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

Warp-Cortex: An Asynchronous, Memory-Efficient Architecture for Million-Agent Cognitive Scaling on Consumer Hardware Jorge L. Ruiz Williams Warp Research https://github.com/JorgeLRW/warp-cortex January 6, 2026 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. 1 Introduction The paradigm of "System 2" thinking in LLMs where models pause to reason before generating has shown promise in improving accuracy. However, current implementations are serial: the model stops, thinks, and then continues. True biological cognition is parallel; while we speak, sub-processes monitor for errors, recall facts, and plan ahead. Replicating this parallelism in silicon is expensive. Running 10 concurrent 7B models requires ≈140GB of VRAM, well beyond consumer reach. Even with smaller models, the KV cache grows linearly with context length L and agent count N, leading to O(N · L) memory complexity. We propose Warp Cortex, an architecture that reduces this complexity to O(1) for weights and O(N · k) for memory, where k ≪L. By treating agents not as separate processes but as asynchronous threads sharing a single "brain" (model instance) and "memory" (synapse), we unlock massive scalability. 2 Related Work Topological Data Analysis for High-Dimensional Sparsification. The selection of representative landmarks from high-dimensional manifolds is a well-studied problem in computational topology. In prior work on medical imaging [1], we demonstrated that a hybrid metric balancing geometric coverage against inverse kernel density can reduce mean pairwise distances in full-brain MRI volumes by 30–60% while preserving persistent homological features via witness complexes. Warp Cortex extends this principle to the transformer’s latent space: we treat the Key-Value (KV) cache as a dynamic manifold and apply hybrid landmarking to achieve 98% context compression without semantic loss. Multi-Agent LLM Systems. Concurrent work has explored enabling multiple reasoning perspectives from language models. Yang and Zhang [2] introduce Bayesian Transformers for population intelligence, 1 arXiv:2601.01298v1 [cs.LG] 3 Jan 2026 Warp Cortex: Asynchronous Agent Councils 2 sampling diverse model instances via stochastic normalization layers. Their approach achieves behavioral diversity through Bayesian inference but maintains separate functional instances per sample. Warp Cortex addresses a complementary problem: rather than diversity, we focus on density—enabling 100+ concurrent agents to share a single model instance on consumer hardware. Our topological sparsification could enable practical deployment of their Bayesian populations. Mixture-of-Experts Architectures. Sparse conditional computation has been explored in Switch Transformers [3] and Mixtral [4], which route tokens to subsets of parameters. BitNet [5] demonstrates that extreme quantization can maintain model quality. These works optimize compute sparsity; Warp Cortex addresses context sparsity, compressing O(N · L) memory to O(N · k) through attention-based landmark selection inspired by topological witness theory. Efficient Inference. Modern inference systems rely on KV caching [6] for autoregressive efficiency. Warp Cortex introduces Referential Injection, a novel KV cache update mechanism that allows asynchronous sub-agents to influence generation without disrupting the primary stream—a capability not addressed by existing caching strategies. 3 Architecture 3.1 The River & Stream Topology Standard inference pipelines are synchronous. Warp Cortex implements a split topology: • The River (Main Agent): A high-priority CUDA stream dedicated to user interaction and persona maintenance. • The Stream (Side Agents): Multiple medium-priority CUDA streams that branch off to perform specific reasoning tasks (fact-checking, logic verification). These streams execute concurrently on the GPU. While the River generates token ti, a Stream can p

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