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

Jorge L. Ruiz Williams

TL;DR

This work tackles the memory bottleneck of parallel, multi-agent reasoning in LLMs by decoupling agent logic from memory using Warp Cortex. It introduces a split River & Stream topology, Singleton Weight Sharing, a Topological Synapse for landmark-based context sparsification, and Referential Injection to update KV caches asynchronously, achieving $O(1)$ weight memory and $O(N * k)$ context memory. The approach enables 100 concurrent agents on a single RTX 4090 with theoretical capacity over $10^3$ agents before latency dominates, representing a practical path to edge-scale, privacy-preserving multi-agent cognition. This architecture has potential to transform local AI deployments by enabling large-scale, parallel reasoning without cloud resources while preserving semantic structure through topological sparsification.

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.

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

TL;DR

This work tackles the memory bottleneck of parallel, multi-agent reasoning in LLMs by decoupling agent logic from memory using Warp Cortex. It introduces a split River & Stream topology, Singleton Weight Sharing, a Topological Synapse for landmark-based context sparsification, and Referential Injection to update KV caches asynchronously, achieving weight memory and context memory. The approach enables 100 concurrent agents on a single RTX 4090 with theoretical capacity over agents before latency dominates, representing a practical path to edge-scale, privacy-preserving multi-agent cognition. This architecture has potential to transform local AI deployments by enabling large-scale, parallel reasoning without cloud resources while preserving semantic structure through topological sparsification.

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.
Paper Structure (17 sections, 2 equations, 1 figure, 2 tables)

This paper contains 17 sections, 2 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Architecture
  4. The River & Stream Topology
  5. The Prism: Singleton Weight Sharing
  6. The Topological Synapse
  7. The Cortex Router: Dynamic Delegation
  8. The Validation Gate
  9. Referential Injection
  10. Implementation
  11. Evaluation
  12. Theoretical Scalability
  13. Empirical Results
  14. Conclusion
  15. Implications for Edge AI
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Warp Cortex Architecture: All agents share a single model instance (Prism). The Synapse provides $O(k)$ context compression. Referential Injection (red) updates the Main Agent's KV cache.