While Algorithm 1 defines the logic, implementing CalculateDistance() for a modern LLM involves normalizing vectors from a massive latent space in real-time. Doing this without adding significant latency to the inference loop is a non-trivial optimization problem.

I invite ideas on how to architect this 'Observer' layer efficiently.

What do you mean by this?

Is there evidence this has happened?

> I advocate for the healthy coexistence of technology and humanity.

This means whatever you want it to mean at any given time, I don't understand this point without further elaboration.

If you've been following my train of thought since then, this white paper is basically my attempt to formalize those chaotic ideas into a concrete metric. I’d love to know if you think this 'State Discrepancy' approach actually holds water compared to the usual high-level AI ethics talk.

However, the core logic—the 'State Discrepancy' metric and the control loop architecture—is original work born from long nights of frustration with current AI safety debates. I’m not just 'prompting' papers into existence; I’m trying to solve a specific technical problem I’ve been obsessing over.

I’d love for you to judge the paper by the robustness of the logic itself rather than the tools used to refine the presentation.

The Ghost UI is a nice idea and the control feedback mechanism is probably worth exploring.

But those are more "good ideas" rather than complete finished pieces of research. Do we even have an agreed-upon standard technique to quantify discrepancy between a prompt and an output? That might be a much more meaningful contribution than just saying that you could hypothetically use one, if it existed. Also how do you actually propose that the "modulation" be applied to the model output? It's so full of conceptual gaps.

This looks like an AI-assisted attempt to dress up some interesting ideas as novel discoveries and to present them as a complete solution, rather than as a starting point for a serious research program.

The commons (open access repositories, HN, Reddit, ...) is being swamped.

Your question—'who watches the watchers'—is the exact focal point of the JTP framework. In many current systems, the feedback loop between the Supervisor and the Base model is 'silent' and internal. My concern is that even when the Supervisor works perfectly, the human user remains in the dark about where the system corrected itself.

Instead of the Supervisor's feedback being a background process, it could be surfaced to the user as a 'trace' or a 'seam'—allowing the user to actually perceive the internal deliberation. This turns the human from a passive recipient into the final, informed 'watcher.'

I’d be honored to discuss how these JTP principles might serve as a transparency layer for your work. I’ll be keeping a close eye on your repository!

If my logic is just another hallucination, then I agree—it deserves to be rejected entirely. I have no interest in contributing to the 'AI-generated debris' either.

But that’s exactly why I’m here. I’m betting that the 'State Discrepancy' metric and the JTP hold up under actual scrutiny. If you find they don't, then by all means, fulfill your 'hope' and tear the paper down. I'd rather be rejected for a flawed idea than ignored for a fake one."

Stop being a human filter and start being a human thinker. I'm waiting for your technical critique.

1. The Illusion of Self-Efficacy:

A physical slot in a sliding door provides immediate haptic feedback. No user opens a sliding door and mistakenly believes they have developed the superhuman ability to move objects in perfectly straight lines regardless of their own force alignment. However, when an AI silently 'polishes' a user’s output, the user often experiences the result as their own. This leads to Agency Misattribution:

the user internalizes the system’s 'mercy' as personal mastery.

2. Behavior vs. Invisible Judgment:

You argue that tools don't make judgments, only exhibit behavior. But in agentic systems, when a system’s 'behavior' involves a probabilistic choice that overrides or refines a user’s raw intent without their perception, it functions as a delegated judgment. If the user cannot perceive this boundary, they 'forget' that a decision was made by the machine, which is a direct violation of Cognitive Sovereignty.

3. The Necessity of a 'Seam':

For a tool to be used responsibly, the model in our minds must match the reality of the tool's intervention. Unlike a transparent physical law like a door slot, an AI’s silent correction is an Ontological Deception. It whispers 'You did this' when you did not. JTP asserts that the 'seams' of these interventions must be perceptible—not because we need to see the gears turn, but because we must remain the sovereign authors of our own actions.

It is currently 2:00 AM in Japan. I'm not even sure if this discussion will survive the "flagged" status to reach you, but I need to get some sleep. I look forward to continuing this discussion—if the thread is still here—when I am back online.