Preprint Open access
AMBER: Training Long-Horizon Web Agents through Append-Only Memory
Modern language-model agents increasingly interact with external environments over long-horizon, multi-step trajectories, where the accumulated interaction history can quickly exceed practical context budgets. To ensure reliability, agents must maintain factual information over long horizons, remember execution errors …