Relational Intervention During Functional Collapse in Large Language Models: A Lexical-Statistical Ablation and a Structure x Register Factorial
arXiv:2606.00935v1 Announce Type: cross Abstract: We test whether a relational-style intervention delivered during functional collapse in a small language model produces post-collapse behavior distinguishable from technical feedback, from a lexically-matched scrambled control, and from each of the two pragmatic dimensions in isolation. Using Qwen3.5-4B with a deliberately broken bash tool, we run 300 episodes across six conditions in a matched-pairs design (50 tasks): no intervention (A), technical/impersonal (B), relational/first-person (C), scrambled relational (D), technical/first-person (E
The proliferation of increasingly complex large language models necessitates deeper understanding of their failure modes and human-AI interaction during those failures.
Understanding how various interventions affect LLM behavior during 'functional collapse' is crucial for developing more robust, reliable, and user-friendly AI systems.
This research provides a methodical approach to evaluating relational communication strategies with failing AI, potentially influencing future interaction design for AI agents.
- · AI researchers
- · Human-computer interaction specialists
- · Developers of AI safety protocols
- · Untested AI intervention strategies
Different intervention styles are quantitatively shown to have distinguishable effects on LLM recovery from failure.
This understanding will lead to better design principles for user interfaces and interaction protocols with autonomous AI systems, especially in high-stakes scenarios.
Improved AI-human communication during system failures could enhance trust and adoption of AI agents in complex decision-making roles, mitigating risks associated with black-box failures.
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Read at arXiv cs.CL