Evaluation on AIBussin — AI applications, systems and books

Thinking in Primitives: Why AI Reasoning Should Learn to Point

Sun, 24 May 2026 13:16:50 +0100

From visual primitives to context-filtered reasoning, grounded verification, and AI movie repair

TL;DR

This post argues that AI reasoning should not operate over everything it can see, read, or detect. It should operate over the right primitives for the current task.

The paper Thinking with Visual Primitives shows that multimodal models reason better when they can point to visual entities using boxes and points. That solves a Reference Gap: language is often too vague to anchor reasoning to the right part of an image.

Beyond Hallucination Energy: A Three-Dimensional Framework for Reliable AI Outputs

Wed, 22 Apr 2026 10:35:46 +0100

🧩 1. TLDR

AI doesn’t just hallucinate. Sometimes it gives answers that are fluent, safe… and completely useless.

Most discussions about AI failure focus on hallucination:

making things up
getting facts wrong
fabricating sources

That’s real. It matters.

But it’s not the most dangerous failure mode in production systems.

There is a quieter one.

A more subtle one.

And in practice a more pervasive one.

AI systems often fail not by being wrong, but by failing to think at all.

Applied Policy: How to incorporate Policy and Hallucination in self-improving system

Wed, 18 Feb 2026 08:00:16 +0000

Building a Self-Improving AI: Cooperative ERL and Embed-RL in a Trace-Native Architecture

1. The Problem

Most self-improving AI systems fail for one of three reasons:

First, scalar reward collapse. Traditional reinforcement learning compresses multi-dimensional quality into a single scalar. This creates catastrophic interference: improving one axis (e.g., coherence) can degrade another (e.g., hallucination safety). The system optimizes for the blended metric, not the underlying objectives.

Second, representation drift. Embedding-based optimization without behavioral feedback creates geometric collapse. The embedding space becomes increasingly narrow, losing discriminative power. Similar queries map to identical regions. Diversity vanishes. The system becomes brittle.

From Evidence to Verifiability: Rebuilding Trust in AI Outputs 🔏

Tue, 03 Feb 2026 12:25:58 +0000

⏰ TLDR

This work shows that the hardest part of using AI in high-trust environments is not the model, but the policy. Once editorial policy is made explicit and executable, AI systems become interchangeable the real challenge is engineering reliable measurements and deterministic enforcement of those policies.

📋 Summary

AI systems are becoming deeply embedded in how we research, write, and reason. At the same time, their use in high-trust environments is under strain not because models are incapable, but because they are being deployed into settings that demand determinism, provenance, and enforceable rules.

Search–Solve–Prove: building a place for thoughts to develop

Sun, 02 Nov 2025 01:13:06 +0000

🌌 Summary

What if you could see an AI think not just the final answer, but the whole stream of reasoning: every search, every dead end, every moment of insight? We’re building exactly that: a visible, measurable thought process we call the Jitter. This post the first in a series shows how we’re creating the habitat where that digital thought stream can live and grow.

We’ll draw on ideas from: