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

> Source: <https://github.com/samamatariq3527/Deferred-Cognitive-Architecture>
> Published: 2026-10-31 16:34:19+00:00

**Author:** Samama Tariq

**Whitepaper PDF:** [Download Full Whitepaper](https://github.com/samamatariq3527/Deferred-Cognitive-Architecture/blob/main/dca_whitepaper.pdf)

**Official Citation DOI:** [https://doi.org/10.5281/zenodo.23839670](https://doi.org/10.5281/zenodo.23839670)

Modern Large Language Models (LLMs) incur quadratic computational complexity 

1. 
**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).
2. 
**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.
3. 
**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)}
}
```