Major Source Code Leak Unveils Hidden Features of Anthropic’s Claude Code - ForkLog
<a href="https://news.google.com/rss/articles/CBMinAFBVV95cUxPb3Znbi14VDRjUnBDY1Y0SmdQa3AteE9IdjdiMHZweFBlLXRFUWtXTHhabWJJUVNjMU1UQS05OExfd21UYkI0cnJZSDVsU21kbnVJY0VBMmt4VHRvcE5ON3JaczVUSXBPOXJvQW1CUENnRGQwZkNvOERHY1dFc0tKQjdLSDJlWFRqck9Celo2dF96aG5URUF3dDVCaVA?oc=5" target="_blank">Major Source Code Leak Unveils Hidden Features of Anthropic’s Claude Code</a> <font color="#6f6f6f">ForkLog</font>
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ChatGPT vs. Claude: 7 real-life benchmarks that crown the 2026 AI Madness Champion - Tom's Guide
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Polysemanticity or Polysemy? Lexical Identity Confounds Superposition Metrics
arXiv:2604.00443v1 Announce Type: new Abstract: If the same neuron activates for both "lender" and "riverside," standard metrics attribute the overlap to superposition--the neuron must be compressing two unrelated concepts. This work explores how much of the overlap is due a lexical confound: neurons fire for a shared word form (such as "bank") rather than for two compressed concepts. A 2x2 factorial decomposition reveals that the lexical-only condition (same word, different meaning) consistently exceeds the semantic-only condition (different word, same meaning) across models spanning 110M-70B parameters. The confound carries into sparse autoencoders (18-36% of features blend senses), sits in <=1% of activation dimensions, and hurts downstream tasks: filtering it out improves word sense di
Large Language Models in the Abuse Detection Pipeline
arXiv:2604.00323v1 Announce Type: new Abstract: Online abuse has grown increasingly complex, spanning toxic language, harassment, manipulation, and fraudulent behavior. Traditional machine-learning approaches dependent on static classifiers and labor-intensive labeling struggle to keep pace with evolving threat patterns and nuanced policy requirements. Large Language Models introduce new capabilities for contextual reasoning, policy interpretation, explanation generation, and cross-modal understanding, enabling them to support multiple stages of modern safety systems. This survey provides a lifecycle-oriented analysis of how LLMs are being integrated into the Abuse Detection Lifecycle (ADL), which we define across four stages: (I) Label \& Feature Generation, (II) Detection, (III) Review \
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