Written by AI, Managed by AI: Semantic Space Control and Index Sickness Elimination Across 391 Consecutive Sessions
arXiv:2606.19121v1 Announce Type: cross Abstract: The prevailing engineering intuition for addressing conceptual drift in long-horizon LLM collaboration is to trade more formal constraints for more reliable outputs -- designing symbolic identifier systems, accumulating defensive rules in System Prompts, expanding context windows. Our engineering record shows that in long-horizon settings, this direction may produce effects contrary to design intent. Using action research methods in a real software project (Bang-v3) spanning approximately one month and 391 collaborative sessions, we document an
The paper addresses a critical challenge emerging as LLM applications move beyond prototypes into long-term, complex collaborative environments, where conceptual drift and maintenance become significant issues.
This research provides empirical evidence and a potential solution for managing the inherent instability of long-horizon AI agent interactions, which is crucial for the deployment of reliable autonomous systems.
The prevailing intuition for managing LLM conceptual drift, relying on more formal constraints, is challenged, suggesting a need for alternative, potentially more dynamic, control mechanisms.
- · AI agent developers
- · Companies implementing autonomous workflows
- · Researchers in AI collaboration
- · Developers relying solely on rigid, constraint-based LLM management paradigms
- · Systems unprepared for semantic drift
Increased exploration and adoption of dynamic semantic control methods for AI agents.
Accelerated development of more robust and less 'sickness-prone' self-managing AI architectures.
Shift in AI development methodologies towards adaptive, learning-based control over static, rule-based systems, potentially simplifying complex AI deployments.
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Read at arXiv cs.CL