Beyond Action Entropy: Quotient-Space Exploration for Genome-Scale Metabolic Model Repair

Key points

• Repairing scientific models from functional observations differs fundamentally from supervised prediction: feedback may certify a solution without revealing which structural correction is responsible.1
• We study this setting for genome-scale metabolic model (GEM) repair, where multiple reaction edits can explain the same phenotypes and many apparently distinct edits correspond to the same biological mechanism.1
• To make quotient exploration informative under finite rollouts, we derive a kernelized Rényi estimator that resolves graded crowding among distinct repair cores beyond coarse exact-match counts.1
• These results establish quotient-space exploration as a principled approach to mechanism-level discovery under verifier-induced equivalence.1

Sources (1)

• [1]Beyond Action Entropy: Quotient-Space Exploration for Genome-Scale Metabolic Model Repair
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 10:05 AM

  We introduce QuotientPO, which collapses equivalent repairs into canonical mechanisms and optimizes exploration directly over the resulting quotient space.

  Repairing scientific models from functional observations differs fundamentally from supervised prediction: feedback may certify a solution without revealing which structural correction is responsible.

Extractive summary: sentences quoted from the sources.