<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
	<channel>
		<title>Cognitive Architectures on AIBussin — AI applications, systems and books</title>
		<link>https://aibussin.com/tags/cognitive-architectures/</link>
		<description>Recent content in Cognitive Architectures on AIBussin — AI applications, systems and books</description>
		<generator>Hugo</generator>
		<language>en-US</language>
		
		
		
		
			<lastBuildDate>Mon, 18 May 2026 11:14:15 +0100</lastBuildDate>
		
			<atom:link href="https://aibussin.com/tags/cognitive-architectures/index.xml" rel="self" type="application/rss+xml" />
			<item>
				<title>Delta Memory: Cargo-Culting Human Memory with Search</title>
				<link>https://aibussin.com/post/delta/</link>
				<pubDate>Mon, 18 May 2026 11:14:15 +0100</pubDate>
				<guid>https://aibussin.com/post/delta/</guid>
				<description>&lt;blockquote&gt;&#xA;&lt;p&gt;AI systems today have no idea why their own memory changes, that’s the problem we are trying to solve in this post.&lt;/p&gt;&#xA;&lt;/blockquote&gt;&#xA;&lt;h2 id=&#34;summary&#34;&gt;Summary&lt;/h2&gt;&#xA;&lt;p&gt;Most AI memory systems start from a practical place: retrieval. Retrieval is useful, scalable, and often the right tool for the job. But if we want systems that interact with humans in more human‑like ways, we need a different analogy, not storage, but &lt;strong&gt;thinking&lt;/strong&gt;.&lt;/p&gt;&#xA;&lt;p&gt;Humans don’t store perfect records. We don’t retrieve exact text or replay video files. What we call “memory” is a shifting landscape of associations, impressions, weights, and patterns. When you recall something, you’re not pulling a file from disk, you’re running a &lt;strong&gt;search&lt;/strong&gt; across your internal world, shaped by everything you’ve lived through.&lt;/p&gt;</description>
			</item>
			<item>
				<title>Epistemic Engines: Building Reflective Minds with Belief Cartridges and In-Context Learning</title>
				<link>https://aibussin.com/post/epistemic-engines/</link>
				<pubDate>Wed, 02 Jul 2025 14:15:50 +0100</pubDate>
				<guid>https://aibussin.com/post/epistemic-engines/</guid>
				<description>&lt;h2 id=&#34;-summary-building-the-engine-of-understanding&#34;&gt;🔍 Summary: Building the Engine of Understanding&lt;/h2&gt;&#xA;&lt;p&gt;This is not a finished story. It’s the beginning of one and likely the most ambitious post we’ve written yet.&lt;/p&gt;&#xA;&lt;p&gt;We&amp;rsquo;re venturing into new ground: designing epistemic engines modular, evolving AI systems that don’t just respond to prompts, but build understanding, accumulate beliefs, and refine themselves through In-Context Learning.&lt;/p&gt;&#xA;&lt;p&gt;In this series, we’ll construct a self-contained system separate from our core framework Stephanie that runs its own pipelines, evaluates its own beliefs, and continuously improves through repeated encounters with new data. Its core memory will be made of cartridges: scored, structured markdown artifacts distilled from documents, papers, and the web. These cartridges form a kind of belief substrate that guides the system’s judgments.&lt;/p&gt;</description>
			</item>
	</channel>
</rss>
