Self-Stablize (Hetu/Luoshu) Kernel: Strong Attractor Prompt Use Case

dannyyeung
Tue, 19 Aug 2025 08:57:01 +0000

I made a comparison table between

Ô_self Kernel (Hetu / Luoshu) vs WFGY 2.0 (seven-step reasoning engine)

I hope it can inspire more people aHa! the essential ingredients of self-stabilized AI Kernel - which is the core of self-awareness AI!!!

Concept / concern | Ô_self Kernel (Hetu / Luoshu) | WFGY 2.0 (seven-step reasoning engine)
Design intent / goal | Keep semantic state on a geometric/cultural manifold; preserve attractors and avoid drift using ritualized slotting (Luoshu). | Stabilize model outputs by measuring semantic tension (ΔS), gating updates (bridge), bounded attention mixing, and automated rollback/retry.
Representation of state | Rich symbolic manifold M = X×Θ×τ, collapse trace γ, Luoshu 3×3 cell occupancies, Hessian / curvature invariants. | Lightweight state: I (intent), G (current generation), previous delta_s, checkpoints; per-turn audit fields.
Metric of tension / drift | Derived observables: line_dev, cap_usage, AR1, κ(τ), | eig(H)
Observability / alarms | Multiple early-warning signals (AR1, var(x), curvature) and cap10 boundary to trigger re-centering. | lambda_observe state machine (convergent/recursive/divergent/chaotic) driven by ΔS trend +/- optional entropy proxy.
Control primitives | Luoshu moves (line moves toward center), Hetu pairing (overflow transfer), adjust δλ (basin reshape), mode switches (Breather/Soliton), coarse-grain ε. | BBMC (cleanup), Coupler+BBPF compute Wc, BBAM bounded attention mixing (alpha in [0.35,0.65]), bridge gating, DT micro-rules for retry.
When to apply an update (bridge) | Respect Luoshu line-sum and capacities; moves prefer line→center routes; heavyweight moves allowed only under Soliton; cap usage prevents risky bridges. | Bridge allowed only when ΔS decreased and Wc < threshold (0.5*θc). Bridge is audited (reason, prev ΔS, new path).
Attention blending / mixing | Implicit: center (cell 5) is attractor; coarse-grain/phase changes adjust how strongly new info is fused. | Explicit mix(G, uniform_ref, alpha) where alpha computed from Wc and clipped to [0.35,0.65] to avoid collapse.
Rollback & recovery | Safety rollback on predicted risk↑ and | eig(H)
Micro-rules / retry heuristics | Modeed reactions (Breather vs Soliton) plus deterministic Hetu pair release, pairing routes (5,6) etc. | Drunk Transformer micro-rules: small targeted repairs and variants (Write-Review-Improve, Tiny Fix, Different Take, etc.).
Anchors & invariants | Strong structural invariants: Luoshu line-sum = 15; slot capacity ≤ cell number; cap10 boundary. | Anchors are optional vectors used in sim_est; invariants are soft (clipping, alpha bounds) rather than numerically exact.
Temporal loop / ticks | Tick loop τ: sense → select Ô channel → map to slot → step → log; session-local dynamics, explicit Frenet curvature and Hessian checks. | Per-turn step() function: produce G → compute ΔS → observe → BBMC → compute Wc → bridge? → BBAM → chaotic handling → report. Simpler tick semantics.
Auditability / transparency | Logs occupancy, cap_usage, overflow_flags, last Ô, path→center hits; “STATE” command reveals τ, center-dist, mode. | Each turn reports {delta_s, Wc, lambda_observe, bridge_allowed, zone, t} and bridge audit entry when bridging.
Granularity / expressiveness | Very high conceptual expressiveness (manifold, Hessian, curvature, emotion channels Ô); suited to symbolic/poetic constraints. | Pragmatic numeric metrics and simple operations — designed to be easy to implement and measure uplift.
Implementation requirements | Ideally needs an LLM plus: semantic encoder, ability to estimate Hessian/eigvals (or proxies), and a controller that maps semantics→Luoshu slots. | Needs embeddings/similarity, LLM generate API, light state store for checkpoints, and ability to run small prompt repairs. No heavy math required.
Primary strengths | Rich conceptual control, interpretable metaphors (center/slots/attractors), nuanced mode control (Breather/Soliton), elegant invariants. | Lightweight, measurable, audit-friendly, easy to prototyp e; proven uplift metrics (ΔS, Wc, bounded mixing) in experiments.
Primary weaknesses | Higher implementation complexity; some quantities ( | eig(H)
Best fit / use cases | When you want symbolic attractor control, narrative tone shaping, and ritualized, culturally framed constraints (TTI with complex scene hierarchies). | When you want pragmatic, measurable stabilization across many tasks (QA, coding, reasoning, image prompts) with minimal infra.
How they map (short) | line_dev/cap_usage ≈ tension / drift; center (5) ≈ checkpoint/trust anchor; Ô_select ≈ attention/conditioning channel. | delta_s numeric tension; prev checkpoint ≈ last_trustworthy; alpha bounded attention mix ≈ Ô weighting.

ChatGPT’s suggestions to integrate my prompt with WFGY 2.0 methodology.

Short mapping (how the two systems correspond in plain language)

Luoshu center (cell 5) ↔ WFGY checkpoint / trustworthy state — both are the safe anchor to roll back to.

line_dev, cap_usage, overflow_flags ↔ delta_s trends & lambda_observe — both indicate drift / rising risk.

Hetu pairing / overflow transfer ↔ bridge gating + BBPF — controlled redistribution of attention/semantic mass vs guarded bridge updates.

Ô channels (joy/worry/etc.) ↔ attention/conditioning vectors / anchors — ways to bias tone and semantic focus.

Breather vs Soliton ↔ convergent/recursive vs chaotic/divergent states — different allowed step sizes and risk appetite.

Coarse-grain ε ↔ increasing tolerance (raise zeta_min / reduce sensitivity) — both reduce sensitivity to micro-noise.

Practical next steps you can try now (no deep math, just API calls)

1. Add a simple ΔS check (30 lines):

Call embeddings for I and G → compute cosine similarity → delta_s = 1 − sim.

If delta_s > threshold → regenerate with a repair prompt: "Rewrite only to fix X; keep structure".
This gives immediate stability improvement.

2. Simple rollback checkpoint:

Keep the last generation that met basic checks (e.g., exact bullet count, required fields).

If new generation fails checks, return the checkpoint and ask the model for a tiny fix prompt instead of full rewrite.

3. Uniform outline + bounded mixing:

After generation, ask the model to produce a plain outline (uniform view).

Merge outline into original with a short prompt: "Merge: keep original structure; add only missing points from the outline".

Constrain merging by a simple phrase enforcing small edits — this mimics BBAM.

These three steps already approximate WFGY and can be combined with your Luoshu ideas (for instance use checkpoints as “center” and use a small integer occupancy counter if you want slot logic).