The Context Collapse Paradox: Why Enterprise LLMs Fail With Massive Data Dumps By Akshat Raj Researcher & Founder, OnePersonAI ORCID: 0009-0005-8565-0145 The prevailing consensus across enterprise engineering teams is deceptively simple: if an LLM context window supports 128k, 200k, or 1M tokens, we should pipe our entire knowledge base directly into the prompt. Teams hook vector databases, raw Confluence wikis, Jira backlogs, and multi-year conversational histories directly into generative pipelines. The expectation is that the self-attention mechanism of the underlying Transformer will naturally function as an ad-hoc, in-memory query optimizer. In production, the opposite occurs. As context length expands, enterprise systems routinely suffer from instruction amnesia , fact confabulation , and runaway inference latency . This systemic breakdown is what we formalize as Context-Entropy Collapse (CEC) . We recently released the comprehensive mathematical proof and architectural speci...