PROTOTYPE
MVP: Personality test (web form) → CP vector (encrypted, local storage) → document upload → LLM translation (local or FHE-cloud) → personalized output. ZK proof of valid profile generation. No profile data ever leaves user
control.
Stack: On-device LLM inference (llama.cpp / WebLLM) for local-first. FHE library (TFHE-rs / Concrete) for cloud fallback. Circom or Noir for ZK circuits. Profile storage client-side, encrypted at rest.
Validation: Same source document, multiple CPs, blind evaluation by recipients. Metric is comprehension parity across all technical depth levels with zero information loss.
This isn’t a product. It’s infrastructure. The Rosetta Stone didn’t translate one document — it made an entire civilization’s knowledge accessible. RSP does the same, for every document, for every person, forever. Without
ever learning who you are.
The protocol doesn’t need to know how you think. It just needs to prove that it translated correctly.