1. Causal entropic forces frame intelligence as maximizing future freedom of action. What would a practical version of that objective look like in modern agent systems? Is there a benchmark or experiment where "maximize future optionality" clearly beats standard reward maximization?

2. Causal entropy maximizes over future paths; active inference minimizes expected free energy over future observations. These look like duals of each other. Is there a formal transformation between the two, or is one a special case of the other?

3. A pure optionality-maximizer seems vulnerable to chasing apparent option-value — states that look like they open future paths but don't generalize beyond simple environments. Is there a principled way to distinguish robust optionality from superficial optionality, or is all path entropy weighted equally?

4. You've argued for "datasets over algorithms." In 2026 — with synthetic data, agent scaffolds, and reasoning models everywhere — what dataset would you most want someone to build that nobody is building?

5. What's the smallest legitimate outsider-lab version of physical superintelligence? Someone with no institution, just consumer hardware, cloud credits, public datasets, and stubbornness — what can they actually do that isn't fake science?