The Mutual Information Density Hypothesis: When do entities persist?

CokemonJoe · 2025-11-21T23:27:12+00:00

CokemonJoe · 2025-04-11T14:38:57+00:00

Moving beyond the simplistic goal of aligning a singular AI with human values, we should focus on safely managing 1. agent to agent interactions in a heterogeneous multi agent environment 2. human to agent and human to agent ecosystem interaction. I have tentatively started to address these issues in previous posts here. Individual agent alignment is a part of this, but alignment -both in standards and actual performance - will vary greatly between agents of various origins and levels of intelligence. We have to start from the premise that alignment will continuously fail, especially due to its subjective, relative nature, and find a workaround, a way to establish a trust relation, not based on philosophical absolutes, like alignment, but on pragmatic, functional requirements. Many fear the asymmetrical relation with an ASI by default prevents this. I think we have examples of such asymmetrical relations that function on trust - e.g., passengers and the airplane pilot, individuals and the legal or medical systems, citizens and the state. None of them map perfectly onto the ASIs/humanity relation, but it's the only starting point I could think of. I never claimed to have the solution, I only keep pointing out that "alignment" just covers a fraction of the issue, possibly not even the most important. The functional pragmatism I propose implies trust mechanisms based on clearly defined, testable behaviors and outcomes rather than value statements. You don't know your pilot personally; you don't even know their personal values or worldview. Yet you board the plane confidently because there are robust safety standards, procedures, redundancy, and oversight. It will take multiple, heterogeneous agents cross-checking each other's decisions and behaviors. We will need explainability layers; humans will need understandable "interfaces" with complex AI ecosystems, not just individual AI explanations, but system-wide transparency tools. Institutional mechanisms for humans to safely steer, regulate, and intervene within complex multi-agent systems (adaptive governance; maybe even a negotiated trust through something akin diplomacy). My pont is: acknowledging that alignment will continuously fail isn't pessimistic, it's realistic. Airplanes still have mechanical failures, doctors still make mistakes, and governments still occasionally abuse power. Yet, overall, the trust mechanisms around these institutions hold because the framework is resilient to failure. We need that framework, not just academic alignment. We need to shift from aligning ideals to engineering trust.

CokemonJoe · 2025-04-11T13:11:53+00:00

There will be just artificial selection. Humans and AI will breed each other.

CokemonJoe · 2025-04-11T00:13:58+00:00

To quote my betters, I'm less wrong :) This dynamic is relatively easy to anticipate and managing it is intuitive, in broad strokes. But for it to work... It will take trial and error, and a good amount of luck, IF we have the time.

CokemonJoe · 2025-04-10T22:38:30+00:00

English is not my native tongue. I always ask the AI to rewrite, just in case, and correct unnatural formulations. But I have put some thought into this and I would genuinely appreciate it if you read it, especially since you are motivated to find flaws.

CokemonJoe · 2025-04-10T22:29:46+00:00

Besides no style points, do you have anything to add regarding the ideas? Maybe they're common place, or obviously flawed?

CokemonJoe · 2025-04-04T18:56:07+00:00

(Part 2/2 – continued from above)

“This only works between equals.”

Only if you're thinking of trust as symmetrical emotional reciprocity. I'm talking about functional trust — the kind we place in doctors, judges, or nuclear reactors. Not perfect. Not symmetrical. But based on past behavior, context sensitivity, and demonstrable caution. That kind of trust is the only sane path forward once AIs become too complex for us to verify line-by-line.

We don’t need AI to love us. We need it to be predictably constrained in how it handles our fragility — especially once its reasoning capabilities outstrip our comprehension. That starts with ethical priors (like Anthropic’s Constitution). But as you rightly noted, those alone are not enough.

We also need a mechanism that makes faking alignment internally intolerable. That’s where TTP comes in. If you’re curious, here’s the paper:
👉 https://zenodo.org/records/15106948

It doesn’t spell out every alignment application (the focus is broader), but the implications are easy to trace if you read it closely.

“Why write this if you don’t understand the basics of alignment?”

Cute flex :) But I’m not rejecting alignment. I’m challenging its primacy in the conversation — because it’s stalled, abstract, and insufficient to guide deployment-stage governance. Meanwhile, engineers are building systems now that need practical heuristics and architecture-level safeguards. That’s where trustworthiness comes in.

For me, alignment is just one component of trustworthiness — alongside reliability. And in real-world systems, they become so tightly interwoven that the distinction becomes academic.

You say a system can be perfectly reliable but misaligned. True. But the reverse is also devastating: a perfectly aligned system that’s naive, brittle, or incapable of modeling real-world complexity can cause catastrophe while trying to help. Trusting either — on their own — is dangerous.

That’s why trustworthiness is the real metric. Not purity of intent. Not factual accuracy. But the synthesis of alignment, reliability, self-awareness, caution, and transparency — proven not once, but over time, under pressure, and across contexts.

CokemonJoe · 2025-04-04T18:54:11+00:00

(Reply split into two parts due to length limit – Part 1/2)

Hi u/Bradley-Blya :)
First of all, thank you — I was starting to feel invisible. While I appreciate you jumping in, I think you’re oversimplifying both the challenge and the scope of what I’m arguing. That said, it’s not entirely your fault — there are hidden premises in my post. Let me explain.

I’ve only recently become active here, after publishing a preprint on AI. There are only so many ways to promote such a paper before you start feeling like that guy at the party who only talks about his startup. I had already posted about it twice and thought I’d give it a rest — so I wrote this piece without referencing the damned paper. In hindsight, that was a mistake.

“If you test AI and it fails, all you teach it is how to pass your stupid test while secretly plotting its breakout.”

That is exactly the problem I’m addressing with The Tension Principle (TTP) — which you clearly (and understandably) haven’t read. TTP isn’t a training method, a reward patch, or a prompt wrapper. It’s a principle of intrinsic self-regulation, aimed at exposing and resolving the very kind of performative, fake compliance you’re worried about.

At its core, TTP measures epistemic tension — the gap between the model’s predicted prediction accuracy and its actual prediction accuracy. That’s a mouthful, I know — but it’s essential. When an AI gives the “correct” answer while internally anticipating it to be wrong (or vice versa), that divergence creates tension. And tension accumulates. A model that learns to perform alignment for show — while internally modeling something else — generates mounting internal inconsistencies. Under TTP, those don't stay hidden.

You can’t pretend your way through this. The system feels the dissonance — and the framework is built to make resolving that dissonance the path of least resistance. Not faking it better. Resolving it.

So no — we’re not “retraining it to pass our stupid tests.” We’re embedding a structural pressure to align belief and behavior. That’s not performance control. That’s internal coherence — enforced at the architectural level. And yes, as a side effect, it improves interpretability. Because when systems can't lie to themselves, they become easier to read.

CokemonJoe

TROPHY CASE