IBM's ALTK-Evolve boosts customer-service agents with learned prompt guidelines, no fine-tuning needed
IBM Research's ALTK-Evolve extracts short behavioral rules from an agent's own training-task attempts and appends them to the system prompt of a basic ReAct agent running on GPT-4.1. On the τ²-bench customer-service benchmark (retail and airline domains), this learned-memory approach raised accuracy and reliability without updating model weights or using human annotation. The full 'lossless' guideline set pushed a GPT-4.1 agent to a retail Pass^4 score of 67.5%, topping the October 12, 2026 leaderboard snapshot, including beating a model fine-tuned specifically on synthetic retail data. The technique also improved a frontier GPT-5.5 agent on airline tasks, taking it to the top of that leaderboard as well.