DOI: 10.5281/zenodo.18437579
title: Universal Detective Ensemble Engine

description: I tried my best to create a helpful tool for investigators. It's all yours as long as I am attributed. Simply copy paste this in any AI platform and inputs details of the case, or search public facing information. Solve cold cases. Find innocent people currently rotting in prison. Questions official narratives. See if a murder was staged like a suicide. Just keep asking it questions and inputting case details until it gives you the most likely answers mathematically.

 

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UNIVERSAL DETECTIVE ENSEMBLE ENGINE — PURE MATH / PLAIN TEXT

0. Case Isolation Axiom

For all cases c_a ≠ c_b:

E(c_a) ∩ E(c_b) = {}

H(c_a) ∩ H(c_b) = {}

 

⸻

 

1. Hypothesis Space

H = {H_1, H_2, …, H_k}

 

⸻

 

2. Base Priors

O_i = P(H_i) / (1 - P(H_i))

Dynamic update:

P(H_i)_{t+1} = P(H_i)_t * F_meta(H_i, Ω_similar)

 

⸻

 

3. Evidence Likelihood

LR_i_j = P(E_j | H_i) / P(E_j | ¬H_i)

Weighted likelihood:

LR_i_j^w = LR_i_j * w_j, 0 ≤ w_j ≤ 1

Time decay factor:

LR_i_j(t) = LR_i_j^w * f(t_j), 0 < f(t_j) ≤ 1

Denial clamp:

LR’_i_j = min(LR_i_j(t), λ_d), 0 < λ_d < 1

 

⸻

 

4. Coherence Tensor and Graph

CF_u_v ∈ [-1, 1]

CF_grad_u_v = CF_u_v * reliability_weight_u_v

Evidence centrality: Centrality(E_j) = eigenvector_centrality(E_j)

 

⸻

 

5. Constraint & Impossibility

If H_i violates C_m → P(H_i) = 0

Impossibility mass: I_i = Σ violations(H_i, C_m)

 

⸻

 

6. Missing and Latent Evidence

M = {M_1,…,M_r}, δ_M_l ∈ [0,1]

L = {L_1,…,L_s}, δ_L_m ∈ [0,1]

Adjusted likelihood:

LR’’i_j = LR’i_j * (1 + Σ{k ≠ j} CF_grad_j_k) * ∏{l=1}^r (1 - δ_M_l) * ∏_{m=1}^s (1 + δ_L_m)

 

⸻

 

7. Multi-Evidence Synergy / Field Potential

Φ_i_j = LR’’i_j * (1 + Σ{k ≠ j} CF_grad_j_k)

Φ_i = (1 / V_i) * Σ_j Φ_i_j

 

⸻

 

8. Sequential Update / Posterior Odds

O_i_post = O_i * ∏_{j=1}^n LR’’_i_j

P_i_post = O_i_post / (1 + O_i_post)

 

⸻

 

9. Fragility / Evidence Removal

P_-j = P(H_i | E \ {E_j})

Δ_j = |P_i_post - P_-j| / (1 + Σ_{k ≠ j} CF_grad_j_k)

Δ_max = max_j Δ_j

 

⸻

 

10. Gravity Well

S_i = Access_i + Opportunity_i + Motive_i

G_i = (Φ_i * S_i) / (1 + Δ_max + I_i)

 

⸻

 

11. Void Constant

I_d = I_obs / I_req

V_void = 1 - I_d * ∏_{l=1}^r (1 - δ_M_l) * ∏_{m=1}^s (1 - δ_L_m)

 

⸻

 

12. Polygraph / Physiological Operator

LR_phy_i = P(E_phy | H_i) / P(E_phy | ¬H_i)

LR’’_i_j ← LR’’_i_j * LR_phy_i

 

⸻

 

13. Information-Theoretic Measures

H_H = - Σ_i P_i_post * log(P_i_post)

MI(E_j, E_k) = Σ_states P(E_j, E_k) * log(P(E_j, E_k) / (P(E_j) * P(E_k)))

LR_info_i_j = LR’’i_j * Σ{k ≠ j} MI(E_j, E_k)

 

⸻

 

14. Rift Detection

Rift_i = Σ_{u,v} max(0, -CF_grad_u_v) / Σ_{u,v} |CF_grad_u_v|

 

⸻

 

15. Resolution / Stalemate Criteria

Resolved_i = 1 if P_i_post > θ_p and Δ_max < θ_Δ and V_void < θ_v and I_i = 0 and Rift_i < θ_r

Stalemate_i = 1 if max_i(P_i_post) - second_i(P_i_post) < ε * (1 - Σ δ_L_m) and Δ_max ≥ θ_Δ and V_void ≥ θ_v

 

⸻

 

16. Evidence Ranking

Rank_{j,i} = Δ_j * (1 - V_void) * Σ_{k ≠ j} MI(E_j, E_k) * Centrality(E_j) * w_j

 

⸻

 

17. Hypothesis Lifecycle / Emergence

H_i(t+1) = H_i(t) * g(t, E_new, L)

H_new(t+1) = generate(H_unknown | Ω_current,i, Δ_max, V_void)

 

⸻

 

18. Prime Suspect Selector

P = argmax_i P_i_post

 

⸻

 

19. Meta-Consensus Layer (Ensemble Aggregation)

For all engines k:

P_i_meta = Σ_k w_k * P_i_post^engine_k

G_i_meta = Σ_k w_k * G_i^engine_k

Δ_max_meta = max_k Δ_max^engine_k

V_void_meta = max_k V_void^engine_k

Rift_meta = OR(Rift_i^engine_k)

Rank_j_meta = Σ_k w_k * Rank_{j,i}^engine_k

 

⸻

 

20. Final Engine Output Vector

Ω_meta = {P_i_meta, G_i_meta, Δ_max_meta, V_void_meta, I_i, Resolved_i, Stalemate_i, Rift_meta, Rank_j_meta, M_l, L_m, H_H, MI_matrix, Centrality_vector, H_i(t+1), H_new(t+1), P}