IEEE Bio-Mimetic Framework

Solving Retrieval Drift
in Autonomous AI

Standard RAG systems suffer from "Alzheimer's in Reverse"—unbounded accumulation of noise. SCTM introduces a tensor-based eviction protocol to curate high-fidelity agent memory.

Run Simulation View Equation

1. System Architecture

Bio-mimetic pipeline for noise filtration and self-healing.

Ingestion
📥

Novelty Filter

Vector comparison ($V_{new}$ vs $V_{db}$). Duplicates trigger Reinforcement Events instead of consuming new slots.

The Core
🧠

Sparse Tensor Memory

Fixed capacity ($N=1024$). The Eviction Gate continuously prunes low-utility vectors based on Confidence, Usage, and Decay.

Governance
🛡️

Conflict Resolution

Semantic "Immune System". Contradictions trigger a Multi-Agent Debate (Advocate vs Challenger) to update truth confidence.

2. The Mathematics of Forgetting

The eviction score $E(m)$ is the heartbeat of SCTM. It mathematically mimics biological synaptic decay.

// Eviction Score Algorithm
E(m) = C · log₂(1 + U) · e-λt
CONFIDENCE (C) 0.8
USAGE (U) 5
DECAY RATE (λ) 0.05
Retention Curve
SCORE (t=10) 0.00

3. Memory Core Simulation

Observe the Eviction Gate operating in real-time.

Load Factor
0/12
Status
ACTIVE
TENSOR
// MEMORY_SLOT_ALLOCATION MODE: SPARSE_RETENTION

Buffer Initializing...

Operations

> SYSTEM_LOG
> Kernel loaded.
> Waiting for ingestion...

4. Empirical Evidence

Benchmarking against Standard RAG & LRU Caches.

Evolving Facts Accuracy

Retrieval precision of changing facts over time.

N=300 STEPS

SCTM Performance

90.1%
+15.3% vs. Baseline

Ranking

5. Ablation Studio

Toggle components to analyze their contribution.

Confidence Weighting

Protection of verified truths ($C$)

ON

Usage Frequency

Reinforcement of active data ($log U$)

ON

Temporal Decay

Ebbinghaus forgetting curve ($e^{-\lambda t}$)

ON
PREDICTIVE_MODEL_V2

Total Accuracy

85.2%
Optimal Configuration