Universal Detective Ensemble Engine

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. -------------------------- 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}