Version 1 (April 2026)
Published on Preprints.org: 10.20944/preprints202604.1390.v1
Known Issues & Imprecisions
The following issues have been identified in the current version of the paper based on community feedback (e.g., via Reddit discussions). These will be addressed in the next revision.
- 2026-05-13:
Expansion of the theoretical comparison section:
The current in-text treatment of major consciousness theories (GWT, IIT, AST, etc.) is too compressed, leading to potential overclaiming of theoretical alignment. Future revisions should integrate the Covers / Sticks Out structural framework into the manuscript. Specifically, incorporating the FAQ's comparison table as a formal figure or table would provide more nuance regarding how FC overlaps with or deviates from existing theories. - 2026-05-13:
Reproducibility and variable individuation policy:
To ensure the FCS metric is applied consistently across different systems, a clearer variable individuation policy is required. Future revisions should include a formal checklist of decisions an evaluator must make before calculation (defining the temporal horizon, information depth, ...) to improve the reproducibility of the worked examples. - 2026-05-13:
Need for additional FSMA evaluation on diverse datasets:
The current demonstration of FSMA only uses one text (Mark on the Wall) with SBR top-down priors. To prove the generalizability of the metric, future revisions should include evaluations on wildly different text sources, ensuring the assessment is not over-fitted to the specific characteristics of the SBR instantiation. - 2026-05-13: Justification for applying Bialek et al. scaling across diverse substrates: The manuscript uses the scaling law from Bialek et al. (2001) as a universal model for Reasoning Power (P). However, the applicability of this information-theoretic bound across widely different architectures—such as Model Predictive Control (MPC) in robotics versus transformer-based inference in LLMs—requires more explicit justification. The next revision should address whether the logarithmic scaling of predictively relevant information holds uniformly or if architectural constants significantly alter the functional comparison.