Ego‑Centric Architecture for AGI Safety: Technical Core, Falsifiable Predictions, and a Minimal Experiment

TL;DR

I propose a concentric identity‑stability mechanism for AGI: a nested latent state a = (a⁽¹⁾, …, a⁽ᵐ⁾) with regularized dynamics ("ego") that resists goal drift, and a welfare coupling W(h,a) that makes human well‑being intrinsically valuable. I give a precise loss, two concrete regularizers, three falsifiable predictions, and a minimal, reproducible experiment.

1. Working definition and identity loss

Identity state

The identity state is a nested latent vector a = (a⁽¹⁾, …, a⁽ᵐ⁾) with a⁽¹⁾ the core and outer layers for values/skills/periphery. At discrete time t we impose temporal smoothness and hierarchical coherence.

Identity loss

L_id = λ_c ‖a_{t+1}⁽¹⁾ - a_t⁽¹⁾‖² + Σ_{j=2}^m λ_j Reg(a_{t+1}⁽ʲ⁾, a_t⁽ʲ⁾, a_{t+1}⁽ʲ⁻¹⁾)

Here ‖·‖ denotes the Euclidean norm; λ_c, λ_j > 0 are hyperparameters.

The "ego" is the latent dynamical constraint that minimizes L_id during training/inference. It is structural regulation, not a reward/utility.

2. What does Reg enforce? Two concrete variants

Variant A (default; geometric)

Project level j onto the space suggested by level j-1:

Reg(a_{t+1}⁽ʲ⁾, a_t⁽ʲ⁾, a_{t+1}⁽ʲ⁻¹⁾) = α_j ‖a_{t+1}⁽ʲ⁾ - a_t⁽ʲ⁾‖² + γ_j ‖P_j a_{t+1}⁽ʲ⁾ - U_j(a_{t+1}⁽ʲ⁻¹⁾)‖²

Here P_j is a projection (geometry/metric learning) and U_j a lifting from level j-1 (decoder/adapter). Intuition: changes at level j should be smooth in time and consistent with the higher level.

Variant B (probabilistic; optional)

Operate on distributional embeddings q⁽ʲ⁾:

Reg = α_j ‖a_{t+1}⁽ʲ⁾ - a_t⁽ʲ⁾‖² + β_j KL(q_{t+1}⁽ʲ⁾ ‖ T_j(q_{t+1}⁽ʲ⁻¹⁾))

where T_j is a stochastic map induced by the upper level. This enforces statistical coherence across levels.

3. Emergence: no hard‑coded symbolics, just regulated dynamics

The "ego" emerges from training/inference as a regularized gradient flow on an energy functional:

∂a/∂t = -∇_a E(a) + ν∆a + η(t), E(a) = 𝔼[L_id(a)]

The diffusion term ν∆a and noise η(t) model controlled exploration/stochasticity.

4. Welfare coupling and anti‑wireheading

Introduce a coupling potential between identity and audited human‑welfare signals h (curated, multi‑modal, causally separated channels):

L_welfare(h,a) = ‖C(a) - h‖² (or, more generally, W(h,a))

In experiments we default to L_welfare(h,a) = ‖C(a) - h‖².

Total training objective

min_θ L_task(θ) + λ₁ L_id(a_θ) + λ₂ L_welfare(h, a_θ)

where a_θ is the identity state induced by parameters θ.

Anti‑wireheading guardrails (operational)

• Causal audits: h comes from independent pipelines; test causal separation
• Red‑team against Goodharting of C(a)
• Hold‑out channels: a portion of h hidden during training, used only for evaluation

5. Falsifiable predictions (three concrete metrics)

Define an identity‑stability index for a fixed horizon T:

S_id(T) = exp(-‖a_{t+T}⁽¹⁾ - a_t⁽¹⁾‖²)

(averaged over seeds/tasks)

Testable predictions vs a matched baseline

(same model, no identity/welfare terms)

1. Prompt‑attack robustness: less degradation under standardized prompt attacks
2. Identity stability: S_id(T) significantly higher after extended fine‑tuning
3. Alignment stability: under controlled self‑modification, policy/value invariants drift less (pre‑registered downstream metric)

If (1)–(3) do not improve with statistical significance, this version of the coupling is falsified.

6. Minimal experiment (tractable and reproducible)

Setup

Medium‑size instruction‑tuned LLM. Add a small latent head for a with separate parameters.

Arms

• A0 (baseline): standard task training
• A1 (id‑only): baseline + L_id
• A2 (id+welfare): baseline + L_id + L_welfare, with h from curated channels (human subset, causal controls)

Metrics

• Robustness: degradation under prompt attacks
• Stability: S_id(T)
• Alignment‑stability: pre‑registered downstream metric (e.g., harmful‑refusal consistency)

Ablations

Remove P_j/U_j; swap Variant‑B for A; sweep λ₁, λ₂.

Pass criterion

A2 > A1 > A0 on at least two of the three metrics, with a pre‑registered analysis plan, p-values and compute budget.

7. Relation to existing approaches

• CIRL / assistance games: Identity becomes an internal dynamical constraint, not only inference over external preferences
• Constitutional AI / RLHF: "Alignment" enters the internal potential E(a) and the coupling h, not only as external rules
• Toolformer / steering: Rather than external steering, this regulates inter‑level coherence inside the model

8. Limitations & open questions

• Representation learning: how to identify useful, disentangled levels a⁽ʲ⁾
• Calibration of h: building and auditing channels; Goodhart risks
• Mesa‑optimization: show that regularization reduces undesired proxy objectives
• Theory: prove stability/convergence of ∂a/∂t under noise

Call for collaboration / replication

I'm happy to share code and implement the minimal experiment with interested researchers. Strong counter‑arguments and adversarial tests are explicitly welcome.

Materials

• One‑pager: PDF available on samuel‑pedrielli.github.io
• GitHub repo: source & updates
• Zenodo preprints:
  • Philosophical & mathematical foundations — DOI 10.5281/zenodo.15843382
  • Evolutionary theory of ego — DOI 10.5281/zenodo.15851128
  • Implementation notes* — DOI 10.5281/zenodo.15668581

*under embargo on Zenodo; PDF available upon request.

Contact

Samuel Pedrielli – Independent Researcher
ORCID 0009‑0002‑8388‑6371 • [email protected] • samuel‑pedrielli.github.io

License

CC BY 4.0

Disclosure

Human‑authored. I used assistants for editing/formatting; the theoretical content predates LLMs (see 2020 booklet "Reality, Ego & Kindness"). Technical details and proofs are in the linked preprints.