Show HN: PILF, The ultimate solution to catastrophic oblivion on AI models

Summary

Technical Notes: PILF (Predictive Integrity Learning Framework) Document Version: 3.0 Core Concept: A cognitive learning framework designed to transform fixed hyperparameters (like learning rate, model capacity) into dynamic policies driven in real-time by the intrinsic "surprise" ( Surprise ) of data. It is essentially an adaptive hyperparameter scheduling algorithm that allows a model to autonomously decide "how much to learn" and "with what capacity to learn" based on the value of the learning content. This framework originates from the Integrated Predictive Workspace Theory, with further details available in the paper at https://github.com/dmf-archive/IPWT. 1. Design Philosophy: From "Fixed Rules" to "Dynamic Policies" Traditional training paradigms rely on manually set hyperparameters that are typically fixed or decay according to a predetermined schedule throughout the training process. This "one-size-fits-all" approach ignores the vast differences in learning value contained in different data batches. PILF's design philosophy is: to replace static, human-set rules with dynamic, data-driven policies. It no longer blindly uses a fixed learning rate or model capacity. Instead, it dynamically and proportionally adjusts its learning behavior by assessing the Surprise from each data batch: Dynamic Learning Rate: When Surprise is moderate, it signals valuable "learnable zone" information, and the system assigns a higher learning rate. When Surprise is too low (redundant information) or too high (anomalous information), it assigns a learning rate close to zero, naturally achieving "ignore" and "reject" effects. This directly replaces manually set learning rate schedulers. Dynamic Capacity: In a Mixture-of-Experts (MoE) architecture, Surprise not only adjusts the learning rate but also determines the number of "experts" k to activate. Simple tasks (low Surprise ) require only a few experts, while complex tasks (high Surprise ) dynamically engage more experts. This rep...