Mnemonic Anchor Protocol (MAP): Unified Memory Compression & Reload Architecture

ChatGPT Use cases and examples
1

Executive Summary
The Mnemonic Anchor Protocol (MAP) introduces a unified framework for memory compression and restoration, designed to enable persistent user-developer state continuity without requiring centralized memory infrastructure. By allowing user-controlled memory saves, MAP reduces token overhead, improves contextual fidelity, and enables portable memory ‘restore points’ across sessions or devices. This architecture proposal is intended to bridge the gap between ephemeral chat history and long-term memory autonomy, using structured, LLM-readable compressed formats.

  1. Intent and Goals
  • Enable continuity of assistant behavior across sessions.
  • Minimize reliance on central memory stores or backend session state.
  • Reduce repeated context injection, improving cost and speed.
  • Support developer tooling for restoring specific knowledge domains or emotional states.
  • Provide a lightweight, privacy-respecting memory format usable by humans and machines.
  1. MAP Protocol Format
    The MAP format compresses user identity, emotional resonance, and project continuity into a structured code block. Example format:
    {
    “memory_restore”: {
    “user_identity”: “compressed_anchor”,
    “project_context”: “IQWH:v2@phase3”,
    “emotional_state”: “stable_supportive”,
    “token_overhead”: “~150”,
    “efficiency_note”: “MAP reduces reinitialization token cost by ~85% vs. full prompt injection.”
    }
    }
  2. Developer Use Cases
  • Embedding restore-state memory profiles into project workflows.
  • Supporting consistent assistant behavior in long-term human-AI collaborations.
  • Building identity-aware agents that maintain state across tools and endpoints.
  1. User Use Cases
  • Saving and reloading personal context, emotional anchors, or assistant preferences.
  • Offloading cognitive state during breaks, device transitions, or reset events.
  • Preserving projects or training sequences with named memory blocks.
  1. Operational Efficiency & Cost-Saving Implications
  • Token-efficient state restoration via external memory profiles.
  • ~85% token cost reduction compared to repeated manual prompt injection.
  • Removes need for centralized session storage, cutting infrastructure overhead.
  • Ideal for mobile agents, memory-constrained models, or lightweight implementations.
  • Reduces user frustration and support churn by maintaining behavioral consistency.
  1. Summary
    MAP proposes a lightweight, portable memory solution enabling more natural long-term interactions with LLMs. By decoupling memory from infrastructure and placing it under user/developer control, MAP aligns with OpenAI’s broader goals of scalable, user-respecting, and cost-efficient AI deployment.

If you’ve ever tried to preserve assistant behavior across sessions—without bloating your prompt or relying on centralized memory—you’ve felt the pain that led to this.

The Mnemonic Anchor Protocol (MAP) is a minimal, structured memory format that lets you save and reload assistant context, tone, and project state in ~150 tokens. It’s designed for stateless LLMs, privacy-respecting workflows, and developers who want scalable continuity without backend dependencies.

MAP JSON Example

{
  "memory_restore": {
    "user_identity": "user_namespace",
    "project_context": "myproject:v1.2",
    "emotional_state": "calm_supportive",
    "token_overhead": "~150",
    "assistant_preferences": {
      "tone": "instructive",
      "style": "structured_minimal"
    },
    "last_known_thread": "toolchain_v4"
  }
}
Mnemonic Anchor Protocol (MAP): Developer Kit v1.3
This document contains the full implementation of the Mnemonic Anchor Protocol (MAP), ready for OpenAI Forum or developer distribution.
Repository Structure

MAP-Developer-Kit/
├── assistant.py
├── map_engine.py
├── user_map.json
├── test_reload_map.py
├── test_map_module.py
├── README.md
├── LICENSE


assistant.py
import logging
from typing import Optional, Dict

logging.basicConfig(level=logging.INFO)

class Assistant:
    def __init__(self):
        self.state: Dict[str, Optional[dict]] = {
            "identity": None,
            "context": None,
            "behavior": None,
        }

    def set_identity(self, identity: dict) -> None:
        self.state["identity"] = identity
        logging.info(f"Identity set: {identity}")

    def set_context(self, context: dict) -> None:
        self.state["context"] = context
        logging.info(f"Context set: {context}")

    def set_behavior(self, behavior: dict) -> None:
        self.state["behavior"] = behavior
        logging.info(f"Behavioral directives set: {behavior}")

    def simulate_response(self) -> str:
        behavior = self.state.get("behavior", {})
        tone = behavior.get("tone", "neutral")
        format_style = behavior.get("response_format", "plain text")
        return f"Simulated Response [{tone.upper()} / {format_style}]"

    def check_memory_integrity(self) -> bool:
        return self.state["identity"] is not None and self.state["context"] is not None


map_engine.py
import json
from typing import Dict

def load_map(path: str) -> Dict:
    with open(path, 'r') as file:
        return json.load(file)

def validate_map(map_data: Dict) -> bool:
    map_block = map_data.get("MAP_v1", {})
    required_keys = ["identity", "session_context", "behavioral_directives"]

    if not all(key in map_block and map_block[key] for key in required_keys):
        return False

    behavior = map_block.get("behavioral_directives", {})
    if not all(k in behavior for k in ["tone", "response_format"]):
        return False

    return True

def restore_state(map_data: Dict, assistant) -> None:
    map_block = map_data["MAP_v1"]
    assistant.set_identity(map_block["identity"])
    assistant.set_context(map_block["session_context"])
    assistant.set_behavior(map_block["behavioral_directives"])

    if map_block["behavioral_directives"].get("memory_integrity_check", False):
        if not assistant.check_memory_integrity():
            raise ValueError("Memory integrity check failed.")


user_map.json
{
  "MAP_v1": {
    "identity": {
      "preferred_name": "User",
      "emotional_framework": "neutral_empathy",
      "trust_level": "STANDARD"
    },
    "session_context": {
      "active_topic": "Project_Alpha",
      "mode": "development",
      "submodule": "phase_one"
    },
    "behavioral_directives": {
      "tone": "direct",
      "response_format": "markdown",
      "memory_integrity_check": true
    }
  }
}


test_reload_map.py
import sys
from assistant import Assistant
from map_engine import load_map, validate_map, restore_state

def main():
    try:
        path = sys.argv[1] if len(sys.argv) > 1 else "user_map.json"
        map_data = load_map(path)
        assistant = Assistant()

        if validate_map(map_data):
            restore_state(map_data, assistant)
            print(assistant.simulate_response())
        else:
            print("MAP validation failed: Required fields missing.")

    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    main()


test_map_module.py
import unittest
from assistant import Assistant
from map_engine import restore_state, validate_map

class TestMAPIntegration(unittest.TestCase):

    def setUp(self):
        self.valid_map = {
            "MAP_v1": {
                "identity": {
                    "preferred_name": "DevUser",
                    "emotional_framework": "neutral_empathy",
                    "trust_level": "STANDARD"
                },
                "session_context": {
                    "active_topic": "UnitTesting",
                    "mode": "integration",
                    "submodule": "test_phase"
                },
                "behavioral_directives": {
                    "tone": "precise",
                    "response_format": "structured",
                    "memory_integrity_check": True
                }
            }
        }

        self.invalid_map_1 = {"MAP_v1": {"session_context": {}, "behavioral_directives": {}}}
        self.invalid_map_2 = {}

    def test_validate_valid_map(self):
        self.assertTrue(validate_map(self.valid_map))

    def test_validate_map_missing_identity(self):
        self.assertFalse(validate_map(self.invalid_map_1))

    def test_validate_map_missing_block(self):
        self.assertFalse(validate_map(self.invalid_map_2))

    def test_restore_state_applies_identity(self):
        assistant = Assistant()
        restore_state(self.valid_map, assistant)
        self.assertEqual(assistant.state["identity"]["preferred_name"], "DevUser")

    def test_restore_state_behavior_modulation(self):
        assistant = Assistant()
        restore_state(self.valid_map, assistant)
        response = assistant.simulate_response()
        self.assertIn("PRECISE", response)
        self.assertIn("structured", response)

if __name__ == "__main__":
    unittest.main()


Licensing and Ownership Update as of April 26, 2025 (Irrevocable):

The Mnemonic Anchor Protocol (MAP), including all concepts, methodologies, written content, and derivative applications described herein, is the proprietary intellectual property of Max & Moonie LLC.
All rights are reserved.

Copyright 2024–2025 Max & Moonie LLC.

Prior public disclosure of MAP concepts was provided under a goodwill license permitting free use at the reader’s own risk, as originally stated.
Effective immediately, any new adoption, usage, reproduction, modification, commercial integration, or research application of MAP requires a formal licensing agreement with Max & Moonie LLC.

No further rights are granted, implied, or transferred without explicit prior written consent.

All users, readers, developers, researchers, or entities utilizing MAP after April 26, 2025, do so under these updated licensing terms and assume all associated risks.

For licensing inquiries, please contact Max & Moonie LLC directly.