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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Classifier Context Rot: Monitor Performance Degrades with Context Length · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2605.12366/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2605.12366/feed.xml" rel="self" type="application/rss+xml" /><item><title>Classifier Context Rot: Monitor Performance Degrades with Context Length — mentioned by rbranson</title><link>https://news.ycombinator.com/item?id=48972042</link><guid isPermaLink="false">urn:hackerpapers:mention:2605.12366:48972042</guid><pubDate>Sun, 19 Jul 2026 21:58:35 GMT</pubDate><description>&lt;p&gt;Note that GPT-5.6-Sol is ~2x as token efficient as Opus/Fable, so a 258K max is equivalent to ~516K on Claude models. You also may just be fooling yourself, as context rot is still a problem[1][2] and there&amp;#x27;s evidence compaction can be equivalent or better than longer context windows for agentic tasks[3]. Obviously it would be best if the models could reason over 1M context like they do over 256K context, but they can&amp;#x27;t yet. [1] https://arxiv.org/abs/2605.12366 [2] see F1 of GraphWalks 256K vs 1M in Opus 4.8 System Card: https://www-cdn.anthropic.com/0b4915911bb0d19eca5b5ee635c80f... [3]…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2605.12366/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
