# Agent Error Dataset

The work introduces the Agent Error Dataset (AED), comprising 50,228 error–diagnosis pairs from 9,961 source tasks across 33 environments, 19 harness families and 23 policy models, and a five-stage Agentic Error-to-Training (AET) pipeline that links natural failures to diagnoses, proposed corrections and optional replay evidence; across 3,062 matched replay pairs, first-proposal corrections raise verifier pass rates from 18.4% to 51.1% (a 32.7 percentage-point gain), full-diagnosis fine-tuning on a separately frozen diagnosis release raises Qwen3-8B's exact-step agreement with internal teacher labels from 47.2% to 63.6% (averaged over three seeds on a 943-case holdout), and in a single-seed actor-training comparison action-only repair training scores 6.

The work introduces the Agent Error Dataset (AED), comprising 50,228 error–diagnosis pairs from 9,961 source tasks across 33 environments, 19 harness families and 23 policy models, and a five-stage Agentic Error-to-Training (AET) pipeline that links natural failures to diagnoses, proposed corrections and optional replay evidence; across 3,062 matched replay pairs, first-proposal corrections raise verifier pass rates from 18.4% to 51.1% (a 32.7 percentage-point gain), full-diagnosis fine-tuning on a separately frozen diagnosis release raises Qwen3-8B's exact-step agreement with internal teacher labels from 47.2% to 63.6% (averaged over three seeds on a 943-case holdout), and in a single-seed actor-training comparison action-only repair training scores 6.