Warp-Cortex: An Asynchronous, Memory-Efficient Architecture for Million-Agent Cognitive Scaling on Consumer Hardware

TL;DR

Warp Cortex introduces an asynchronous, memory-efficient architecture enabling millions of cognitive agents on consumer hardware by decoupling agent logic from memory and applying topological data analysis techniques.

Contribution

It presents a novel architecture with Singleton Weight Sharing and Topological Synapse techniques to drastically reduce memory complexity for large-scale multi-agent LLM systems.

Findings

Supports 100 concurrent agents on a single GPU

Theoretical capacity exceeds 1,000 agents before latency issues

Achieves 2.2 GB total VRAM usage for 100 agents

Abstract

Current multi-agent Large Language Model (LLM) frameworks suffer from linear memory scaling, rendering "System 2" parallel reasoning impractical on consumer hardware. We present Warp Cortex, an asynchronous architecture that theoretically enables million-agent cognitive scaling by decoupling agent logic from physical memory. Through Singleton Weight Sharing and a novel Topological Synapse--inspired by hybrid landmarking techniques from Topological Data Analysis (TDA)--we reduce memory complexity from O(N * L) to O(1) for weights and O(N * k) for context, where k << L. By treating the KV-cache as a point cloud in latent space, we apply witness-complex-inspired sparsification to preserve persistent homological features of the context manifold. On a single NVIDIA RTX 4090, we empirically demonstrate 100 concurrent agents at 2.2 GB total VRAM, with theoretical capacity exceeding 1,000 agents before compute latency becomes the bottleneck. We further introduce Referential Injection, a non-intrusive KV-cache update mechanism that allows asynchronous sub-agents to influence primary generation without stream disruption.