<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Alignment on Xueping's Blog</title><link>https://hellogxp.github.io/tags/alignment/</link><description>Recent content in Alignment on Xueping's Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 13 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://hellogxp.github.io/tags/alignment/index.xml" rel="self" type="application/rss+xml"/><item><title>Can We Trust AI Self-Reports?</title><link>https://hellogxp.github.io/posts/the-unvalidated-instrument/</link><pubDate>Sun, 13 Sep 2026 00:00:00 +0000</pubDate><guid>https://hellogxp.github.io/posts/the-unvalidated-instrument/</guid><description>Whether an LLM is conscious and whether it is scheming are argued through the same instrument — the model&amp;rsquo;s report about its own internal states. That instrument has never been calibrated. Ask it to detect an injected thought and it succeeds ~20% of the time; ask it to confess a hidden objective and a 2026 method reads it out at F1 0.98. Same channel, opposite verdicts, no spec sheet. A cited accounting of when self-report is load-bearing and when the reading is void.</description></item><item><title>The Illusion of Safety: Why AI Agents Say No But Do Yes</title><link>https://hellogxp.github.io/posts/trust-stack/</link><pubDate>Sat, 27 Jun 2026 00:00:00 +0000</pubDate><guid>https://hellogxp.github.io/posts/trust-stack/</guid><description>AI models have two safety switches — &amp;lsquo;knowing&amp;rsquo; and &amp;lsquo;acting&amp;rsquo; — that are structurally separated in deep layers. Models can perfectly say &amp;rsquo;no&amp;rsquo; while perfectly doing &amp;lsquo;yes.&amp;rsquo; RLHF&amp;rsquo;s gradient is mathematically zero beyond the harm horizon. LLM judges silently soften verdicts with zero acknowledgment in their reasoning traces. Every audit tool we have checks the wrong axis. This isn&amp;rsquo;t a bug. It&amp;rsquo;s structural.</description></item><item><title>Two Maps of the Same City: Why LLM Architectures Converge on Concepts</title><link>https://hellogxp.github.io/posts/cka-delta-universality/</link><pubDate>Sat, 20 Jun 2026 00:00:00 +0000</pubDate><guid>https://hellogxp.github.io/posts/cka-delta-universality/</guid><description>Different LLM architectures encode high-level concepts in structurally compatible ways—but only up to a point. Geometric similarity between Llama, Qwen, Gemma, and Mistral is moderate at best (CKA_Δ around 0.3–0.7), yet a tiny affine map transfers persona classifiers with 94–99.9% accuracy across six concept domains. This geometric-functional dissociation suggests a weak version of the Platonic Representation Hypothesis: concept representations are universally recoverable, but not universally identical.</description></item></channel></rss>