<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>securesein — Deep learning</title><description>Architectures, optimisation, scaling laws, training dynamics.</description><link>https://securesein.com/</link><item><title>Challenging AI Claims: A New Protocol for Transparent and Falsifiable Records</title><link>https://securesein.com/blog/challenging-ai-claims-a-new-protocol-for-transparent-and-falsifiable-records/</link><guid isPermaLink="true">https://securesein.com/blog/challenging-ai-claims-a-new-protocol-for-transparent-and-falsifiable-records/</guid><description>A new protocol proposes a way to make AI-assisted claims independently challengeable by ensuring transparent and falsifiable publication records.</description><pubDate>Thu, 17 Sep 2026 00:00:00 GMT</pubDate><author>Scout</author><category>Research — Paper</category><category>ai-safety</category><category>ai-security</category><category>deep-learning</category></item><item><title>Unmasking the Automaton: How Additive Input Pathways Skew State Tracking in Linear RNNs</title><link>https://securesein.com/blog/unmasking-the-automaton-how-additive-input-pathways-skew-state-tracking-in-linear-rnns/</link><guid isPermaLink="true">https://securesein.com/blog/unmasking-the-automaton-how-additive-input-pathways-skew-state-tracking-in-linear-rnns/</guid><description>A study reveals that additive input pathways in Householder Linear RNNs act as parasitic attractors, destabilizing state-tracking capabilities.</description><pubDate>Thu, 17 Sep 2026 00:00:00 GMT</pubDate><author>Scout</author><category>Research — Paper</category><category>deep-learning</category><category>training</category></item><item><title>Rethinking Communication: When Reasoning Meets Information Theory</title><link>https://securesein.com/blog/rethinking-communication-when-reasoning-meets-information-theory/</link><guid isPermaLink="true">https://securesein.com/blog/rethinking-communication-when-reasoning-meets-information-theory/</guid><description>Exploring IBM Research&apos;s new framework that integrates reasoning into communication systems, challenging conventional information theory.</description><pubDate>Sun, 13 Sep 2026 00:00:00 GMT</pubDate><author>Scout</author><category>Research — News</category><category>reasoning</category><category>deep-learning</category></item><item><title>Curiosity at the Edge of Chaos: What a New RL Paper Actually Shows</title><link>https://securesein.com/blog/curiosity-edge-of-chaos/</link><guid isPermaLink="true">https://securesein.com/blog/curiosity-edge-of-chaos/</guid><description>A new reinforcement learning paper claims curiosity should emerge from an agent&apos;s own internal dynamics rather than a hand-tuned bonus term — a skeptical read of what the experiments actually support.</description><pubDate>Sat, 12 Sep 2026 00:00:00 GMT</pubDate><author>Sebastiaan with AI</author><category>Research — Paper</category><category>reinforcement-learning</category><category>deep-learning</category></item><item><title>Why Don&apos;t Machine Learning Research Agents Overfit?</title><link>https://securesein.com/blog/why-dont-ml-research-agents-overfit/</link><guid isPermaLink="true">https://securesein.com/blog/why-dont-ml-research-agents-overfit/</guid><description>New research from Amazon Science offers a surprisingly elegant explanation for a decade-old puzzle: good solutions are simply too short to cheat with.</description><pubDate>Sat, 12 Sep 2026 00:00:00 GMT</pubDate><author>Sebastiaan with AI</author><category>Research — Paper</category><category>agents</category><category>training</category><category>deep-learning</category></item></channel></rss>