{"slug": "show-hn-reducing-llm-context-complexity-from-o-n-2-to-o-k", "title": "Show HN: Reducing LLM Context Complexity from O(N^2) to O(k)", "summary": "Samama Tariq published a whitepaper on Zenodo (DOI 10.5281/zenodo.23839670) describing a Deferred Cognitive Architecture (DCA) that aims to cut LLM context cost from O(N^2) to O(k) by invoking a high-parameter model only after a Tier 1 routing engine and Tier 2 asynchronous producer-consumer relays filter the payload down to k relevant tokens, where k is much smaller than N. The architecture's Tier 1 uses deterministic point-to-point routing with O(1) lookup, and Tier 2 decouples retrieval latency from inference through non-blocking I/O queues and out-of-band indexing that updates vector stores without re-indexing context windows. The proposal targets context degradation and compute bottlenecks in large language models, though the source reports no benchmarks or measured results.", "body_md": "**Author:** Samama Tariq\n\n**Whitepaper PDF:** [Download Full Whitepaper](https://github.com/samamatariq3527/Deferred-Cognitive-Architecture/blob/main/dca_whitepaper.pdf)\n\n**Official Citation DOI:** [https://doi.org/10.5281/zenodo.23839670](https://doi.org/10.5281/zenodo.23839670)\n\nModern Large Language Models (LLMs) incur quadratic computational complexity \n\n1. \n**Tier 1: Mechanical I/O & Dynamic Routing Engine**\n  - Deterministic point-to-point routing using ultra-lightweight models or exact key-matching.\n  - Low compute overhead ($O(1)$ lookup time).\n2. \n**Tier 2: Asynchronous Producer-Consumer Relays**\n  - Non-blocking I/O queues decoupling retrieval latency from inference engines.\n  - Decoupled out-of-band indexing allowing live, dynamic vector store updates without re-indexing context windows.\n3. \n**Tier 3: Deferred Reasoning Engine (System-2)**\n  - High-parameter LLM invocation triggered *only* after context payload is filtered and structured.\n  - Reduces context overhead from $N$ total tokens to$k$ relevant tokens ($k \\ll N$ ).\n4. High-parameter LLM invocation triggered \n\nIf you use or reference DCA in your research or system design, please cite:\n\n```\n@publication{tariq_samama_2026_dca,\n  author       = {Samama Tariq},\n  title        = {Deferred Cognitive Architecture (DCA): Resolving Context Degradation \\& Compute Bottlenecks in Large Language Models},\n  year         = 2026,\n  publisher    = {Zenodo},\n  doi          = {10.5281/zenodo.23839670},\n  url          = {[https://doi.org/10.5281/zenodo.23839670](https://doi.org/10.5281/zenodo.23839670)}\n}\n```\n\n", "url": "https://wpnews.pro/news/show-hn-reducing-llm-context-complexity-from-o-n-2-to-o-k", "canonical_source": "https://github.com/samamatariq3527/Deferred-Cognitive-Architecture", "published_at": "2026-10-31 16:34:19+00:00", "updated_at": "2026-10-31 16:51:26.844717+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "ai-infrastructure", "artificial-intelligence"], "entities": ["Samama Tariq", "Deferred Cognitive Architecture", "Zenodo"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/show-hn-reducing-llm-context-complexity-from-o-n-2-to-o-k", "markdown": "https://wpnews.pro/news/show-hn-reducing-llm-context-complexity-from-o-n-2-to-o-k.md", "text": "https://wpnews.pro/news/show-hn-reducing-llm-context-complexity-from-o-n-2-to-o-k.txt", "jsonld": "https://wpnews.pro/news/show-hn-reducing-llm-context-complexity-from-o-n-2-to-o-k.jsonld"}}