Engineering Cartesian Doubt in Large Language Models đź“„ https://github.com/Kikingodoy/Kikingodoy/blob/77097d6b3097fc086f767abbe95c7f1ae9ef5dbd/Rene-Deskeptic_Research_Brief_AaronGodoy.pdf
LLMs are parametric parrots. Give them a book, and they ignore it—defaulting to memorized quotes. This project architectures a solution.
Current AI models are optimized for pattern matching, producing agreement engines rather than reasoning engines. Rene‑Deskeptic enforces rigorous metacognitive paranoia by integrating Descartes' method of universal doubt directly into the model's optimization. Thus, actively distrusting its own initial deductions while mathematically penalizing unsupported assertions.
Skepticism cannot merely be prompted; it must be codified into the mechanics of the model. The architecture relies on two primary frameworks: 1. Epistemic Regret Minimization (ERM) Prevents Aleatoric Entrenchment – being right for the wrong reasons. The agent maintains an explicit causal Directed Acyclic Graph: G_t = (V, E, w) L_task(Y,Y*) Standard outcome loss (e.g., cross‑entropy) R_ep(t) Epistemic regret – KL divergence between predicted interventional distribution and observed outcomes L_con(G_t) Penalty for inconsistency in the causal graph
L(θ) = L_task + λR_ep + μL_con
2. The Empirical Distrust Algorithm Modern datasets are saturated with circular citations and institutional narratives. This algorithm introduces an empirical penalty term during backpropagation: L_empirical = α × || log(1.0 - authority_weight) + provenance_entropy ||²
*authority_weight (0–0.99): flags coordinated sources. *provenance_entropy (Shannon bits): rewards uneditable, decentralized evidence (e.g., raw instrument logs, patents). (α multipliers and implementation details available upon request.)
IV. Inference Orchestration: Deep Truth Mode When evaluating complex logic, the system executes a mandatory forensic sequence:
- Parallel Steel‑Man Tracks
Track A: Primary source data only
Track B: Pure logic, stripped of authority appeals
Track C: Hybrid hypotheses ignored by both factions
- Red‑Team Crucifixion Round The model adopts a hostile persona to attack all three tracks simultaneously. Only logic that survives is output- along with explicit falsification pathways.
Status & Resource Needs ✅ Mathematical frameworks formalized (ERM, Empirical Distrust) ✅ Architectural specifications complete ⏳ Seeking GPU sponsorship (4+ H100 equivalents) ⏳ Seeking academic mentorship for fine‑tuning and evaluation We are open to partnerships with universities and industry labs.
We are open to partnerships with universities and industry labs
Contact: aaron.godoy.research@gmail.com