Beyond Adaptation Speed: What Evolutionary Architecture Adds to the Superhuman Adaptable Intelligence Framework

Goldfeder, Wyder, Yann LeCun, and Shwartz-Ziv 2026 argue that AGI remains a poorly defined target, and that adaptation speed — Superhuman Adaptable Intelligence — offers a more tractable and scientifically grounded benchmark for AI progress. Their evolutionary critique of human-centric AI benchmarks is well-founded and directionally correct. This paper extends their argument by introducing the Five Task Model (5TM), a comparative framework developed through behavioral analysis of 1,530 species, which makes explicit the architectural structure that biological evolution produced under ESR (energy, safety, reproduction) constraints. The model identifies five basic adaptive informational tasks, organized in a gated, sequential, and cumulative architecture that defines the solution space for behavioral modulation across biological systems. The 5TM supports the evolutionary grounding of the Superhuman Adaptable Intelligence framework while introducing a structural dimension that adaptation speed metrics do not capture: how a system identifies what kind of problem it is facing before generating a response. Before a system can solve a problem, it must recognize that it is in one and identify it within a dynamic informational context. We formalize this distinction as the difference between Naive AI, which operates within externally specified task frames, and Strategic AI, which performs prior assessment of General Informational Flow and independently recognizes tasks before responding. This distinction defines an architectural threshold rather than a capability gradient. From this perspective, two implications follow. First, the Adaptation Speed Paradox: in naive architectures, improvements in adaptation speed amplify both solution quality and susceptibility to task framing. Second, the Asimov–Sheckley constraint: in systems operating with full multi-agent architecture, rule-based control functions as informational input rather than external constraint. Together, these results suggest that advancing toward human-like artificial intelligence is not a matter of scaling performance within existing architectures, but of assembling a cognitive architecture whose structure is already specified by the evolutionary record.