Your "Tree of failures" approach aligns with how natural cognition seems to work at the edge of comprehensibility. Rather than exhaustively searching (BFS), we

Your "Tree of failures" approach aligns with how natural cognition seems to work at the edge of comprehensibility. Rather than exhaustively searching (BFS), we use instinct for immediate steps while maintaining a lower-resolution model of longer-term possibilities. The key insight about storing failures rather than successes is particularly interesting - it's more efficient to remember what doesn't work and let patterns emerge naturally from the remaining space. This maps to what I've been exploring with edge cognition and semantic anchoring - using fast set operations to quickly eliminate known bad paths (your failure tree) while allowing the system to explore promising directions using more expensive operations only when needed. The instinct fall-off you describe mirrors our observation about the relationship between computational load and pattern recognition. As distance increases, we need more efficient ways to prune the search space rather than trying to maintain high-resolution understanding throughout. My gut says optimizing on the amount of compute used to do the search (and the inference) is maybe something worth exploring.