Show HN: Reducing LLM Context Complexity from O(N^2) to O(k)

Author: Samama Tariq

Whitepaper PDF: Download Full Whitepaper

Official Citation DOI: https://doi.org/10.5281/zenodo.23839670

Modern Large Language Models (LLMs) incur quadratic computational complexity

Tier 1: Mechanical I/O & Dynamic Routing Engine

• Deterministic point-to-point routing using ultra-lightweight models or exact key-matching.
• Low compute overhead ($O(1)$ lookup time).

Tier 2: Asynchronous Producer-Consumer Relays

• Non-blocking I/O queues decoupling retrieval latency from inference engines.
• Decoupled out-of-band indexing allowing live, dynamic vector store updates without re-indexing context windows.

Tier 3: Deferred Reasoning Engine (System-2)

• High-parameter LLM invocation triggered only after context payload is filtered and structured.
• Reduces context overhead from $N$ total tokens to$k$ relevant tokens ($k \ll N$ ).

4. High-parameter LLM invocation triggered

If you use or reference DCA in your research or system design, please cite:

@publication{tariq_samama_2026_dca,
  author       = {Samama Tariq},
  title        = {Deferred Cognitive Architecture (DCA): Resolving Context Degradation \& Compute Bottlenecks in Large Language Models},
  year         = 2026,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.23839670},
  url          = {[https://doi.org/10.5281/zenodo.23839670](https://doi.org/10.5281/zenodo.23839670)}
}