Hrm on AIBussin — AI applications, systems and books

HRM — Hierarchical Reasoning With Fast and Slow Recurrent State

Sat, 08 Aug 2026 14:48:00 +0100

HRM — Hierarchical Reasoning With Fast and Slow Recurrent State

The previous models in this series were mostly one-pass models.

MR.Q took two embeddings and produced one score.

EBT kept the same basic structure but added several heads.

SICQL made those heads explicit components.

The architecture grew, but the shape of the computation was still familiar:

input
 ↓
encoder
 ↓
representation
 ↓
heads
 ↓
outputs

HRM changes the question.

The Space Between Models Has Holes: Mapping the AI Gap

Wed, 22 Oct 2025 20:30:36 +0100

🌌 Summary

What if the most valuable insights in AI evaluation aren’t in model agreements, but in systematic disagreements?

This post reveals that the “gap” between large and small reasoning models contains structured, measurable intelligence about how different architectures reason. We demonstrate how to transform model disagreements from a problem into a solution, using the space between models to make tiny networks behave more like their heavyweight counterparts.

We start by assembling a high-quality corpus (10k–50k conversation turns), score it with a local LLM to create targets, and train both HRM and Tiny models under identical conditions. Then we run fresh documents through both models, collecting not just final scores but rich auxiliary signals (uncertainty, consistency, OOD detection, etc.) and visualize what these signals reveal.

Case Based Reasoning: Teaching AI to Learn From itself

Wed, 03 Sep 2025 23:52:29 +0100

✨ Summary

Imagine an AI that gets smarter every time it works not by retraining on massive datasets, but by learning from its own reasoning and reflection, just like humans.

Most AI systems are frozen in time. Trained once, deployed forever, they never learn from mistakes or build on successes. Real intelligence human or artificial doesn’t work that way. It learns from experience.

This is the vision behind Stephanie: a self-improving AI that gets better every time it acts, not by fine-tuning, but by remembering, reusing, and revising its reasoning.