Specification (RFC)

The core technical definition of the L1-L4 architecture.
Origin Story

How MAP emerged from cross-disciplinary thinking.
The Lore

Theoretical intersections with Chiral Network physics.

The Manifold Alignment Protocol (MAP)

A Unified Geometric Standard for Complex System Dynamics

Yunchong TANG
Faculty of Engineering, Tohoku Institute of Technology
Protocol Spec (RFC) Foundational Paper GitHub Ethical Use & Safety Disclaimer

The Paradigm

In an era of emergent AI and complex physical systems, reductionism fails. We cannot understand a Large Language Model or a chaotic RF environment by dismantling it gear by gear.

MAP proposes a Cybernetic alternative: accepting the "Black Box" (L1) as is, and focusing on characterizing its behavioral geometry. MAP asserts that any complex optimization process can be understood as a trajectory moving through a state space. The goal of the protocol is to project these invisible trajectories onto a visible interface, allowing human operators to steer the system towards desired outcomes without needing to reverse-engineer the substrate. MAP is offered as a complementary lens to existing mechanistic studies, focusing on steerability and holistic dynamics.

Protocol Architecture

MAP defines a system through four abstraction layers. A system is "MAP-compliant" if it exposes its internal state through this hierarchy:

  • L4 Interface (The View): Visual metaphors (Funnels, Walls) accessible to human perception.
  • L3 Alignment (The Geometry): Descriptive structures like Attractors, Basins, and Curvature that define stability.
  • L2 Dynamics (The Flow): The laws of motion (Drift, Diffusion, Kinematics) governing state evolution.
  • L1 Substrate (The Reality): The raw, high-dimensional state space (Weights, Latents, Voltages) — the Terra Incognita.

Protocol in Action: Profile C (Cognitive Semantics)

Validating MAP dynamics using Large Language Model reasoning trajectories.

Geometric Convergence

Convergence (L3): Visualizing the thought process. Divergent inputs naturally converge into a single 'truth' basin, validating the geometry of reasoning.

Architecture Analysis

Dynamics (L2): MAP metric ΔA reveals distinct cognitive styles. Llama-3 discriminates semantics early, while Qwen-2.5 maintains a high-abstraction 'superposition' state.

Safety Topology

Safety Topology: Adaptive Mode (guidance) shows a stable U-turn (soft guide), while Rigid Mode (refusal) exhibits an inelastic crash (hard barrier).

Reference Implementations

The protocol is substrate-independent. We provide official implementations validating MAP across the complexity spectrum:

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Profile A: Physical

GAGC (MAP-SDR)

L2/L3 controller for RF Signal Processing. Uses Discrete Curvature to eliminate signal overshoot and identify noise floors.

View Code(Comming Soon)
🎨

Profile B: Generative

MAP-ComfyUI

Geometric optimizer for Stable Diffusion. Uses Q-Score (L3 Metric) to auto-tune Steps and CFG in latent space.

View Node
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Profile C: Cognitive

MAP-LLM-Toolkit

Visualization suite for Discrete Semantics. Extracts hidden-state trajectories to map reasoning funnels.

View Toolkit

BibTeX

@article{tang2025map,
  title={Manifold Alignment Protocol (MAP) Specification},
  author={Tang, Yunchong},
  journal={Zenodo},
  year={2025},
  doi={10.5281/zenodo.18091447},
  url={https://doi.org/10.5281/zenodo.18091447}
}