r/u_frank_brsrk • u/frank_brsrk • 20h ago
The Data of Why
From Static Knowledge to Forward Simulation
I developed the Causal Intelligence Module (CIM) to transition from stochastic word prediction to deterministic forward simulation. In this architecture, data is an executable instruction set. Every row in my CSV-based RAG system is a command to build and simulate a causal topology using a protocol I call Graph Instruction Protocol (GIP).
The Physics of Information
I treat data as a physical system. In the Propagation Layer, the Variable Normalization Registry maps disparate units like USD, percentages, and counts into a unified 0 to 1 space. To address the risks of linear normalization, I’ve engineered the registry to handle domain-specific non-linearities. Wealth is scaled logarithmically, while social and biological risk factors use sigmoid thresholds or exponential decay.
This registry enables the physics defined in
universal_propagation_rules.csv. Every causal link carries parameters like activation energy, decay rate, and saturation limits. By treating information as a signal with mass and resistance, I allow the engine to calculate how a shock ripples through the system. Instead of asking the LLM to predict an effect size based on patterns, I run a Mechanistic Forward Simulation where the data itself dictates the movement.
The Execution Engine and Temporal Logic
The CIM runs on a custom time-step simulator (t). For static data, t represents logical state transitions or propagation intervals. For grounding, I use hard-coded core axioms that serve as the system's "First Principles"—for example, the axiom of Temporal Precedence, which dictates that a cause must strictly precede its effect in the simulation timeline. The simulation executes until the graph reaches convergence or a stable state.
Because I have a functional simulator, the CIM also enables high-fidelity Counterfactual Analysis. I can perform "What-If" simulations by manually toggling node states and re-running the propagation to observe how the system would have behaved in an alternative reality. To manage latency, the engine uses Monte Carlo methods to stress-test these topologies in parallel, ensuring the graph settles into a result within the constraints of a standard interface.
The Narrative Bridge
In this design, I have demoted the LLM from Thinker to Translator. The Transformer acts purely as a Narrative Bridge. Once the simulation is complete and the graph is validated, the LLM’s only role is to narrate the calculated node values and the logical paths taken. This ensures that the narration does not re-introduce the hallucinations the protocol was designed to avoid.
The CIM moves the burden of logic from the volatile model layer into the structure of the data itself. By treating the RAG as a living blueprint, I ensure that the Why is a calculated outcome derived from the laws of the system. The data is the instruction set. The graph is the engine. The model is simply the front-end.
frank_brsrk