Benchmarking Continuous Context Computation for Enterprise AI

Abstract

As Enterprise AI systems increasingly rely on Retrieval-Augmented Generation (RAG) and Agentic architectures to perform multi-hop reasoning over organizational data, the dominant approach of independently retrieving and assembling context per query presents severe scalability limits. This paper investigates Continuous Context Computation (CCC)—a paradigm where enterprise information is continuously processed to maintain a reusable semantic representation, decoupling context assembly from query-time inference.

We propose a comprehensive benchmark framework designed to evaluate CCC against conventional baselines (Dense RAG, Hybrid RAG with Reranking, and Long-Context LLMs). Our reproducible framework utilizes two public datasets (MS MARCO, HotpotQA) and three large-scale synthetic enterprise datasets (spanning 512,000 documents across 9 simulated platforms). We establish a rigorous 12-level question taxonomy and introduce novel metrics for Context Reuse Ratio and Context Compression.

By isolating query-time computation from pre-computation, this research explicitly determines under what workloads, query patterns, and enterprise scales CCC provides measurable advantages, offering a formal economic model for break-even query volumes.

The Architectural Limits of Standard RAG

Traditional RAG systems treat context assembly as a strictly query-time operation. When a user issues a query, the system embeds the text, performs an Approximate Nearest Neighbor (ANN) search over an unstructured Vector Database, retrieves discrete text chunks, and concatenates them into an LLM’s prompt window.

While effective for localized, single-document retrieval, this architecture catastrophically degrades in complex enterprise environments:

1. Embedding Dilution: Unstructured vector similarity search inherently suffers from $O(N)$ scalability limits. As the enterprise corpus $N$ scales, the geometric distance between semantic concepts begins to collapse.
2. Cross-System Multi-Hop Failure: If an answer requires linking a Slack message to a Jira ticket, traditional RAG must either rely on the LLM's internal reasoning to bridge the chunks, or employ iterative agentic loops which compound latency.

Continuous Context Computation (CCC)

To resolve the query-time bottlenecks of RAG, CCC formally decouples context computation from inference computation. Data streams from disparate systems are ingested asynchronously. The CCC engine continuously extracts entities, resolves schemas, and computes relational edges in the background, maintaining a highly structured, temporally-aware Semantic Graph.

At query time, the system no longer performs a linear $O(N)$ scan. It identifies starting nodes within the pre-computed graph and traverses the explicitly maintained edges $E$. This reduces retrieval complexity to a localized $O(E)$ graph neighborhood traversal. By executing semantic alignment before the user asks a question, CCC achieves flat latency scaling and perfect provenance tracking.