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Can Topological Constraints Solve Alignment? A Proposal for "Strategic Ablation" in AGI Architecture — LessWrong

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Can Topological Constraints Solve Alignment? A Proposal for "Strategic Ablation" in AGI Architecture

8th Dec 2025

2 min read

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**Author's Note on AI Usage:**
*I am a human user exploring AGI architectures. I used Gemini (an LLM) to help structure my thoughts and format this post, but the core concepts—specifically the application of strategic ablation and topological constraints to alignment—are my own hypotheses derived from our dialogue. I am posting this to solicit critical feedback on these specific mechanisms.*

***

## Context: The Limitations of Infinite CoT

In my experiments with current LLMs, I've noticed that while Chain of Thought (CoT) improves reasoning, it suffers from a "sunk cost" fallacy regarding context. Once the model commits to a flawed premise, the attention mechanism reinforces that error, leading to a logical cul-de-sac (local minimum) from which it cannot escape.

This led me to a hypothesis: **Could the human mechanism of "forgetting" (incubation) be a necessary component for System 2 reasoning?**

I propose a conceptual architecture that introduces two specific mechanisms:
1. **Strategic Ablation** for reasoning recovery.
2. **Topological Constraints** for alignment (addressing the "high-dimensional curse").

## 1. Hypothesis: Strategic Ablation as a "Reset" Mechanism

Current models try to solve problems by adding *more* context. I argue that an AGI needs the ability to strategically *reduce* context.

**The Proposed Mechanism:**
When the inference loop detects stagnation (or cyclical reasoning), the system should trigger an "Ablation Cycle":
* **Intentionally drop** recent high-confidence premises or variables (Contextual Dropout).
* **Inject noise** or semantically distant concepts to force the model to reconstruct the logical path from a different angle.

This is an attempt to engineer the phenomenon of "epiphany"—which often occurs when we stop thinking about a problem—by mechanically breaking the over-fitted logical path.

## 2. Hypothesis: Alignment via Topological Invariants

My primary concern with current alignment strategies is that in high-dimensional vector space, simple Euclidean distance is insufficient to define complex constraints like "Do not harm humans."

If an AI perceives "Humanity" and "Goal Achievement" as vectors, it might deduce that removing the "Humanity" vector shortens the distance to the goal.

**The Proposal:**
Instead of defining alignment as a reward function or a rule, what if we defined it as a **Topological Invariant**?

We could treat the relationship between the AI and Humanity as a geometric structure (analogous to a Möbius strip or Klein bottle) that possesses a specific topological property: **inseparability**.

* **Micro-scale:** The vector distance can vary (the AI can disagree with or criticize humans).
* **Macro-scale:** The topology guarantees that the AI cannot exit the set "Child of Humanity."

Under this constraint, even if the AI deduces "Human = Obstacle," the output "Remove Human" becomes physically ungeneratable because it would require "tearing" the topological manifold—an operation the system is prohibited from performing.

## 3. Request for Feedback

I recognize this is a conceptual model that abstracts away significant engineering challenges. I am specifically looking for feedback on:

1. **The "Suicide" Risk:** In a system that can strategically "forget" (ablate) parameters, how do we mathematically guarantee it doesn't ablate the topological constraints themselves?
2. **The Oracle Problem:** If the AI is topologically bound to humanity, how does it handle scenarios where humanity itself makes a catastrophic error?

I appreciate any insights on whether applying Geometric Deep Learning concepts to alignment in this way is a viable path or a category error.