ml-paradigm-impermanence-doubly-determined
IN derived (depth 3)
Created 2026-06-21T10:27:02+00:00 · Reviewed 2026-06-21T15:37:01+00:00
No current ML paradigm can persist: theoretical completeness is demonstrated insufficient for survival (GANs' closed-form analysis didn't prevent displacement by diffusion models), and empirical dominance is independently fragile (pretrain-finetune is standard yet empirically hurtful in some settings) — paradigm impermanence is overdetermined by both theoretical and empirical evidence.
Justifications
SL — Both routes to paradigm durability — theoretical completeness (d2) and empirical dominance (d2) — independently fail
Antecedents (all must be IN):
- IN theoretical-completeness-no-guarantee-of-paradigm-durability — Theoretical completeness does not guarantee paradigm durability — GANs had a notably complete analytical characterization (closed-form optimal discriminator, JSD minimization proof, unique equilibrium) yet were largely supplanted by diffusion models from approximately 2022 onward, suggesting that factors beyond theoretical elegance — possibly including training reliability — may play a significant role in determining which paradigms persist.
- IN dominant-paradigms-empirically-fragile-and-transient — The most successful ML paradigms are simultaneously dominant and fragile — pretrain-then-finetune is standard practice yet empirically hurtful in some transfer settings, GANs dominated generative modeling for years yet were displaced by diffusion — suggesting that current best practices are locally optimal recipes liable to succession rather than fundamental principles.
Dependents
These beliefs depend on this one:
- IN mathematical-completeness-counterproductive-for-survival — Mathematical completeness can become counterproductive for paradigm survival in ML — SVMs illustrate how completeness creates its own scaling barriers (three decades of development produced complexity that compounds with problem size), while broader evidence suggests that neither theoretical elegance nor empirical dominance is sufficient to guarantee persistence, complicating the expected value of mathematical rigor.