Mirror Theory introduces viable path entropy for AI capability measurement

Researchers have introduced Mirror Theory, which proposes evaluating intelligent systems based on their capacity for sustained, coherent continuations under repeated reflection. This theory is operationalized through viable path entropy (VPE), a measure of verified continuation capacity within a finite budget. Experiments on Qwen2.5-Instruct models using the GSM8K benchmark demonstrated that increasing the token budget significantly enhances verified reachability and mode diversity. Notably, the Qwen2.5-1.5B model exhibited a stronger mirror horizon than the larger Qwen2.5-3B, suggesting that capability is defined by accessible verified continuation capacity rather than just parameter count. AI

IMPACT Introduces a new theoretical framework for evaluating AI capability beyond simple accuracy, potentially influencing future AI development and benchmarking.

RANK_REASON This is a research paper introducing a new theoretical framework and experimental results for measuring AI capability. [lever_c_demoted from research: ic=1 ai=1.0]

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