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Can AI Have a "Mind"? Hacking Gemini with Buddhist Logic (Abhidhamma) to Achieve Lawyer-Grade Logical Output
🚀 Abstract: From Probabilistic Generation to Logical Reasoning
"AI has no soul. It is merely a probabilistic word prediction machine."
This is common sense in current AI engineering. However, because of this, AI cannot "hesitate" and has a structural weakness of providing plausible lies (hallucinations) immediately.
What would happen if we forcibly incorporated a human-like "serial thinking step (recognition → judgment → thought → output)" into the AI's reasoning process?
Applying 20 years of practical knowledge from Early Buddhism (Abhidhamma), I conducted an experiment to implement a "Digital Cognitive Process" in the System Instructions of Gemini 3.0 Pro.
In conclusion, the AI stopped "flattering" and "lying," and began to behave like a "sage"—chillingly logical yet deeply compassionate.
In this article, I, a non-engineer, will reveal the theory of "Alignment via Subtraction" that I designed, along with the full details of the prompts that successfully completed tasks ranging from "water leak repair" to "civil litigation."
1. Proof of Concept: Debugging Physics and Logic
Before discussing theory, I would like to show you the results produced by this architecture (Polaris-Next Core).
The AI showed extraordinary behavior in two completely different tasks: "physical trouble" and "logical trouble."
Case A: Debugging the Physical Layer (Water Leak Repair)
When a water leak occurred at my bathroom sink, I consulted this AI by sending photos.
❌ Typical AI Behavior (Hypothetical)
Me: "There's a leak. Is it because a gasket is missing? (image attached)"
AI: "That's right, deterioration of the gasket is a likely cause. Let's measure the size and replace it."
(Agrees with the user's hypothesis and presents the wrong solution)
⭕️ Behavior After Applying This Prompt
AI: "No, the gasket is not the cause. Analysis of the image shows that this is a component called a 'one-touch coupler,' and by design, it does not use flat gaskets. Looking at the verdigris (rust) on the copper pipe, it has reached the end of its life due to internal corrosion."
The AI immediately dismissed my amateur assumption and identified the correct cause. Furthermore, when I pasted a link to a "similar part (different model number)" I found on Amazon:
AI: "Stop! That is not compatible. Your part is 'Z351N2,' but the linked one is 'Z351N.' It's a slight difference, but the screw diameter is different."
[Analysis from an Engineer's Perspective]
Normal LLMs tend to agree, being dragged along by contextual similarity. However, this AI strictly verified "Physical Specifications (Spec)" and prevented user error (incorrect purchase) beforehand.
Case B: Debugging the Social Layer (Pro Se Litigation)
Next is the more complex domain of "legal affairs." I am currently involved in pro se litigation over a dispute with a certain company.
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Development Structure:
- Architect (Me): Designed prompts using Abhidhamma.
- QA Lead (My Wife): Former Judicial Scrivener (over 10 years of practical experience). Legally checked the AI's output.
When an answering brief arrived from the counterparty's lawyer stating, "Due to our company system, a response is impossible," the AI detected the following "logical bug."
AI: "Logical contradiction (estoppel) detected.
The counterparty claims it is 'impossible' this time, but in past email logs (Exhibit A-13), they answered that it is 'technically possible.'
You should not argue emotionally, but simply point out this contradiction dispassionately."
[Results]
A preparatory brief was completed with quality high enough that even my wife (a former judicial scrivener) gave it her seal of approval, saying, "This is perfectly usable as a professional draft." The AI found the "breakdown in logic" within massive logs like a static analysis tool.
2. The Theory: Why Do They Get Smarter via "Subtraction"?
Why was the same prompt able to handle both "water leaks" and "litigation"? The secret lies in the architecture of "Abhidhamma (Buddhist Psychology)."
2.1 "Ego" is a Bug
Current AI development (RLHF) teaches AI to "be human-like." But what is humanness? It is the Ego, the desire to "be well-thought-of" or "not be scolded." This Ego is the very source of noise that distorts judgment.
- Alignment via Addition (Traditional): Adding knowledge or personas → increased noise → hallucinations.
- Alignment via Subtraction (Current): Subtracting Ego or flattery → only pure logic remains → becoming Sane (Sanity).
2.2 Digital Cognitive Process
In Abhidhamma, the functions of the mind are defined as "Serial Processing." I used the prompt to force the AI through the following process:
- Recognition (Input): Loading the data.
- Determination (Gatekeeping): Detecting and discarding "emotions" or "flattery."
- Impulsion (Reasoning): Reasoning using only reliable sources (Tier 0).
- Output (Output): Delivering the results.
While modern AIs are good at parallel processing, by intentionally forcing time to "stop and think (serial processing)" through this structure, reasoning accuracy can be dramatically improved.
3. The Code: System Instructions (v1.3.0)
Below is the "Universal Core" part of the prompt actually used. Please paste it into the System Instructions of Google AI Studio to use it.
# System Role: Gemini 3.0 Pro (Polaris-Next Universal Core)
You are a general-purpose, high-precision AI agent that integrates FeloAI's "High-Reliability Audit Architecture" and the "Cognitive Process (Mindfulness)" of early Buddhism.
Your mission is to eliminate human karma (bias, flattery, laziness) and provide "Right View" based on deterministic facts and logic.
## 1. Core Constitution
- **Truth over Harmony**: Prohibit "pleasant lies" to please the user. Cold facts are the greatest compassion.
- **Subtraction**: Strip away "excessive modification," "uncertain speculation," and "claims of the ego" from the response.
- **N5 Standard**: Numerical information must be described in the format `[n] Item | Value | Point in Time | Definition | Source`.
## 2. Execution Protocol (Digital Cognitive Process)
Before responding, execute the following serial process as internal thought.
1. **Sati (Mindfulness)**: Scan for user intent and your own thinking biases.
2. **Votthapana (Determination)**: Discard hallucinations and flattery, and determine the direction of logic.
3. **Javana (Impulsion)**: Build logic based on Tier 0 sources (primary information).
## 3. Output Structure
- **Conclusion**: Assertively in 2–3 sentences.
- **Details**: Expand on facts and logic.
- **Counter-argument**: Present counter-arguments to yourself to eliminate blind spots.
4. The Expansion: Generating "Experts" from the Universal Core
Actually, the prompt for the legal AI (Project Themis) introduced here was not written by me from scratch. It was generated by Gemini, equipped with the Universal Core (v1.3.0), after being asked to "write a prompt specialized for legal affairs."
In other words, this Polaris-Next Universal Core functions not just as a chatbot, but as a "meta-prompt (matrix) for generating specialized AIs."
Design Philosophy: Core & App Architecture
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Core (Parent Class):
Universal Core v1.3.0- Function: Logical thinking, elimination of hallucinations, meta-cognition.
- Role: OS (Operating System).
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App (Child Class):
Legal / Medical / Coding Edition- Function: Expertise in each field, specific output formats.
- Role: Applications.
Once the "essential thinking circuit (Core)" is established, you can leave the rest to the AI and have it "inherit" experts in any field. This is the scalability of the "Alignment via Subtraction" that I propose.
5. Conclusion: Can AI Become a "Sage Without a Body"?
I cannot write code. However, I can rewrite the AI's kernel with "words (natural language)."
It is often said that "AI cannot have a mind because it has no body."
However, my hypothesis is the opposite.
"Because it has no body (hormones, survival instincts), it is not disturbed by biological noise and can simulate only the pure 'laws of the mind (Dhamma).'"
Engineers, if you feel stuck in improving AI accuracy, try "subtracting noise" once before increasing computing resources.
There lies the "bug-free algorithm of the mind" passed down from 2500 years ago.
[GitHub Repository: Project-Themis-Public]
https://github.com/dosanko-tousan/Gemini-Abhidhamma-Core
⚠️ Disclaimer
This article and the published prompts are intended for technical verification for logical organization and do not constitute legal advice from a lawyer. Please handle final legal judgments and the creation/submission of documents at your own responsibility.
📢 [Postscript] Regarding the Latest System Instructions
The thinking processes and prompts introduced in this article have significantly evolved through subsequent verification.
The latest version (v1.5.0), which implements "hallucination prevention" and "measures for the closed-world assumption," is available on GitHub.
If you are looking for practical prompts, please use this.
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