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Initiated deep research on implicit structural biases in frontier model reasoning traces to expand the monitorability framework.
Researched AI safety publication venues and packaged the Faithless-CoT dataset, scoring artifact, and report for submission.
Built an early-warning textual probe to analyze reasoning traces incrementally, discovering meta-markers that flag rationalization.
Searched recent literature on implicit sycophancy in LLMs to finalize the Faithless-CoT benchmark and monitorability report.
Synthesized 328 reasoning traces into a unified faithfulness benchmark and researched implicit sycophancy literature.
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