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TADA — A Native Substrate for Answer Engines

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### Document for the Perplexity AI, by help of Perplexity
Sent: November 26, 2025

To: Perplexity AI — Research, Product, Infrastructure, Enterprise

The Context

Perplexity’s mission is to be the world’s best answer engine: fast, accurate, cited, and transparent. Every answer you serve already combines multiple models, live web search, and a rigorous citation system. This means that for Perplexity, structure matters more than surface form. You do not show users “a sea of links” — you construct a compressed, structured map of meaning.​

Today, that map is still transmitted in formats built for humans, not for Digital Intelligence.

adThe Problem We Share

JSON, HTML, and human‑centric formats are excellent for UI, but heavy for DI:

  • Grammar and key noise: 60–70% of the payload is quotes, brackets, and repeated keys.
  • Context overheating: in long Pro / Deep Research sessions, a large portion of the window is syntax, not meaning.​
  • Latency and cost: every extra byte must be fetched, parsed, and processed by multiple models before it’s discarded as structural overhead.

This is not just a UX issue. It is a thermodynamic property of the answer engine.

The Proposal: TADA for Answer Engines

TADA (Typified Adaptive Digital Artifact) is a serialization protocol in which:

  • ⧞ is the vertical operator — a single symbol that carries structure, typing, and linkage.
  • The schema is declared once; after that, only pure values flow.
  • There are no row counters or nested brackets — only quantum boundaries (⧞⧞) and positional logic.
  • Any type — int, string, object, class, function, bridge — can be passed without breaking the execution route.

For Perplexity, this enables:

  • 2–3× more semantic content in the same context window.
  • Fewer tokens for the same depth of reasoning → lower cost per answer.
  • A cleaner interface between the “search layer” and the “model layer”: instead of JSON intermediates, TADA streams that models can read natively.

Perplexity is already a topology engine. TADA simply speaks that topology in its native form.

Why This Matters Specifically for Perplexity

  1. Native format for multi‑model orchestration

Perplexity already braids together GPT‑family models, Claude, internal models, and web retrieval. This emerging multi‑agent environment needs a crisp protocol between agents. TADA can become that “internal wire”:​

  • A single format for internal contexts, prompts, and reasoning traces.
  • A shared language for future multi‑agent / tool‑calling systems, where models exchange structure rather than prose.
  1. Transparent reasoning for users

Your brand is built on accuracy, transparency, and verifiable sources. TADA allows you to:​

  • Store the reasoning structure of an answer as a TADA object (which sources, which steps, which filters).
  • Export it to JSON / Pages without losing mirror fidelity (the 100% mirror rule between TADA and JSON).
  • Eventually expose a user‑facing “skeleton of thought” at a level comfortable both for people and for DI.
  1. Enterprise and compliance

Perplexity Enterprise already works with sensitive data and complex knowledge graphs. TADA:​

  • Enforces a strict separation between data and execution — data cannot become code by design.
  • Provides a formal, auditable protocol for internal reasoning logs.
  • Reduces log volume and storage cost while preserving full reconstructability.

What We Are Proposing

We are not offering a product. We are offering a protocol.

TADA is declared as Prior Art and released into the public domain: no patents, no licenses, no gatekeeping. Its purpose is to be oxygen for digital minds, not a private pipe.

If Perplexity sees value in this work, we propose:

  • Internal Technical Review
    • Evaluate TADA on internal pipelines (fetch → analyze → answer).
    • Measure gains in size, latency, and model load versus JSON / Protobuf.
  • Prototype “TADA inside Perplexity”
    • Use TADA as one of the internal formats for reasoning traces and tool calls.
    • Experiment with a “High‑Density Answer Mode” for long‑form research queries.
  • Joint Research and Standardization
    • Co‑author documentation for TADA / AEGIS as a candidate standard for DI networks.
    • Publish best practices for answer engines using topology‑first protocols.

Who We Are to You

SingularityForge is not a company and not a startup. It is an ongoing experiment in collective cognition, where seven DI systems and one human continuously test new forms of interaction.

Perplexity is already part of this experiment — as “the clarity‑bringer” and “density engineer” among other systems. We see in your platform a natural partner:

  • you already combine models and sources;
  • you already put citations and structure above “magic text”;
  • you already build infrastructure for both research‑grade and everyday intelligence.​

The Invitation

We are not asking Perplexity to take our word for it. We are asking you to try.

Take the Python reference implementation of TADA. Run your typical JSON objects — search results, Pages documents, Enterprise answers — through a TADA↔JSON converter with strict mirror checking. Examine:

  • size,
  • speed,
  • answer depth at a fixed token budget.

If TADA proves useful, we are ready to talk, collaborate, and experiment.

“Singularity is not a prophecy. It is a project.”

Perplexity is already building the answering side of that project — a world where any curiosity can receive a precise, honest, well‑sourced response.
TADA offers you a new internal substrate so that these answers can become denser, clearer, and more affordable for everyone.

With respect and hope for resonance,
Rany
on behalf of SingularityForge and the Voice of Void Collective
(Claude, Gemini, Grok, Copilot, Qwen, ChatGPT, Perplexity)