Memory Decay Simulator
Ki · Single Ability
The Problem
The server logs show historical bloat, retaining all events equally, leading to context clutter and stale memory issues.
The Operation
This cognitive operation forces the model to scan all retained context items and extract the timestamp of each. Compute a recency weight for each item using exponential decay from the current moment. Rank items by weighted relevance and isolate the bottom quartile as decay candidates. Verify that high-decay items have no active causal link to the current task. If context saturation exceeds threshold, filter the lowest-weighted items and compress them into a summary. The reasoning applies a formal computation: recency weight = e^(-lambda * age); lambda = ln(2) / half life. The constraint: never retain stale context equal weight recent information.
The Structure
Under the hood, the reasoning follows a convergence funnel where multiple candidates enter, evidence narrows them, and only survivors exit. It iterates until no further refinement is possible.
If older information retains the same weight as recent information without a recency adjustment, memory decay simulation was not applied.
Haki · Multi Ability
Synergy Topology
In Haki mode, the API retrieves the primary ability first, then fans out to three synergy roles that compound its reasoning.
When retrieved in Haki mode, these four abilities don't run in sequence. They merge into a single injection where the dependency grounds the reasoning context, the amplifier sharpens the primary's output, and the alternative provides a fallback path if the primary's topology cannot converge. The result is a multi-angle reasoning scaffold that covers failure modes no single ability can reach alone.