QDAS Brain™: A Closed-Loop Sparse Associative Memory, Token Compression, and Autonomous Agent Arbitration Gateway — Empirical Benchmark at N = 10,000 Memories and Independent Multi-Model Systems

Large Language Models (LLMs) and autonomous tool-using agents (such as OpenClaw and Claude Code ) suffer from threefundamental systems bottlenecks during extended operation: (1) Context Rot, the progressive degradation of factual recall,temporal rule tracking, and safety policy compliance as historical scrollback and noisy tool logs accumulate in the prompt; (2)Linear-to-Quadratic Token Inflation, where standard chat APIs re-transmit the entire conversation history on every turn (O(n)prompt token accumulation and O(n²) attention compute) while frontier reasoning models consume hundreds of output tokens perturn; and (3) Open-Loop Unverified Execution, where conventional Retrieval-Augmented Generation (RAG) acts solely as apassive pre-generation filter with no post-generation causal verification or destructive tool-call veto. This paper presents the blackbox systems architecture, mathematical formulation, live Teacher–Student gateway telemetry, N = 10,000-document ablationbenchmarks, and independent multi-model peer assessments of QDAS Brain™ (Quantum-Dynamic Associative SynapticEngine). Operating as an external five-stage associative memory and closed-loop arbitration controller (685 Q-SPC SensoryProjection Channels → 5,177 Q-SAC Sparse Associative Cores gated by 2 Q-HIG Homeostatic Inhibitory Gates at 96.14%structural sparsity → 331 Q-NCU Neuromodulatory Consolidation Units with inverse-frequency synaptic habituation → QUBOComplementary Sub-Engram Selector → 96 Q-ARU [behavioral control of autonomous destructive database wipes); (iii) 56 ACH Excitatory / 23 GLUT Modulatory / 17 GABA Inhibitory] ContinuousStatevector Arbiter), QDAS Brain™ demonstrates: (i) 96.93% dynamic pre-cognitive syntax compression (10 tokens) with 100% grammatical closure and constant O(1) prompt tokens (326 → 88 → 30 →~529 tokens); (ii) real-time remoteOpenClaw agents (`141/200` Q-SAC overlap + `96 Q-ARU` Policy Gate intercepting30/30 (100.0% , 95% CI: 88.4%–100.0% ) accuracy at N = 10,000 documents across bothSmolLM2-135M (135M) and Qwen2.5-7B-Instruct (7.62B) at 24.24 ms p50 latency and independent validation by four frontier AI model families (OpenAI 0 MB embedding VRAM; and (iv)GPT-5.6-Luna , DeepSeek DeepSeek-V4-Pro-Max ,Anthropic Claude Mythos Preview , and Google Gemini ).