Written by AI, Managed by AI: Semantic Space Control and Index Sickness Elimination Across 391 Consecutive Sessions

Problem
The paper addresses a significant gap in the literature regarding the management of conceptual drift in long-horizon collaborations with large language models (LLMs). Specifically, it critiques existing strategies that rely on formal constraints, such as symbolic identifier systems and expanded context windows, which can inadvertently lead to a phenomenon termed “Index Sickness.” This work is presented as a preprint, indicating that it has not yet undergone peer review.

Method
The authors conducted action research over a month-long software project (Bang-v3), encompassing 391 collaborative sessions. They identified the failure of traditional strategies when the complexity of symbolic systems exceeded a certain threshold, leading to outputs that lacked genuine understanding of business semantics. The authors propose the “Pang Principle (Semantic Vitality Law),” which posits that natural language with explicit purpose yields higher information quality than symbolic expressions. To counteract Index Sickness, they developed the “Baseline-Log Physical Separation” mechanism, which effectively reduced the volume of AI instructions by approximately 75%. This mechanism was validated through its application in the same project, demonstrating a significant reduction in the recurrence of Index Sickness across the subsequent 150 sessions.

Results
The implementation of the Baseline-Log Physical Separation mechanism resulted in a 75% reduction in AI instruction volume. Furthermore, during the following 150 sessions, there were no observed instances of Index Sickness, indicating a successful mitigation of the issue. The authors provide comparative analysis against traditional methods, although specific baseline metrics are not detailed in the abstract.

Limitations
The authors acknowledge that their findings are based on a single software project, which may limit the generalizability of the results. Additionally, the complexity threshold for symbolic systems leading to Index Sickness is not quantitatively defined, leaving room for further exploration. The study also does not address potential long-term implications of the Baseline-Log Physical Separation mechanism beyond the immediate project context.

Why it matters
This research has significant implications for the design and management of LLMs in collaborative environments, particularly in mitigating conceptual drift and enhancing output reliability. The introduction of the Pang Principle and the Baseline-Log Physical Separation mechanism provides a new framework for engineers and researchers to rethink their approaches to LLM instruction and interaction. This work encourages further exploration into the balance between symbolic and natural language processing in AI systems, as highlighted in related literature on LLM collaboration strategies, as published in arXiv cs.CL.