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Theoretical framework explores value in biological circuits and AI alignment, suggesting implications for information measurement.
Structural Persistence Value (SPV) defines value as V = I·T/E, where I is effective information (decoder‑relative), T is persistence time, and E = E_maintain + E_decode is total energy cost. The paper proposes three hypotheses: (1) biological neural circuits have internalized an approximate V‑estimator; (2) AI alignment (RLHF/DPO) is reverse engineering of this estimator; (3) zero‑marginal‑cost replication of high‑K (I/E) structures leads to informational niche overlap, diluting marginal causal efficacy and collapsing T. A major correction is made: I is not non‑quantifiable – it has been implicitly quantified in large model parameters. The Three‑Calipers Cross‑Calibration Program operationalizes measurement. Five testable predictions, simulation evidence, and ethical boundaries are provided.
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liangruifeng (2026) studied this question.
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