Saessolsheim
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- 2026-05-11 research_milestone A new paper details a method using SAEs to predict AI agent tool failures. source
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AI agents' tool failures predicted; Spec Kit + Claude Code claims 90% code acceptance
A new paper introduces a method using Scale-Activation Effects (SAEs) to predict when AI agents might fail when using tools, offering internal observability. Separately, a tool called Spec Kit, combined with Anthropic's…
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Researchers develop SNMF for interpretable LLM feature analysis
Researchers have developed a new method for understanding the internal workings of large language models by decomposing MLP activations. This technique, semi-nonnegative matrix factorization (SNMF), identifies interpret…
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CorrSteer method enhances LLM steering using correlated sparse autoencoder features
Researchers have developed CorrSteer, a novel method for steering large language models (LLMs) during generation using features extracted from Sparse Autoencoders (SAEs). This technique correlates sample correctness wit…
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AI interprets protein models to detect biological risks
Researchers have developed a new method called SAEBER, utilizing Sparse Autoencoders (SAEs) to analyze protein design models like RFDiffusion3 and RoseTTAFold3. This technique identifies features within the models that …