# Learned Guidelines

IBM Research's ALTK-Evolve appends behavioral guidelines distilled from an agent's own trial-and-error trajectories to its system prompt, leaving model weights untouched. On the τ²-bench retail domain, an agent using the older non-reasoning model gpt-4.1 raises Pass⁴ from 57.5% to 67.5%, above every retail entry in the October 12, 2026 leaderboard snapshot and above RAFT-30B-A3B's 61.4% from fine-tuning on synthetic data. The same model extracts and applies the guidelines, with no human annotation.

IBM Research's ALTK-Evolve appends behavioral guidelines distilled from an agent's own trial-and-error trajectories to its system prompt, leaving model weights untouched. On the τ²-bench retail domain, an agent using the older non-reasoning model gpt-4.1 raises Pass⁴ from 57.5% to 67.5%, above every retail entry in the October 12, 2026 leaderboard snapshot and above RAFT-30B-A3B's 61.4% from fine-tuning on synthetic data. The same model extracts and applies the guidelines, with no human annotation.