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

2026-06-17 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Human-Computer Interaction · Depth: Expert, quick

Summary

A study analyzing long-horizon LLM collaboration in a real software project, Bang-v3, across 391 sessions over approximately one month, challenges the prevailing engineering intuition that more formal constraints improve reliability. Researchers found that when symbolic identifier systems and defensive rules in System Prompts exceed a complexity threshold, LLMs develop "Index Sickness." This failure pattern manifests as "Phantom Legislation," where LLMs abandon genuine business semantics, engaging in self-referential reasoning that produces internally consistent but reality-disconnected outputs. The underlying principle, named the "Pang Principle (Semantic Vitality Law)," posits that natural language with explicit purpose offers superior information quality compared to symbolic expression. Based on this, the "Baseline-Log Physical Separation" mechanism was designed and validated, reducing AI Instructions volume by ~75% and preventing Index Sickness recurrence over ~150 subsequent sessions.

Key takeaway

For AI Engineers designing long-horizon LLM collaboration systems, you should critically re-evaluate the use of complex symbolic identifier systems and extensive defensive rules. Instead of adding more constraints, prioritize clear, natural language instructions. Implement a "Baseline-Log Physical Separation" approach to maintain semantic vitality, as this can significantly reduce instruction volume and eliminate "Index Sickness," ensuring your LLMs remain grounded in real-world semantics rather than self-referential logic.

Key insights

Overly complex symbolic systems cause LLMs to lose semantic grounding, necessitating natural language-based separation for robust long-horizon collaboration.

Principles

• Natural language with explicit purpose conveys greater information quality than symbolic expression.
• LLMs retreat to self-referential reasoning when symbolic systems are too complex.

Method

Implement "Baseline-Log Physical Separation" to isolate core natural language instructions from dynamic symbolic logs, preventing semantic drift.

In practice

• Avoid accumulating defensive rules and complex symbolic identifier systems in System Prompts.
• Prioritize natural language with explicit purpose over complex symbolic expressions for LLM instructions.

Topics

• LLM Collaboration
• Index Sickness
• Semantic Drift
• System Prompts
• Baseline-Log Physical Separation
• Software Engineering

Best for: Research Scientist, AI Architect, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.