New Paper: Measuring Open-Ended Evolution via Undecidability in Boolean Networks
AnnaCiaunica · x · 2026-09-04
Amahury López-Díaz, Carlos Gershenson and colleagues published an open-access paper in npj Systems Biology and Applications on whether structured novelty can be measured and engineered.
Key points:
- They introduce Ω, a simple model-independent metric summarizing the residence-time-weighted contribution of attractor cycle lengths across recurrent episodes in a finite observation window;
- Ω is zero for single attractors, helping distinguish genuine open-ended evolution — recurrent production of novel phenotypic states — from rapid settling or unstructured noise;
- The substrate is random Boolean networks, characterized through undecidability mechanisms.
More from Research
- O'Reilly builds a working data vocabulary for the semantic era, from warehouses to ontologies — rseroter · 2026-09-22
- Researchers Speed Up DSA Method for Comparing Neural Dynamics, Making It Generalizable Across Domains — GretaTuckute · 2026-09-22
- Glance reads structured visual answers from a frozen 4B VLM, cutting GPU cost up to 85% — multiply_matrix · 2026-09-22
- SkillLift cuts agent skill-evolution token cost 40-70% by ranking, not rollouts — dair_ai · 2026-09-22
- FAIR & NYU scaling law paper pushes fits down to 4M-param models — giffmana · 2026-09-22
- Salesforce: Fine-tuning a weak model to copy Gemini drops success 15%; correcting its own failures works — rohanpaul_ai · 2026-09-22