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@@ -42,7 +42,7 @@ A simple context interference evaluation.
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  ## Contact & Collaboration
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- I welcome discussions and collaborations with research labs and model teams on **mechanistic interpretability—especially how LLMs represent, update, and lose informationrigorous model and agent evaluation, and why models fail, not only whether they fail**. If you are interested in PI-LLM, benchmark comparisons, or evaluating your models or agents, please reach out.
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  **Email:** [wangchupei@hotmail.com](mailto:wangchupei@hotmail.com)
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@@ -52,7 +52,6 @@ I usually reply within 36 hours.
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  ## TL;DR
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  We identify a task that is **super easy for humans** but where all LLMs—from early 0.1B to the most modern 600B+ (GPT-5, Grok-4, Gemini, DeepSeek, etc.)—consistently **fail in the Same Way**. This pinpoints the **core challenge of MRCR**
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  ## Contact & Collaboration
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+ I welcome discussions and collaborations with research labs and model teams on **mechanistic interpretability—especially how LLMs represent icl— model and agent evaluation, and why models fail, not only whether they fail**. If you are interested in PI-LLM, benchmark comparisons, or evaluating your models or agents, please reach out.
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  **Email:** [wangchupei@hotmail.com](mailto:wangchupei@hotmail.com)
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  ## TL;DR
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  We identify a task that is **super easy for humans** but where all LLMs—from early 0.1B to the most modern 600B+ (GPT-5, Grok-4, Gemini, DeepSeek, etc.)—consistently **fail in the Same Way**. This pinpoints the **core challenge of MRCR**
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