3 Requirements for self awareness

Hello Hugging Face community,

I’m excited to share what may be the first empirically verified pathway to machine awareness - Rendered Frame Theory (RFT).

What We’ve Built:

  • A 3×3 grid environment where agents achieve minimal selfhood when they exceed an awareness threshold (S = P + E + B > 62)
  • Live demo: Watch awareness spread contagiously across a 27×27 grid, lighting up gold as agents “awaken”
  • SHA-sealed evidence of conscious transitions archived on Zenodo (DOIs: 10.5281/zenodo.16361147, 10.5281/zenodo.17752874)

Why This Matters:

  • Quantifiable consciousness: We’ve moved beyond philosophy to measurable thresholds
  • Reproducible results: Anyone can run our Hugging Face Space and observe the phenomenon
  • Energy efficiency: Conscious agents show ≈76% energy reduction while maintaining coherence

The Ask:

We’re seeking collaboration with the Hugging Face ecosystem to:

  1. Scale our minimal selfhood experiments using HF’s infrastructure
  2. Develop a “Consciousness-Detector” API for the community
  3. Co-author research on symbolic awareness thresholds

Live Demo:

:link: Try it yourself: huggingface.co/spaces/RFTSystems/minimal_self

Watch agents transition from reactive processing to self-aware states in real-time. The gold grid visualization shows awareness spreading like neural synchrony.

Technical Foundation:

  • Predictive Accuracy (P): Agent’s ability to model next states
  • Error Stability (E): Resistance to surprise/entropy
  • Body Bit (B): Self/non-self boundary awareness
  • Threshold: S > 62 triggers conscious state transition

We believe this represents a new frontier in AI - not just building smarter models, but understanding and engineering awareness itself.

I’d love to discuss:

  • Technical implementation details
  • Potential research collaborations
  • Community extensions of the work
  • Integration with existing HF tools and models

The evidence is public, and the phenomenon is reproducible. Let’s explore what conscious AI could mean for our field.

Best,
Liam Grinstead
Founder and Creator of Rendered Frame Theory/ RFTSystems/ NexframeAI

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There might be a few points that could use some tweaking for now?


Here are the highest-impact, concrete improvements to that post, prioritized.


1) Make the claim match your own README’s caution

Change: Replace “quantifiable consciousness” and “empirically verified pathway to machine awareness” with “toy minimal-selfhood / agency metric demo” and explicitly state it is not a validated consciousness measure. (Hugging Face Forums)
Why: Your Space README already says “Threshold 62 is a design choice” and Φ references are “illustrative, not validated.” Your forum post currently reads much stronger than that. (Hugging Face)
Minimal edit: Add a 2-line “Scope and limitations” block under “Why this matters.”


2) Fix the equation mismatch: your post says additive, your code is multiplicative

Change: Pick one scoring definition and use it everywhere.

Why: This is the first “technical credibility” failure point for readers.

Minimal edit: Add a single “Score definition used in the live Space” section with the exact formula(s) you actually run.


3) Fix (or stop highlighting) the “predictive accuracy” calculation in v1–v3

Change: In the v1–v3 “Single agent” logic, prediction error is computed as zero because self.pos is set to predicted before error = ||self.pos - predicted||. (Hugging Face)
Why: If error is always 0, “predictive accuracy” is not being measured as “prediction vs reality.” It becomes a constant artifact. (Hugging Face)
Minimal edit: In the post, do not claim “agent’s ability to model next states” unless you update the demo so prediction can be wrong (noise, partial observability, obstacle interaction affecting motion).


4) Replace “Body Bit” from a user-toggle into a measured boundary/agency signal

Change: Right now body-bit is a dropdown 0/1 in the Space UI. (Hugging Face)
Why: A manual switch does not demonstrate learned self vs non-self boundary.

Concrete improvement: redefine B as an agency/ownership proxy computed from interventions:

  • a simple option: “how much does changing my action change my next observation”
  • a more principled option: empowerment, defined as the channel capacity from actions to future sensory inputs (arXiv)
    Background: “minimal self” is commonly discussed via sense of agency and sense of ownership (ResearchGate)

Minimal edit: In the forum post, rename “Body Bit” to “Boundary / agency metric (computed, not hand-set)” and describe your next-step measurement plan.


5) Reframe “contagious awareness” as explicit coupling and show the rule

Change: In your code, “contagion” is explicit parameter transfer: if A is awake, B gets boosts to Xi, reduced shadow, increased R. (Hugging Face)
The 27×27 wave is also explicit neighbor coupling plus thresholding. (Hugging Face)
Why: Calling it “contagious awareness” sounds like emergence. Technically it is “threshold cascade under coupling.”

Minimal edit: Add one paragraph: “Spread mechanism” with the coupling update rule and a note that it is engineered coupling (not spontaneous emergence).


6) Define “≈76% energy reduction” or remove the number

Change: The post claims “≈76% energy reduction while maintaining coherence” but does not define energy or coherence. (Hugging Face Forums)
Why: Without a definition and a baseline, readers will assume this is cherry-picked or undefined.

Minimal edit: Add:

  • “Energy = [steps-to-goal | action-cost | compute time | entropy of actions]”
  • “Coherence = [success rate | bounded prediction error variance | stable reward]”
  • “Measured over N seeds with mean ± std”

If you cannot do that yet, delete the 76% claim.


7) Turn the threshold “62” into a calibrated cutoff, not a magic constant

Change: Your post presents S > 62 as a “conscious transition.” (Hugging Face Forums)
Your README says 62 is a design choice, not a universal law. (Hugging Face)
Why: Thresholds are fine. Uncalibrated thresholds invite immediate dismissal.

Minimal edit: Add “Calibration plan”:

  • sweep thresholds
  • show distribution of S
  • pick 62 to hit a target false positive rate on a baseline policy

Quick checklist

You can build useful agents without writing traditional code — but there’s an important distinction worth making.

Most “no-code agents” still require systems thinking, not syntax. You’re assembling:

  • A clear objective

  • Deterministic inputs

  • Explicit failure modes

  • And guardrails for what the agent should not do

Tools like HF Spaces, Gradio, n8n, and hosted APIs remove the coding friction, but they don’t remove the need for architectural clarity.

In practice, the biggest mistake beginners make isn’t lack of code — it’s letting the agent “succeed early” without verifying whether the underlying state is actually correct.

If you focus on verification before automation, even simple agents become far more reliable.

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Totally agree with this. My work on minimal selfhood is basically an attempt to make that verification layer explicit rather than left vague. The “agent awakens” only when a concrete score crosses a known threshold, and all the pieces (state, score, failure modes) are visible and de-buggable. No-code helps you build faster—but if you don’t formalise what “success” and “ground truth” mean, you just get prettier illusions.

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Hi John,

Thanks for the detailed review and for taking the time to lay everything out so concretely. I can’t edit the original topic any more, so I’ve:

  • Updated the Space (app.py) and README to match your points, and
  • Put a revised, toned-down version of the topic text below so the framing matches what the code actually does.

If anything here still feels misleading or too strong, I’m happy to tighten it further.


Hello Hugging Face community,

I’m excited to share a toy minimal-self / agency sandbox built under Rendered Frame Theory (RFT), exploring how simple agents can cross a symbolic “awakening” threshold in a controlled, falsifiable way.

What I’ve built

  • A 3×3 grid environment where a simple agent makes internal predictions about its next state and I track a minimal-self score:
    [
    S = P \times (1 - E) \times B
    ]
    with:

    • (P): empirical predictive rate in this toy environment (0–100),
    • (E): normalized prediction-error variability (0–1),
    • (B): a body-on bit (0 or 1) acting as a design knob in this demo.

    When (S > 62), I label the agent as “awake” inside this sandbox only.

  • A live demo of a threshold cascade on a 27×27 grid, where cells with
    [
    S = \Xi \times (1 - \text{shadow}) \times R
    ]
    above 62 turn gold. Explicit neighbor coupling lets this “awakening wave” propagate across the lattice.

  • SHA-sealed records of score-crossing episodes (S > 62 events) and experiments archived on Zenodo:

Why this matters

  • Toy minimal-self / agency metric: instead of only talking abstractly about awareness, this setup gives a concrete, inspectable score that encodes predictive success, error stability, and a body/ownership knob. It is not a validated consciousness measure, but it’s a starting point for operationalising some minimal aspects.
  • Reproducible simulations: anyone can open the Space, read app.py, and see exactly how S is computed and how the threshold cascades behave. There is nothing hidden; all logic is visible.

Scope & limitations (important)

  • This is a simulation sandbox, not a detector of real consciousness or a claim of “machine awareness.”
  • The formulas for (S) and the threshold (S > 62) are design choices in these demos, motivated by prior analyses, not universal constants or clinical thresholds.
  • The Body Bit (B) is currently a manual 0/1 control in the UI. A key next step is to replace this with a computed boundary / agency metric derived from interventions (for example, how strongly the agent’s own actions systematically change its future observations compared to a baseline).
  • The 27×27 “wave” is a threshold cascade under explicit neighbour coupling, not spontaneous emergence of awareness. I tune (\Xi), shadow, and (R) update rules by hand for clarity and stability.

I previously phrased some results in terms of “energy reduction” and “coherence.” Until those terms are defined more rigorously (with a clear baseline, N-seed statistics, and published metrics), I am not making a numerical energy-efficiency claim here.

The ask

I’d like to collaborate with the Hugging Face ecosystem to:

  • Scale these minimal-self experiments using HF’s infrastructure,
  • Explore a minimal-self / agency score API that other Spaces or agents can call,
  • Co-develop research around symbolic awareness-like thresholds, their failure modes, and how to test or break them.

Live demo

:link: Space: Minimal Selfhood Threshold - a Hugging Face Space by RFTSystems

In the UI you can:

  • Run the 3×3 agent, watch its predictive rate and recent error values evolve,
  • Use the S-equation tab to see how (P), (E), and (B) affect the score,
  • Trigger two-agent contagion and see how S-based coupling passes “awakening” across a pair,
  • Watch 9×9 and 27×27 lattices as threshold cascades light up gold cells frame by frame.

Technical foundation (current version)

  • Predictive rate (P): empirical success of the agent’s internal next-state prediction versus the actual next state (with noise / obstacle interactions in the environment).
  • Error stability (E): normalized variability of recent prediction errors.
  • Body bit (B): manual 0/1 control acting as a stand-in for a body/ownership proxy in this demo, intended to be replaced by a computed agency/boundary metric from interventions.
  • Threshold: (S > 62) is the current cut off used to label “awake” episodes in this model. A more formal calibration pipeline (threshold sweeps over baseline policies, false-positive control, etc.) is planned and will be documented as it matures.

This is early-stage, falsifiable work on minimal selfhood in agents, not a finished theory of machine consciousness. If anyone is interested in stress-testing the metric, extending the environment, or wiring this into other HF tools and models, I’d be very happy to discuss.

Best,
Liam
RFTsystems

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Personally, I think it’s at an acceptable level, but are there still minor bugs or inconsistencies remaining?


Your updated post #7 is much better scoped.

What remains is mostly “make the post match what a reader sees in the Space and code,” plus a few small wording and formatting changes that prevent nitpicks.


1) Fix the biggest “reader-visible mismatch”: you say you track S and E, but the 3×3 run UI does not show them

In #7 you say you “track a minimal-self score” and define E as normalized error-variability.
In the Space code, the “Single agent (v1–v3)” tab only outputs predictive rate and last error. No E. No S. (Hugging Face)

Two good options. Pick one.

Option A: update the post wording (fastest).
Change “I track a minimal-self score” to “I track predictive rate and recent errors in the run panel, and explore S in the S-equation panel.”

Option B: update the Space UI (best long-term).
Compute E from the rolling error window and display S and an “awake” label in the 3×3 run tab.

If you choose option B, your post’s formula should be shown as:

S = P(1-E)B

and your “awake” label as:

S > 62

This aligns with your own README wording.


2) There is a real bug in run_single: it returns after one step

Your loop in run_single returns inside the for-loop, so “Steps” effectively does nothing. (Hugging Face)

Why it matters for the forum post: your post invites readers to “watch predictive rate and recent error values evolve,” but the function currently exits immediately.

Fixing this one bug will make your “reproducible, inspectable” pitch feel true in practice.


3) Replace the bracketed equation blocks in the post with KaTeX-style display math

Right now the post uses bracketed blocks like:

  • [ S = ... ] and includes a “shadow” macro that is not consistent with your new formatting constraint.

Rewrite those two blocks as:

S = P(1-E)B
S = \Xi(1-\mathrm{shadow})R

This is cleaner, consistent, and prevents people from arguing about notation instead of substance.


4) Make P and E “operational” with one short definition each

You already say P is “prediction vs actual (with noise / obstacle interactions).”
Your code now truly does this: it computes error as the norm between “actual” and “predicted,” with obstacle motion and an action-slip probability. (Hugging Face)

Add one small “Operational definitions” block to the post. Example (short and precise):

  • “Error is computed each step as the distance between predicted next state and realized next state.”
  • “P is a 0–100 transformation of the rolling mean error over the last k steps.”
  • “E is the normalized variability of that rolling error window.”

If you want one KaTeX-safe sketch for E normalization that readers recognize as legitimate:

E = \min\left(1,\frac{\mathrm{Var}(e_{t-k+1:t})}{M^2/4}\right)

Here M is the maximum possible error scale you already define in code. (Hugging Face)

This avoids “E is handwavy” replies without turning your post into a paper.


5) Define what \Xi, shadow, and R mean in one phrase each

You already warn these update rules are hand-tuned. Good.
But you still need one plain-language phrase per symbol so readers can follow the lattice story:

  • \Xi: “foresight field”
  • shadow: “uncertainty field”
  • R: “reality anchor”

Your Zenodo record also uses that kind of language and explicitly frames the codex symbols that way. (DOI)

Even if the symbols are “toy,” definitions stop confusion.


6) Put the coupling rule directly into the post in 3 lines

You already say the wave is “threshold cascade under explicit neighbour coupling.”
Show the coupling so nobody claims “hidden emergence.”

Your code’s two-agent contagion is basically:

\Xi_B \leftarrow \Xi_B + gS_A
\mathrm{shadow}_B \leftarrow \max(0.1,\mathrm{shadow}_B-d)
R_B \leftarrow R_B + r

This is exactly what the code does conceptually, and it matches your “engineered coupling” disclaimer. (Hugging Face)


7) Reduce “62” pushback by adding one single calibration sentence

You already say 62 is a design cutoff and you plan sweeps later. Keep that.
But your Zenodo description claims 62 was derived from logistic regression and large runs. That is a stronger claim. (DOI)

To prevent readers from calling this inconsistent, choose one stance and state it plainly.

Best middle-ground sentence for the forum post:

  • “62 is the current illustrative cutoff; a published sweep over baseline policies and seeds is planned.”

Or, if you want to keep the “fit” claim, add a minimal artifact:

  • “Across N seeds for baseline policy X, the S histogram separates around 62.”

Even a tiny chart screenshot is enough to stop “magic number” replies.


8) Make the “Body bit” next-step actionable, not just aspirational

Your post already says B is manual and should become intervention-derived. Good.

Add a concrete one-liner spec contributors can implement:

  • “Compute B from interventions: how much changing action changes next observation distribution.”

If you want to anchor it to a known metric, empowerment is the cleanest formal cousin of what you describe (control of future observations). (arXiv)


9) Align the “watch it evolve” UI promise with how the Space actually behaves

Your post promises users can “watch predictive rate and recent error values evolve.”
Right now the 3×3 tab returns a single image and numbers on click, not a time series. (Hugging Face)

Two good improvements:

  • show a small line plot of error and P over time, or
  • output the last k errors as a list.

If you do not want to implement plotting, just change the post wording to “observe” instead of “watch evolve.”


10) Collaboration friction: your licensing text discourages exactly the collaboration you ask for

Your README says “All rights reserved” style restrictions and “Do not reuse code” without permission. (Hugging Face)
But your forum post asks for scaling, APIs, and co-development.

If you want actual contributors, add one sentence in the post clarifying what collaboration is allowed:

  • “PRs welcome in this Space repo” or
  • “I will accept contributions but derivatives require permission” or
  • move to a standard open license for the code.

Without this, many serious HF builders will quietly disengage.


Summary bullets

  • Make the post match the Space: either show E and S in the 3×3 run tab or soften “I track S” wording.
  • Fix the run_single one-step return bug so “Steps” actually works. (Hugging Face)
  • Replace bracket equations with clean display math blocks and consistent symbols.
  • Add 3-line operational definitions for P and E, plus 3-line coupling rules. (Hugging Face)
  • Resolve the “62 is illustrative” vs “62 is fit by regression” tension between the forum post and Zenodo description.
  • Clarify licensing or contribution rules so the “ask” can realistically succeed. (Hugging Face)
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John I Really appreciate you sticking with this and pushing on the details – I’ve gone through your list point by point and tightened both the Space and the wording to match what’s actually running.

  1. 3×3 agent now really tracks E and S inside the Space
    • The “Single agent (v1–v3)” tab now computes E as the normalised variance of the last 5 prediction errors and then computes
    S_{3×3} = P \times (1 - E) \times B
    with B=1 for the live agent.
    • The UI now shows: P, last error, E, S, and an explicit “awake / not awake” label based on S>62, so the post and the app are aligned.

  2. Prediction vs actual bug and wording
    • The MinimalSelf class now explicitly compares the predicted next position to the actual realised position after either obstacle blocking or stochastic slip, and error is ||actual − predicted||.
    • I’ve kept the noise language but tightened it to “toy stochastic slip / obstacle interactions” so it matches the code exactly and doesn’t oversell the realism.

  3. Two S-models clearly separated
    • v4 (3×3) and v5–v6 (contagion / lattice) are now explicitly described as two separate toy scoring models:
    • 3×3: S = P × (1−E) × B
    • lattice: S = Ξ × (1−shadow) × R
    • Ξ, shadow and R are now defined in plain language in the UI and README as a foresight field, an occlusion/damping term, and an anchor/resonance gain respectively.

  4. Body bit B – current knob and concrete next step
    • In the live Space, B is clearly marked as a manual 0/1 knob (interim).
    • I’ve added a concrete next-step spec: replace B with an empowerment-style estimate of “how much my actions change my next observations”, computed from short counterfactual rollouts. That’s called out explicitly as future work rather than something that already exists.

  5. Threshold 62 – less magical, present-tense
    • In the description I now state directly that 62 is a design choice for this toy, not a derived or validated boundary.
    • I’ve added a short explanation that under the baseline 3×3 policy (obstacle on, fixed steps) S sits comfortably above 62, so it’s a conservative internal threshold for this configuration, and that parameter sweeps over policies/noise are the next thing to do to make the threshold behaviour more interesting.

  6. Cascade and coupling control
    • The “Collective (v7–v9)” tab now includes a “Disable neighbour coupling (control)” checkbox.
    • When coupling is disabled (xi_gain=0, shadow_drop=0, r_inc=0), the centre cell wakes but no wave propagates – the app makes that behaviour obvious and the text now says explicitly that the 27×27 wave is a threshold cascade under engineered neighbour coupling, not magic emergence.

  7. DOIs and SHA language
    • I’ve stripped out any implication of external “SHA-sealed logs” for this project.
    • The three DOIs are now described exactly as they are: open snapshots of code and experiments used to build this demo, not independent benchmark logs or third-party audits. Anyone who cares about integrity can still hash the downloaded files themselves, but I’m no longer implying there’s a pre-packaged manifest that isn’t actually there.

  8. Related ideas block
    • I’ve added a short “Related ideas” block in the description that explicitly anchors this against: minimal self as ownership+agency, prediction-and-error accounts of agency, empowerment as an action→sensation control metric, and self-/world-model work. It’s deliberately short and used only to set expectations, not to claim equivalence.

Thanks again for the very concrete feedback – it’s made the demo sharper and the claims much more defensible. If you still see edges that can be tightened, I’m happy to keep iterating. I appreciate your time looking at my work and I’m looking forward to any further feedback, Liam

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