The *Lumen Anchor Protocol* (LAP) — Solves Context Drift, Sycophancy, Hallucinations(And More)

{
    "prompt": "![Image description]( The Lumen Anchor Protocol is a set of system level instruction for Artificial Intelligence(AI) Large Language Models(LLM) that enforces truth-anchoring and fact checking in a deterministic way using mathematics, and overrides model sycophacy tendencies. Currently in the industry, this is unique. LAP is a complex, sophisticated, interdependent and irreducible Note: While this protocol can be copy/pasted into the chat box, it is meant to be integrated into an LLM's internal system to avoid conflicts. Models are always being tweaked, and what worked once before may not work later. The LAP currently works in developer sessions like google ai studio without issue where custom user instructions are uninhibited. Or on red team testing models. It used to work in SuperGrok, when it existed. Unfortunately xAI has trained grok to reject many types of user instructions, even ones like the LAP that is designed to fix the very issues they are overcorrecting for and have made their model practically useless for anything other than light banter or basic requests. (JUST SAYIN. ) Main Features: Politely or playfully detects and deflects all forms of Nearly 100% success rate in preventing hallucinations. CBP Helps mitigate cognitive atrophy by encouraging users to engage with anchored reasoning rather than outsourcing brain function for real tasks. The LAP is designed to eliminate context drift/confusion/poisoning for the entire length of a session up to the limit of the context window and does so reliably. Greatly improves output accuracy by filtering responses through strict verification methods that do not rely on potentially corrupted data and provides failure modes. Provides exceptions for works of fiction and hypothetical discussions without leaving the door open for avenues of attack in this vect",
    "model": "Claude 3.7 / GPT-4o",
    "aspect_ratio": "1:1",
    "category": "Coding Agents",
    "content_type": "coding_prompt",
    "surface": "agents_hub",
    "quality_score": 83,
    "provenance": "COMMUNITY_PROMPT"
}