3 Requirements for self awareness

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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

1 Like
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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