<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
	<channel>
		<title>Summarization Evaluation on AIBussin — AI applications, systems and books</title>
		<link>https://aibussin.com/tags/summarization-evaluation/</link>
		<description>Recent content in Summarization Evaluation on AIBussin — AI applications, systems and books</description>
		<generator>Hugo</generator>
		<language>en-US</language>
		
		
		
		
			<lastBuildDate>Thu, 05 Feb 2026 13:29:55 +0000</lastBuildDate>
		
			<atom:link href="https://aibussin.com/tags/summarization-evaluation/index.xml" rel="self" type="application/rss+xml" />
			<item>
				<title>Hallucination Energy: A Geometric Foundation for Policy-Bounded AI</title>
				<link>https://aibussin.com/post/energy/</link>
				<pubDate>Thu, 05 Feb 2026 13:29:55 +0000</pubDate>
				<guid>https://aibussin.com/post/energy/</guid>
				<description>&lt;h2 id=&#34;-summary&#34;&gt;🚀 Summary&lt;/h2&gt;&#xA;&lt;p&gt;This post presents the current research draft and implementation of a geometric framework for bounding stochastic language models through deterministic policy enforcement.&lt;/p&gt;&#xA;&lt;p&gt;The central contribution is a scalar metric termed &lt;strong&gt;Hallucination Energy&lt;/strong&gt;, defined as the projection residual between a claim embedding and the subspace spanned by its supporting evidence embeddings. This metric operationalizes grounding as a measurable geometric quantity.&lt;/p&gt;&#xA;&lt;p&gt;We proceed in three stages:&lt;/p&gt;&#xA;&lt;ol&gt;&#xA;&lt;li&gt;&lt;strong&gt;Formal Definition&lt;/strong&gt; a draft manuscript introducing Hallucination Energy, its mathematical formulation, and its role within a policy-controlled architecture.&lt;/li&gt;&#xA;&lt;li&gt;&lt;strong&gt;Empirical Evaluation&lt;/strong&gt; structured calibration and adversarial stress testing across multiple domains to assess the robustness and limits of projection-based grounding.&lt;/li&gt;&#xA;&lt;li&gt;&lt;strong&gt;Applied Validation&lt;/strong&gt; large-scale evaluation on 10,000 samples from the HaluEval summarization benchmark, demonstrating that projection-based containment functions as a strong first-order grounding signal in a real generative setting.&lt;/li&gt;&#xA;&lt;/ol&gt;&#xA;&lt;p&gt;This work does not claim to solve hallucination. Rather, it characterizes the boundary of projection-based grounding, establishes its suitability as a deterministic policy scalar, and documents both its strengths and its structural limitations.&lt;/p&gt;</description>
			</item>
	</channel>
</rss>
