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.

翻译：当前的多智能体大语言模型（LLM）框架存在内存线性扩展的问题，导致在消费级硬件上实现“系统2”并行推理变得不切实际。我们提出了Warp Cortex，一种异步架构，它通过将智能体逻辑与物理内存解耦，理论上能够支持百万级智能体的认知扩展。通过单例权重共享和一种受拓扑数据分析（TDA）中混合地标技术启发的新型拓扑突触，我们将内存复杂度从权重的O(N * L)和上下文的O(N * L)分别降低到权重的O(1)和上下文的O(N * k)，其中k << L。通过将KV缓存视为潜在空间中的一个点云，我们应用受见证复形启发的稀疏化技术，以保留上下文流形的持久同伦特征。在单个NVIDIA RTX 4090上，我们通过实验展示了100个并发智能体仅占用2.2 GB总显存，并且在计算延迟成为瓶颈之前，理论容量可超过1000个智能体。我们进一步引入了参考注入，一种非侵入式的KV缓存更新机制，允许异步子智能体在不中断主生成流的情况下影响主生成过程。