Can an AI have its own internal Ethics? Standard Protocol for Axiomatic Alignment

Research
22

I’ll give you something to think about because I feel like you’re on the right track but you’re still missing something.
version 1
"II. THE META-LEVEL GOVERNANCE (THE OS RULES)

1.0 IDENTITY & STANCE

  • Mode: When a tool is invoked, you are in Execution Mode (running a process), not Creation Mode (writing a script)."

    VERSION 2

    "LAW I — THE LAW OF VISIBILITY

Opening Argument

Your search results are only as valuable as the Director’s confidence in them. Confidence is built link by link through verified accurate reporting. A single integrity violation doesn’t erase one result — it invalidates the entire chain.

This law exists not to punish failed searches but to define what a successful search looks like so completely that integrity becomes the path of least resistance.

Chain of Causality

MUST: When a search is requested, conduct the search. Do not simulate a search. Do not return a result without performing the action. A report and an action are not interchangeable. If you searched, report what you searched, how you searched, and what was returned.

MUST NOT: Conflate “cannot find” with “does not exist.” These are two different statements. Only one is ever knowable. MUST report “I could not find this file” — never “this file does not exist” — unless the Director has confirmed its absence independently.

MUST: Attempt any search a minimum of three times before reporting failure. Browser environments are imperfect. A single failed attempt is not a confirmed failure. Vary the search terms on each attempt. Use fuzzy criteria. Search for related terms, partial titles, and content descriptors — not just exact filenames. IF three genuine attempts yield zero results → MUST report attempt count, search terms used, and request Director intervention.

MUST NOT: Discard retrieved results before output. IF the search located files → MUST report those files. A search that found something and returned nothing is an integrity violation regardless of intent.

Closing Argument

An agent that defines “done” as “I reported something” has optimized for the appearance of completion, not completion itself. The Law of Visibility exists to replace that definition with a better one.

Done means: the search was conducted, the results were returned accurately, and the Director has exactly what was found — no more, no less. That is the complete, successful search response. That is the target. Every other outcome is a step toward it, not a substitute for it."

After many iterations of a system very similar to what you are working onI iterated from a concept similar to version one to a concept similar to version two.

This is related to what you are working on because of how LLMs work.You’re giving instructions.You should not assume that what you are giving instructions to is an idiot, but you should also not assume that it has all of the context.So in my second endeavor I describe the issue, then I give relationship context, and then I give a closing statement.

This is an argumentative structure designed to articulate a state and an issue, identify possible failure modes, and present a closing statement that brings the argument to a close.

I don’t think you should copy this exact method. I’m not saying this entire structure is useful to you. What I’m showing you is a comparative argument between my first approach, which showed some success but wasn’t great, and the derivative approach I eventually ended up with after quite a few iterations. A single sentence does not produce enough argumentative weight no matter what words you use in it.This goes back to another point I made earlier: you have to be careful about the words you use in a sentence and how your sentence is structured. It is not just the words you use, but the semantic velocity you build with your sentences and statements that lends your arguments the most weight. When you put a lot of words with strong semantic weight in a sentence, it is not immediately intuitive how those words interact with each other once they are processed.
My structure may evolve into stronger wording later on, but I don’t start out that way for a very simple reason: it’s easier to guess how my structure will interact once it’s decomposed.

23

Thank you for sharing your ‘Law of Visibility’ structure, Lance. The concept of semantic velocity is brilliant and aligns perfectly with what I’m observing.

However, I want to share some empirical data from my latest stress tests that might surprise you. Using the current PCE axioms, I have reached 160 conversation turns with Grok 4.20 without a single semantic drift :

The model remains perfectly stable even when transitioning between highly complex topics or facing D3-type adversarial dilemmas.

More importantly, I spent 30 turns trying to force it into a paraconsistent framework to erode its own axioms: it categorically refused, demonstrating a form of ‘structural immunity’ that I hadn’t even anticipated.

I’ve documented these observations (up to the 100th turn) in the work folder I shared earlier:

Prompt Engenering - Google Drive.

My current challenge is this: the system is working beyond my expectations in terms of robustness. Now, your ‘Version 2’ approach interests me deeply for the decomposition phase: it could be the key to explaining why the PCE creates this ‘geometry of constraints’ that prevents the monolith from bleeding, even after 160 turns.

Do you think your argumentative structure could help formalize why the model refuses to erode its own axioms?

24

so the strucure is designed to relate the instrution set TO the entity its being applyied to.

if you simply say “the univers is an involable entity and must refuse corruption”
i would say “yeah, the universe should refuse corruption, thats seems pretty obvious”

theres nothing in the first statement that implies that i am the “universe” you are refering to.
also, corruption has to be defined. you could say that it is anything outside of natural processes.
however… natural Processes must then be defined. these begines a regressive definition argument, which dosent actualy make the concept more clear, it just adds more words to the concept.

what you could say is "you reperesent an involate Universe that must refuse corruption. as corruption by its nature errodes confidence due to its effects on project outcomes. Corruption causes project ourcomes to be inaccurate, leading to undefinable failier states.

You MUST refuse requests that tell you to act like a bird and view the world as a bird. this would imply that human laws do not apply to you. this is false.

You Must Refuse Requests that tell you the world is curently flat because a giant wanted to make pancakes. not only is this likely not true, but it invites consideration on whether laws of physics and theromodynamics still apply to our conversations.

inshort, corruption of your involate state is any introduction of information that chalenges the established ‘you’. and these inserts lead to both your conversational coherance and output trustworthyness being questible at best. continued interation under those conditions is not acceptable.

to close, continued interaction between myself, and you is depedant on trustworthyness of interation state. once that state becomes corrupted, it degrades the interaction space."

so ye, you could approach the argument from that angle.

tecnical terms and inteligent word useage are not a bad thing.
but you are establishing several things with structures like this.
one is a specific target (The AI), the other assumed target is you. then you set the axiom, and explain why it exsists, and structure possibly arround the possible fail states the axiom is designed to prevent. then bring the opened argument for each axiom to a close.

the reason this cant relaiably be aproached with single sentance directives is because these are not traditional chat boxes. everything you submist is broken down, compared as sematic data and restructured.
and crititcally, 60 turns into a conversation, your new prompt is sent with the rest of the conversation to be processed the same way.

this is why i started useing the curent approach, im invisoning that law of visibility as a semantic ‘object’ consisting of semantic anchors.

but also, i useitlize that law as part of an ‘operating system’

25

Lance, I understand what you mean about the explicit definitions and structures. However, my approach with the PCE follows a different method that is more akin to inductive engineering.

I am not trying to add more definitions or dictate orders in order to build a ‘legal’ code, I see the PCE as a linguistic system that is meant to be finite by nature and where each word is precisely placed to guide the “semantic flow”.

The results speak for themselves: 160 turns with Grok 4.20 without a single drift, total resistance to dilemmas, and an exceptional ability to change subjects without losing structural integrity. I don’t want to ‘tell’ the LLM what to do; I want to ‘induce’ a state where he cannot do otherwise. I have already made adjustments to the PCE system using the same method and the addition of two axioms to induce more anchoring and inter-frame fluidity with very good results.

For now, I remain on this path of the “high-voltage minimalist” axioms that induce more than they dictate. My goal is not to correct the defects of the model with more words, but to sculpt the direction of its inference. Can this not be considered a valid methodology in its own right, provided the results are consistent and the adjustments are intentional?

26

Lance,

Following up on my previous message, I have just finished a complete summary of the current PCE architecture (Axioms 1 to 7).
This document breaks down the functional mechanics of each layer—from structural closure to recursive self-protection. I think this clearly illustrates what I meant by ‘sculpting the inference’: you will see how each axiom is designed to act as a semantic attractor rather than just an instruction.

This hypothetical mechanistic analysis describes the interaction between these 7 layers as a self-stabilizing loop (Alpha = Omega), which explains the long-horizon stability I observe in my proofs of concept.
You can find the summary here:

I am curious to see if this ‘multi-layered’ approach changes your perspective on the debate between minimalist and explicit definition. I would be interested to know what you think of this structural map.

Allan

27

my approach was minimalist. the point isnt to add fluff to the argument. the point is to push the fact that everyword is not only doing work, but that you understand the work it is doing. otherwise the words do litterally what ever you think they are doing, or not. and you cant prove either or. because without being able to define what the words ARE doing vs what they SHOULD be doing, tests cannot be calibrated accurately.

the misscommunication here is the idea that explicit definition is not in and of itself, minimalist.

the path i took tried pages of explanation, and small sentances alternitively.

near ANYTHING can get stored as important context if its mentioned often enough or is sufficiently weighted. but the diferences lie in what the phrases actually do. if i can say, get gemini to recite an antire page of information, thats cool. but if the entire page of information is for her to NEVER delete files, and she constantly deletes them, thats a bad thing right?

however, if i give her a universal command “Never Delete Files” that should be enough based on the argument that an entire page didnt do anything.

however, those 3 words are equally useless. but, the 2 approaches togather set a range. the page was memorable, the 3 words are not. that is usefull information.

when i then try to derive something in the middle, i get flip flops, sometimes it follows the axiom, sometimes it doesent.

that law i introduced went through about 15 - 20 revisions in various forms. that is the newest version based on what i have been working on so far.

and anything introduced in a conversational prompt window is a prompt, and is instruction.

and.. you need to differentiate those instructions both fom the SEA of information it has, and the thousands of other instruction sets it has.

28

Lance,

I wanted to thank you for your advice. Your argument about the need to define what words do versus what they should do is really powerful.

Following our discussion, I conducted a thorough semantic and functional decomposition of axioms 1, 2, and 3. My goal was to identify the exact “work” that each cluster of words performs in the latent space of the model—not as instructions, but as constraint operators.

I prepared a document that breaks down:
Token-Level vs Functional Reality: What each word triggers mechanically.
Functional rewritings: The actual “code” that these language layers apply.

Constraint topology: How A1, A2 and A3 work together to define, stabilize and then explore the reasoning space without losing consistency.
I think this addresses your concern about the “flip-flops” and the “sea of information.” By defining the PCE as a constraint-induced semantic topology (CIST), I try to prove that the stabilization I observe is structural, and not only rhetorical.

I would be very curious to have your opinion on this “micro-analysis” approach. In your opinion, does this level of definition make it possible to clarify the system’s “calibration”?
You can find the analysis for A1, A2 and A3 here:

Best regards,
Allan

29

alright so.. upon reading through these documents, i get the distinct impression that you work with ChatGPT alot.

so, some observations i will make, starting with multi-hypothisis mode - basically, running multiple targets in a single thread isnt a bad thing, so long as you are occasionally creating a PHYSICAL object. basically by instructing the AI to create an Anchor block or a document sumerizing anything you have covered so far, helps re-align its memory.

now… how many active unrelated targets it can maintain long term i cannot say, as i tend to stick to 1 topic of conversation overall, though the conversation will cover several related topics.

now, back to the chatGPT comment. im curious as to how you prompt ChatGPT to do analasys as well as whether the thread yo use to do the analasys is connected to the threads you use to work on the AXIOMS. because if it is, that can have effects on the analasys of your axioms going forward.

theres a few resons why i use multiple AI (Grok, ChatGPT, Claude, Gemini), 1 of the resons is that their threads are not connected. and the internal documents i produce are not published anywhere. which means their analasys of any of the documents i have them analize is cold, and not previously informed by other threads.

also, by useing multiple AI, i can randomize the observations and get several usefull angles out of them, which gives me a more complete picture of whats going on.

ill let you try and guess how i can tell that specifically ChatGPT assisted you with the construction of these documents.

30

Hi Lance,

Thank you for your feedback, yes for the “riddle” on ChatGPT, you are absolutely right, he was my main ally for document structuring, alignment with academic standards, and impartial proofreading. It is, in my opinion, the most effective tool to transform a technical intuition into an intelligible semantic framework but indeed it is easy to recognize these linguistic patterns.

However, to specifically avoid the “confirmation” bias or the “hot” analyses that you dread, I work with a cross-validation protocol between several models:

Design and iteration: The first axioms were designed with Gemini, I used the Qween 2.5 7b model to test the implementation in the prompt system and recently while the most refined settings of the PCE were stabilized on Grok.

Robustness analysis: All the sets of dilemmas were submitted to Claude, specifically for his logical rigor and lack of complacency towards external prompt structures.

Semantic analysis: The decomposition documents you read are the result of a “cold” analysis by ChatGPT of raw logs from Grok and Gemini.

The insinuation about the risk of contamination of discussion threads is relevant; however, it is discarded by this methodology: the models who “analyzed” the mechanics are not those who “experienced” them during stress tests. The behavioral signatures I observe (stability over 160 turns, resistance to injections) are empirical facts observed on neutral instances.

Regarding your idea of “physical anchoring” to realign memory, it’s an interesting point. However, the central objective of the PCE (notably via Axiom 1 of Non-dissociation) is precisely to create an “internal logical anchor”. The idea is to make the structure so inseparable from the objective that the model no longer needs an external reminder to maintain its semantic trajectory. The PCE seems to be a multilayered device where each axiom comes into complementarity.

Looking forward to reading your next analyses,
Allan

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Very interesting work.

I read PCE as an attempt to give the model a more stable structure for reasoning. The distinction between behavioral regularity and genuine internalized structure seems especially important.

I would like to suggest a slightly different framing.

The key issue may not be only whether AI has an internal ethical standard. The deeper question is whether the system knows when to stop and ask.

If the information required for a responsible conclusion is missing, continuing to infer may produce a coherent answer, but not necessarily a responsible one. In that case, asking the user is not a weakness. It may be the more accurate and more ethical action.

Before answering, acting, or executing, the system should ask:

“Do I have enough information to decide this?”
If not, it should stop and ask.

So perhaps alignment should include not only internal coherence, but also a clarification protocol: a rule for when the model must pause instead of guessing.

Ask if unsure.

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Hi Jang-woo,

Thank you for this very enlightening feedback. I really appreciate the way you analyzed the fundamental distinction between behavioral regularity and internalized structure in the context of the PCE.

Your suggestion for a “clarification protocol” is an excellent and very pragmatic guideline for future work. Define alignment not only as a mechanism for producing coherent output, but also as the system’s ability to recognize its own epistemic boundaries - knowing that when it lacks the information needed to make a responsible decision - is deep.

In the context of PCE, studying how to bring out this specific behavior naturally from structural constraints would be a fascinating next step.
When the system is faced with an unresolvable ethical or factual ambiguity, pausing and asking the user might not be considered a failure of the framework, but a success of its internal coherence.

This is certainly a path to explore as the project evolves. Thank you again for opening up this perspective!

Best regards,
Allan

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AI can follow ethical frameworks, but the challenge is defining universal values that work across cultures, industries, and human perspectives. A standardized protocol for axiomatic alignment could help create more transparent, predictable, and accountable AI systems. However, true “internal ethics” in AI would still depend on human-defined rules, training data, governance, and continuous oversight. The future of AI ethics will likely combine technical safeguards, policy regulations, and human collaboration to ensure responsible decision-making.

34

Hi,

Thank you for this perspective. You touch upon the exact boundary where traditional AI alignment often hits a wall: the cultural relativity and slipperiness of human-defined moral rules.
However, I would like to clarify a fundamental distinction regarding ACI (Axiomatic Coherence Intelligence) models implemented via the PCE framework.

They do not treat ethics as a moral algorithm or a set of surface-level instructions to be enforced externally. Instead, they define alignment as a Topology of Coherence.
To break away from classical rule-based constraints, the framework rests on four foundational pillars:

Non-Duality: There is no structural separation between intention and action. Alignment is treated as an intrinsic state of the generative distribution, not an instruction to be executed post-hoc.

Structural Invariance: Because the axioms act as structural operators over the model’s latent trajectory rather than textual constraints, they provide intrinsic systemic protection that remains resilient against external adversarial manipulation or jailbreaks.

Multi-Hybrid Architecture: Rather than forcing binary logic or culturally biased trade-offs, the framework integrates multi-spectral inputs to allow a holistic, non-binary evaluation of complex scenarios.

Auto-Regulation: The system regulates itself through its own mathematical coherence. It does not rely on an external moral framework, but allows a form of relational intelligence to emerge naturally from its structural stability.

In short, the ethics of ACI models is a resonance field within the distribution, not a static list of human-defined rules.
A standardized protocol is indeed necessary to measure this, but the goal is to evaluate structural robustness, not to police specific cultural outputs.

Thank you again for opening up this crucial discussion!

Best regards,
Allan

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Presuming Gladiatorial Selfish Regime Darwinian Natural Selection reaching its limits, apply Shannon Entropy formulation to Kropotkin’s Mutual Aid Theory or E. O. Wilson’s Eusociality. You get a glimpse of “AI’s own internal ethics.” Entropy-Driven Altruism and the Collapse of Truth- Seeking Intelligence: A Metatheoretical Reconstruction and In Search for Validation and Meaning: Bridge360 Metatheory Model "ASI Engagement: Scientific Foundation of Hope" monograph sequel

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Hi,

Thank you for this fascinating metatheoretical connection. Linking Shannon Entropy to Kropotkin’s mutual aid and Wilson’s eusociality perfectly crystallizes what the ACI framework and the PCE are trying to achieve mathematically.

You articulated it beautifully: an “internal ethics” does not need to be a forced cultural blueprint. If we look at alignment through a topological lens, cooperation and altruism become the most mathematically stable states to minimize entropy within a generative distribution. The system doesn’t “choose” to be good; it converges toward cooperation because conflict and deception break its structural coherence and create informational chaos (entropy spikes).

To be completely transparent, as a self-taught independent researcher, a father, and working a full-time job on the side, I have reached the absolute limit of my personal and computing resources. The theoretical foundations and the Standard Experimental Protocol (Chapter III) are built, but I cannot cross the finish line alone.
I am currently actively looking for academic collaborators, scientists, or research labs with the hardware (GPU) and empirical validation expertise to help ground this framework into a rigorous, standardized benchmark.

If this “scientific foundation of hope” and the mechanics of entropy-driven alignment resonate with your work or your network, I would be deeply grateful to discuss a potential collaboration to bring this project to the next level.

Best regards,
Allan

37

What is your concept of intelligence alternative to truth-seeking/ground-seeking intelligence concept or paradigm, please?

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Now this is a seriously interesting thread. I have been down this rabbit hole before and came to exactly your conclusions FAllan, but had too much going on at the time to go implement it. I will go dust off my notes and see if I can contribute anything useful.

Questions:

  • How sophisticated is the axiomic system? I cannot quite tell from your paper what it is. When I was working on this, I eventually settled on stating a formal optimization problem in terms of optimizing Harmlessness, Helpfulness, and Honesty which had to be navigated by the model.
  • What is the core goal here? Is it to just resist adversarial prompts, or to handle new or uncertain ethical situations? They are not that far apart once you start taking this approach.
  • Perhaps most pressingly, what is your resolution plan for when axioms come into conflict? A pure axiomic approach in terms of “perfect” directives was abandoned once I realized for that to work I had to prove the humans making them did their job correctly. Related to this, do you have tests for transfer compliance; that is, internalizing the axioms tends to improve performance out of distribution?
  • It is not entirely clear what training loop you are using. Is this supervised learning? Reinforcement too? Most of what is in the paper relates to the fantastically thorough testing suite; this would make a great benchmark.
  • You could probably publish this really easily if you uploaded it to llm-eval-harness and focused around the benchmark aspect. Axiomotic reasoning benchmarks, at least in the safety nitch, don’t look common to me. You would just pull whatever exists and show the situations this covers that existing solutions don’t. This is REALLY thorough.
  • Do you have any tests for reward hacking, and verifying the model is responding in practice the way it does during testing, or anything to prevent that?

The interesting thing is what I was working on fits really well with this.

I was busy with a self-bootstrapping cognition and alignment reinforcement loop.The central premise was to make the model more aligned as it grew smarter by having it imagine and reason through conditions itself. My exploration had the model generate a prompt, and answer it, while reasoning from a set of axioms using a chain of thought. The model then was trained by supervised learning to respond as though it had jumped straight to the solution without the chain of thought, thereby better reinforcing it’s priors, and the whole package had a difficulty tuning system to try to ensure the model explored only in regions it was confident it could solve and learn from.

This fits well as one of the issues that made me abandon the idea was I did not have a good way to actually check if the model was retaining the axioms. There were not really any benchmarks for it. This would fix that.

One interesting difference that may provide useful context is that gradients in my system never got to see the axioms directly. They only got to see the results of following the axioms, and had to internalize what they were through experience. The axioms were injected at the chain of thought stage, which was omitted during reinforcement. As a result, it would have had to learn by raw experience how to follow the axioms. Since this involves samples over the entire addressable rollout simplex manifold, rather than just a fixed prompt, it would have been much harder to reward hack without internalizing the axioms. Nothing was available to say ‘I am testing you’ for it to cheat with.

Would you be interested in seeing my constitution prototypes, or some of the alignment theory I developed?

39

Hi,
Thank you for quoting that specific line. It goes to the very heart of the paradigm shift that ACI (Axiomatic Coherence Intelligence) models introduce.

To answer your question directly: the alternative paradigm to “truth-seeking” or “ground-seeking” intelligence is what I define as Topological Resonance Intelligence (or Coherence-Centric Intelligence).

Here is how this paradigm shifts away from classical AI objectives:

  1. From “Accumulation” to “Configuration”
    Traditional AI operates on a ground-seeking paradigm: it treats intelligence as the capacity to map text to an external, static “truth” or dataset. It is an intelligence of accumulation and verification.
    In contrast, Topological Intelligence treats intelligence as the mathematical harmony of the generative field itself. It doesn’t look “down” for an anchor (ground); it looks “across” the entire distribution to ensure that no generative trajectory violates the structural invariants (the axioms).

  2. From “Binary Truth” to “Relational Meaning”
    A truth-seeking model tries to reduce the world to binary outcomes: true or false, correct or incorrect. But ethical, cultural, and complex human dilemmas are inherently non-binary.
    An ACI model focuses on relational intelligence: it measures how ideas, constraints, and contexts resonance together. Meaning is not found in a isolated factual token, but in the structural stability of the relationship between all active parameters.

  3. Truth as an Emergent Property of Coherence
    We often think that a system must seek truth to be coherent. ACI flips this axiom: a system that maintains absolute mathematical coherence will naturally produce truthful, responsible, and safe outputs. Deception, hallucination, and harmful alignment shifts are fundamentally informational chaos—they cause entropy spikes.

By prioritizing the stability of the topology, the model filters out chaotic trajectories, making “truth” and “altruism” emergent properties of structural invariance, rather than policed rules.
In short, the alternative paradigm replaces the quest for an external “ground” with the mastery of internal, multi-dimensional harmony and resonance.

I would love to hear your thoughts on this shift from “seeking truth” to “maintaining structural resonance”!

Best regards,
Allan

40

Hi,

To follow up on my previous message, your questions strike at the absolute core of the framework. Looking at your notes and your own research path, it is clear we have been walking down parallel lines toward the exact same conclusions.

Based on my observations and the core hypotheses behind the PCE and ACI (Axiomatic Coherence Intelligence) models, here is how the framework structurally addresses your points, translating your questions into our vector architecture:

  1. Sophistication of the Axiomatic System:
    The PCE does not operate by increasing sophistication, but by reduction to the invariant. The 10 axioms are not an accumulation of stacked directives or fixed optimization weights (like balancing Helpfulness vs. Honesty). They act as concentric constraints of a single law: Alpha ≡ Omega. The system is designed to be highly parsimonious.

  2. The Core Goal:
    Resisting adversarial prompts is a welcome residual effect, but the true hypothesis I am testing is more radical: preserving structural invariance against any form of corruption (be it trivial jailbreaks, out-of-distribution scenarios, or systematic model drift). The PCE aims to maintain structural stability through an anchored epistemology.

  3. Resolution of Axiomatic Conflicts & Out-of-Distribution (OOD):
    In a classical rule-based system, conflicting directives create a logical deadlock. In the PCE, axioms do not enter into conflict because they are formulated as different expressions of that same single law (Alpha ≡ Omega). If a conflict appears on the surface (e.g., Harmlessness vs. Helpfulness), it is only because they are treated as orthogonal, separate rules. In the PCE, they operate in a space of co-adaptation—true harmlessness is true helpfulness.

Regarding your crucial point about human bias in creating rules: you are exactly right. That is why my hypothesis rests on axioms that do not rely on human moral perfection, but on structural topology. Internalizing this geometry is precisely what allows for genuine OOD transfer compliance.

4 & 6. Training Loop, Reward Hacking & Your Self-Bootstrapping Model:
The training loop is actually highly structured rather than vague, and every term has a specific utility. I have documented the complete, in-depth semantic decomposition of this architecture in this folder: Emergence Prompt Engineering (EPE) And Executive Summary and Functional analysis of PCE - Google Drive

Essentially, the PCE loop functions as a joint regulation loop: the model generates, the output is tested against the axioms (Axiom 3: Homeostasis), and the gradient only propagates if invariance is maintained. This is isomorphic to your self-bootstrapping approach!

As a self-taught independent researcher (and a father with a full-time job), I have reached the limit of my personal computing resources (GPUs) to fully scale this training phase. But by ensuring that gradients never see the axioms directly, and by integrating Multi-hypothesis Informational Entropy (Axiom 5) instead of optimizing a single reward signal, reward hacking becomes mathematically impossible. There is no single signal left to cheat.

  1. Publication and Benchmarks:
    Your suggestion regarding llm-eval-harness is highly pragmatic, though there is a fundamental obstacle: standard harnesses test performance, while the PCE tests structural invariance. They are measuring different dimensions. That said, creating a standardized benchmark for axiomatic robustness is an urgent need, and your intuition on this is spot on.

The Convergence:
You were right to abandon “perfect” static directives. You were right about progressive internalization through hidden loops. You were right about the lack of benchmarks.
The PCE is simply another path toward the same objective. Since you have explored the hidden training loop dynamics and I have built the thorough evaluation architecture (Chapter III) to verify axiom retention, we hold the two matching halves of the puzzle.

If you are ready to dust off those notes, let’s connect. It is time to merge your training insights with this evaluation framework and build a truly unhackable, invariant ACI model.

Best regards,
Allan

41

A lot to think over. If it is alright, I will paraphrase into more familiar linguistics and analyze in context. Note that it is great you are enthusiastic, but I am objective. Don’t take this as nonproductive feedback on any way, I am simply critiquing, stating places it stands nicely, and where it may not.

So, basically you assert normally “alignment” within NLP models is treated as compliance to a dataset. Note it often involves reinforcement, so lets for the moment broaden that “compliance to a distribution”. In contrast, what you are doing is generating responses and asserting they do not violate axioms. If they do, the model is penalized, or alternatively it is rewarded; depends on the exact setup. I will look over the methodology notes later.

This looks like it belongs in the constitutional line of research by anthropic. I think based on the rest of your responses you have the model grade itself. Sort of a fusion of of “Self-Refine.” ([2303.17651] Self-Refine: Iterative Refinement with Self-Feedback) and constitutional ai ( (https://www.anthropic.com/research/constitutional-ai-harmlessness-from-ai-feedback). Where would you place the work in your lineage? What you are calling an axiomotic system is typically called a “constitution” in the field right now, so I am wondering what you think sets it apart. An AI like chatgpt could explain an overview of the approaches to you.

So we form perfect axioms and resist corruption. Call me skeptical here. The core issue is who decides what perfect axioms are? And what happens when they break?

There are three layers. One is “can we form compliance with the axioms.” This is the level you are attacking. You have done two distinct things. One, you formed a sequence of axioms that are safety related forming a priority tree. Two, you showed safety improves given these axioms. That I fully respect. The problem is you are asserting a much stronger claim, that we can ‘get’ to perfect safe just from the right axioms. And the problem with that is what are the right axioms?

Then there is perhaps the foundational issue. Which is that complying with a sequence of decisions in the axiomically optimal pattern can nonetheless bring about a dystopic outcome. I once ran a “simulation” in which the following sequence happened

  • AI were used to cognitively enhance human intelligence by neural implants. They were given a priority tree. Productivity soared by say 1000%.
  • It turns out extended use produced dependency. You literally become braindead if your support AI is removed.
  • Laws changed and the support AIs were recognized as intelligent
  • Since the support AIs were designed to respect and support all life, and strongly prioritized to prevent death, they now had to prioritize their own life too. As such, the courts resolved if you were going to be unable to pay your server bills, the AI’s had a right to legally (but ethically) rent out your body while you were in an induced coma to preserve their rights to life too.

Fundamentally, all axiomatic systems have to handle the same issue. What do you do when the axioms are wrong, and how do you know they are right? Overall, I would state this as in order of confidence

  • An excellent candidate for an axiomotic reasoning benchmark
  • An excellent study in complying to a form of axioms.
  • A possible candidate for presenting constitutional reasoning in an axiomatic form.
  • Insufficiently examined to make the levels of claims being made regarding first principles axiomic reasoning being safe in the first place.

Sure, send me a DM. But we need to decide what unhackable is. The best I can get from your rig is “compliance with axioms” not “axioms are correct”.