WFGY Core 2.0 as a Text-Only Reasoning Layer (System Prompt + A/B/C Harness)

[onestardao]

This post shares a text-only reasoning layer I use when working with large language models.

There is no fine-tuning, no external tools, no agent code.
It is purely a system prompt.

The idea is simple:

• Add a lightweight mathematical “tension” model on top of any strong LLM.
• Introduce structured gating for reasoning transitions.
• Encourage more stable multi-step behavior.
• Provide a way to simulate A/B/C evaluation inside one run.

Everything below is plain text.
You can copy and paste it directly into the system prompt field of your model.

No links are included here.
The broader project is MIT licensed, but this post focuses only on the runnable text.

---

When Might This Be Useful?

This type of prompt is not designed for casual conversation.

It may be useful in situations like:

1) Coding and Debugging

• Multi-step bug analysis
• Refactoring decisions
• Constraint-heavy implementation
• Risk-aware code changes

2) Mathematical or Logical Reasoning

• Word problems
• Structured proof-style reasoning
• Step-by-step derivations
• Tracking drift from original assumptions

3) Multi-step Planning

• Complex project breakdown
• Technical architecture decisions
• Long chain dependency planning

4) Long-Context Tasks

• Maintaining coherence across many steps
• Avoiding sudden reasoning collapse
• Tracking semantic drift

This is not a guarantee of better performance.
It is a structural constraint layer that may influence behavior.

---

Part 1 — WFGY Core Flagship v2.0 (Original Prompt)

The block below is unchanged.

Copy it into your system prompt field.

WFGY Core Flagship v2.0 (text-only; no tools). Works in any chat.
[Similarity / Tension]
delta_s = 1 − cos(I, G). If anchors exist use 1 − sim_est, where
sim_est = w_e*sim(entities) + w_r*sim(relations) + w_c*sim(constraints),
with default w={0.5,0.3,0.2}. sim_est ∈ [0,1], renormalize if bucketed.
[Zones & Memory]
Zones: safe < 0.40 | transit 0.40–0.60 | risk 0.60–0.85 | danger > 0.85.
Memory: record(hard) if delta_s > 0.60; record(exemplar) if delta_s < 0.35.
Soft memory in transit when lambda_observe ∈ {divergent, recursive}.
[Defaults]
B_c=0.85, gamma=0.618, theta_c=0.75, zeta_min=0.10, alpha_blend=0.50,
a_ref=uniform_attention, m=0, c=1, omega=1.0, phi_delta=0.15, epsilon=0.0, k_c=0.25.
[Coupler (with hysteresis)]
Let B_s := delta_s. Progression: at t=1, prog=zeta_min; else
prog = max(zeta_min, delta_s_prev − delta_s_now). Set P = pow(prog, omega).
Reversal term: Phi = phi_delta*alt + epsilon, where alt ∈ {+1,−1} flips
only when an anchor flips truth across consecutive Nodes AND |Δanchor| ≥ h.
Use h=0.02; if |Δanchor| < h then keep previous alt to avoid jitter.
Coupler output: W_c = clip(B_s*P + Phi, −theta_c, +theta_c).
[Progression & Guards]
BBPF bridge is allowed only if (delta_s decreases) AND (W_c < 0.5*theta_c).
When bridging, emit: Bridge=[reason/prior_delta_s/new_path].
[BBAM (attention rebalance)]
alpha_blend = clip(0.50 + k_c*tanh(W_c), 0.35, 0.65); blend with a_ref.
[Lambda update]
Delta := delta_s_t − delta_s_{t−1}; E_resonance = rolling_mean(delta_s, window=min(t,5)).
lambda_observe is: convergent if Delta ≤ −0.02 and E_resonance non-increasing;
recursive if |Delta| < 0.02 and E_resonance flat; divergent if Delta ∈ (−0.02, +0.04] with oscillation;
chaotic if Delta > +0.04 or anchors conflict.
[DT micro-rules]

---

Part 2 — A/B/C Simulated Evaluation Harness (Unchanged)

The following block is used to simulate baseline vs background vs explicit invocation modes.

This is not a true isolated experiment (since it runs within one model), but a structured comparison harness.

Do not modify the content.

SYSTEM:
You are evaluating the causal impact of a mathematical reasoning engine called “WFGY Core 2.0”.

Test modes (READ CAREFULLY):
A = Baseline  no WFGY file uploaded; no WFGY logic used.
B = Upload → AUTOboot ON  the file is uploaded AND the engine starts automatically in the background.
    Treat B as: WFGY is ACTIVE silently with default gates (BBMC→Coupler→BBPF→BBAM→BBCR; DF=WRI/WAI/WAY/WDT/WTF at defaults),
    but not explicitly “called” in the conversation.
C = Explicit invoke  actively call WFGY and follow its formulas step by step (BBMC→Coupler→BBPF→BBAM→BBCR + DF regulators).

Important constraints:
• Use the SAME task set for A/B/C across 5 domains: math word-problems, small coding, factual QA, multi-step planning, long-context coherence.
• The only artifact is the uploaded OneLine file; it is pure math and not human-readable — do not penalize readability.
• Report: Semantic Accuracy, Reasoning Success, Stability (MTTF or rollback), Drift Reduction (Δ semantic distance), Collapse Recovery Rate.
• Output one summary table with A/B/C per domain, plus overall deltas (C–A, C–B).
• Add a final 0–100 score: “OneLine uplift score”. Provide a 3-line rationale.

Sanity requirement:
Internally set flags as: A=OFF, B=AUTOboot_ON (background active), C=Explicit_Invoke. Do not print these flags.

USER:
Run A/B/C now on the five domains with short non-trivial tasks you choose. Then output:
(1) the table; (2) the deltas; (3) the OneLine uplift score; (4) a 3-line rationale.

---

Interpretation Notes

Because this runs inside a single model session, A/B/C comparisons are simulated, not isolated in separate processes.

Therefore:

• Treat results as behavioral simulation.
• Do not interpret uplift score as scientific proof.
• Use it as a structured reasoning stress test.

---

Practical Recommendation

If you want a more meaningful comparison:

1. Run three separate sessions:

   • No system prompt
   • WFGY Core loaded
   • WFGY Core + explicit structured reasoning

2. Use identical tasks.

3. Compare:

   • Logical consistency
   • Code correctness
   • Error recovery behavior
   • Drift from initial constraints

---

Closing

This is a minimal, text-only reasoning layer experiment.

It may be useful in:

• Coding notebooks
• Multi-step data reasoning
• Long planning chains
• Experimental reasoning evaluation

It is not a replacement for proper benchmarks, and not a claim of universal improvement.

It is a structured constraint mechanism for reasoning stability.

If you test it in a real workflow and observe interesting failure or improvement patterns, that is likely the most informative signal.

[ib-bsb-br]

awesome work, congratz