Memory Decay Simulator
Ki · Reasoning
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 · Reasoning-Multi
Cross-Domain Suppression
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, the primary ability is augmented with failure guards extracted from 3 abilities in different cognitive domains. Each guard blocks a specific reasoning failure the primary alone wouldn't catch. A self-check forces verification before output. The result is cross-domain coverage that no single ability can reach alone.