The FIL Hypothesis: Inductive Biases Help with Kernel Engineering
The Bitter Lesson, which posits that general-purpose methods that scale with computation and data ultimately outperform those with built-in human knowledge, has become a dominant paradigm in the era of Large Language Models. We revisit this principle by observing a new and critical scaling dimension: the duration of the Feedback Information Loop (FIL), the time required for a system to receive a verification signal after generating a prediction. Most historic successes in Artificial Intelligence (AI) have benefited from near instantaneous feedback (e.g., games or classification tasks), but we argue that future AI applications in science and the physical world will inherently involve FILs ...
| Authors | Nikolai Rozanov, Subhabrata Dutta, Preslav Nakov et al. |
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| Primary category | cs.AI |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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