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METR · March 5, 2025

DeepSeek-R1 evaluation highlights tool-interface failures and elicitation limits

analysis Model EvaluationAI Engineering
Why it matters

METR’s early-2025 DeepSeek-R1 assessment uses autonomous software tasks and research-engineering environments through a third-party model provider. Transcript inspection found hallucinated tool results, malformed calls and difficulty recovering from mistakes, showing how interface behavior can limit measured performance. The report also acknowledges modest elicitation, incomplete checks for strategic underperformance and potentially biased exclusion of server-error runs. Published example transcripts were selected to illustrate behavior, not estimate its prevalence. These results describe a historical model-and-scaffold combination rather than an upper bound on open-weight model capability.

My takeaway: Separate infrastructure failures, tool-format errors and reasoning failures in evaluation logs. Disclose excluded runs, invest in scaffold tuning on a development set and reserve unseen tasks for the final capability assessment.
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