In Brief
Warp-Cortex lets a single model instance host 100+ concurrent reasoning agents on a 24GB consumer GPU by compressing per-agent context memory by about 98%, enabling private and low-cost multi-agent systems.
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Key Findings
A shared, asynchronous architecture lets many lightweight agent threads use one model and one compressed memory store instead of separate full model copies. By treating the model’s attention cache as a dynamic space and selecting a small set of representative landmarks, per-agent memory drops dramatically while preserving semantics. The system runs side reasoning tasks concurrently with the main interaction thread so sub-agents can check facts or plan ahead without interrupting user-facing output. Retrieval-Augmented Generation
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Data Highlights
198% compression of the attention (key-value) context cache reported with no semantic loss using topological landmarking.
2Demonstrated theoretical capacity to host 100+ concurrent agents on an NVIDIA RTX 4090 (24 GB) versus ~140 GB needed to run ten independent 7B models — >10× agent density increase.
3Architectural memory growth shifts from proportional to agents × context length to proportional to agents × k (k ≪ context length), while model weights remain shared (constant per-device).
What This Means
Engineers building local multi-agent setups and teams wanting to run private reasoning pipelines can use this to drop cloud costs and keep sensitive data on-premises. Product and research leaders evaluating agent orchestration should consider this architecture when they need many concurrent evaluators, fact-checkers, or planning sub-agents without buying large GPU clusters. Consensus-Based Decision Pattern
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Learn MoreConsiderations
Claims are driven by an architectural design and theoretical evaluation on a 24GB GPU; full empirical benchmarks across tasks and latency profiles are limited in the report. The approach depends on GPU stream concurrency and CUDA tooling, so portability to non-NVIDIA hardware or CPU-only environments may be constrained. Compression introduces an abstraction layer (landmark selection and referential injection) that could complicate debugging and may trade subtle consistency or timing behaviors for memory savings. Context Drift
Methodology & More
Warp-Cortex replaces the common approach of running multiple independent model copies with a thread-like design where one model instance is shared and many asynchronous sub-agents run as concurrent streams. A high-priority main stream (the River) handles user interaction while medium-priority side streams (the Stream) run specialized reasoning jobs — for example, fact-checking or logical verification — on slightly earlier tokens. Side streams can inject references into the shared attention cache without breaking the main generation, enabling continuous background "System 2" style reasoning. Chain of Thought Pattern The key technical trick is treating the attention key-value cache as a dynamic high-dimensional manifold and applying topological landmark selection to keep only a small representative set of entries. That compresses context memory by about 98% while preserving semantic fidelity. The result is a memory complexity that scales with the number of agents times a small k (k ≪ full context length) instead of the full context length per agent, and model weights no longer multiply with agent count. Implemented with PyTorch and CUDA streams and evaluated for theoretical capacity on an RTX 4090 (24 GB), Warp-Cortex enables running 100+ lightweight agents locally, which unlocks on-device privacy, large cost savings versus per-token cloud APIs, and simpler agent coordination with no network round-trips. Tree of Thoughts Pattern
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Credibility Assessment:
Single author with no affiliations or reputation signals and arXiv preprint — low credibility.