PACS — Building an Optimizer From Gradient Statistics

Sat, 08 Aug 2026 15:05:00 +0100

So far in Models From First Principles, every post has asked some version of the same question:

What should the model compute?

MR.Q gave us a scalar quality estimate.

EBT split one shared representation into Q, V and Policy.

SICQL made those heads explicit, replaceable components.

HRM introduced repeated computation over fast and slow latent states.

Tiny compressed iterative refinement into one recursive latent state.

From Photo Albums to Movies: Teaching AI to See Its Own Progress

Sun, 07 Sep 2025 08:50:43 +0100

This post details the implementation of:

PACS: Implicit Actor–Critic Coupling via a Supervised Learning Framework for RLVR
NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings within our self-improving AI, Stephanie.

The core idea is to move beyond static, single-point feedback to a richer, more dynamic form of learning: