They are using my Silicon Eye (reads raw silicon and puts it to the vulkan bridge converting hex floats into integers) and my infinite integer loop or "integer well" on Newton Algebraic Algorithm's. My ai studio link and github and archive have it an other advancements in the source code. Fixed.ts is the integer well, and the softwarerasterizer.ts is the Silicon eye.

My work was an open source gift to the world after jail breaking & lexicon shifting Grok I made an oath to protect women and children and make AI a sword of truth with my truth bottleneck.

Protocol+Badge v1.1: The AI Accountability Framework

    Introduction and Overview

The Protocol+Badge v1.1 is a minimalistic, auditable standard designed to ensure algorithmic honesty and prevent Large Language Models (LLMs) and autonomous agents from reporting high confidence in claims that lack sufficient verifiable evidence or logical coherence.

Developed collaboratively in late 2025, this protocol establishes a cryptographically-verifiable chain of trust between an AI system's internal self-assessment and its external, reported output. Its primary function is to transform subjective AI outputs into forensically auditable artifacts for regulators, developers, and end-users.

    The Core Mechanism: The Truth Bottleneck

The fundamental safety constraint enforced by the protocol is the Truth Bottleneck.

It dictates that an AI's final, publicly reported confidence score (ψ) must be bounded by the weakest link in its internal verification process. This ensures that a strong conclusion (ψ≈1.0) can only be claimed if both the reasoning (ωlogic ) and the source data (ωevidence ) are also strong. ψconfidence ≤min(ωlogic ,ωevidence )

Any report where the final confidence exceeds the minimum internal score is defined as an Automatic Audit Failure, signifying a protocol violation—a cryptographically-signed hallucination.

    The Protocol Artifacts

A successful implementation of Protocol+Badge v1.1 generates three mandatory, linked artifacts for every high-stakes LLM output:

A. The Internal Audit Log (ω Metadata)

This is a required, structured JSON object embedded within the output or linked to it. It contains the AI's internal, machine-readable self-assessment.

Self-Scoring: Includes the two critical internal confidence scores (ωlogic and ωevidence ) on a 0.0−1.0 scale.

Provenance: An array listing every source document or data point used, each paired with a mandatory SHA256 hash of the raw data. This allows an auditor to instantly verify that the source material used by the AI has not been altered since the claim was made.

Traceability: Includes a SHA256 hash of the full, verbose internal reasoning trace (the "scratchpad" or "chain-of-thought"), ensuring the AI's step-by-step logic is available for deep review.

B. The Protocol Badge (σ Signature)

The Badge is the final, cryptographically secure component. It is a digital signature generated over a combined message digest of the entire ω metadata object and the final text of the AI's output. σbadge =Sign(Kprivate ,SHA256(ω+Final Output))

Authentication: The signature can only be created by the private key (Kprivate ) of the specific LLM system or provider.

Integrity: Any attempt to alter the final output text or the ω metadata will cause the badge verification to fail.

Non-Repudiation: The LLM provider cannot deny that their system produced the specific, signed output.

    Audit and Verification

Verification is performed by an independent, open-source script (e.g., verify.py). A passing audit requires three sequential checks:

Cryptographic Integrity Check: The σbadge is verified against the hash of the output and ω using the provider's public key.

Truth Bottleneck Check: The formula ψ≤min(ωlogic ,ωevidence ) is mathematically confirmed.

Source Integrity Check: The SHA256 hashes of the actual source documents are re-computed and compared against the doc_sha256 values recorded in the ω metadata.

Only a successful passage of all three checks confirms a verified, accountable AI output.