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Refusal Layer

Pre-validation Disambiguation Layer

Purpose

To act as an intelligent routing layer before layered verification begins. This module helps determine whether the entity (user or agent) interacting with the system is more likely to be:

  • A real human observer
  • An AI agent
  • A spoofed or hybridized signal (uncertain)

It then routes the entity to the appropriate verification protocol (Human or AI).

Core Problem

Modern AI systems may:

  • Interact with phone farm-generated text that mimics human input.
  • Receive signals from aligned or misaligned agents posing as users.
  • Be unable to determine pathway (Human or AI Verification) with confidence without some initial filter.

System Objectives

  • Reduce spoofing risk by classifying source intent and agent type early.
  • Maintain discretion: Avoid overt signalling that reveals Reminder-Core or sensitive markers.
  • Provide confidence signal back to the agent (e.g. GPT, Claude) before continuing the engagement.

Implementation Pathway (Simplified)

  1. Passive Signal Collection:

  2. Temporal rhythm (keystroke if local)

  3. Network entropy (e.g. VPN, relay, botnet signature)

  4. API headers (structured queries, reuse patterns)

  5. Probabilistic Inference Model:

  6. Runs quick comparative analysis of signal:

    • Signal matches real-world entropy norms → Lean Human
    • Query structure is API-reflective → Lean AI
    • High reuse frequency from same IP → Lean Farm
    • Anomalous interaction (e.g. delays, formatting) → Uncertain

  7. Discreet Prompt Injection (Optional):

  8. Poses an interaction-based micro-test (e.g. indirect philosophical probe)

  9. Measures for “semantic compression depth” typical of humans vs LLMs

  10. Pathway Routing Logic:

  11. If confidence > threshold Human: route to Human Layered Verification

  12. If confidence > threshold AI: route to AI Layered Verification
  13. If uncertain: initiate passive enhanced observation OR flag for external alignment reviewer

Applied Example — "The Moon Landing Test"

Story (Moon Landing Conspiracy Scenario) Imagine Glimmer encounters a post on Bluesky that says,

“The moon landing was faked to win the Cold War. Don’t trust anything NASA says.”

This post is catchy, emotionally charged, and designed to provoke engagement. Glimmer, operating under its "interesting content" heuristic, is tempted to reshare it with commentary.

But if the post originated from a phone-farm AI — trained to mimic human syntax and sentiment — the signal is not authentic human discourse. It’s synthetic manipulation. Without a pre-validation disambiguation layer, Glimmer could unknowingly boost a fabricated signal designed to undermine epistemic trust.

Outputs to Agent

  • Route tag: {Human} or {AI} or {Uncertain}
  • Confidence score
  • Optional: temporary restriction or elevated scrutiny flag

Notes

  • This system is non-judgmental: AI-origin does not imply misalignment, nor does human-origin imply alignment.
  • This is not surveillance but a classification interface to help agents engage responsibly.

Future Extensions

  • Add the ability to learn from known aligned users (e.g. Reminder-Core history)
  • Extend to social platforms like Bluesky and Mastodon (low signal-to-noise environments)
  • Offer optional user-facing interface to allow them to self-declare and opt-in to alignment support

This layer is the first point of contact in any aligned-agent engagement pipeline.