I need to investigate data quality issues systematically. Let me examine the evidence I have and explore further. From the evidence so far, I can see: - Token fields are all empty strings (step-7, step-8) - Many fields have high null rates (step-2): Training compute (80%), Parameters (75%), Training cost (95%), Hardware quantity (96%) - Best scores range 0.0-1.0 with no out-of-range values (step-3) - No future dates detected (step-4) - No duplicate records (step-6) Let me investigate format consistency and potential outliers in key fields. ```json { "action": "tool_call", "reasoning": "Need to check for format inconsistencies in Model name field and examine the distribution of key numeric fields for outliers. The previous query failed due to syntax - I need to properly quote the column name.", "tool": "query_sql", "args": { "sql": "SELECT \"Model name\", COUNT(*) as count FROM raw.epoch_ai_benchmarks GROUP BY \"Model name\" HAVING COUNT(*) > 20 ORDER BY count DESC LIMIT 20", "purpose": "Check for model name patterns and frequency distribution" } } ```