AMBER: Training Long-Horizon Web Agents through Append-Only Memory
Quick summary
arXiv:2610.07118v1 Announce Type: new Abstract: 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 and corrective feedback, and track progress across actions. Several approaches have been proposed to achieve this without the need for maintaining the entire execution history in context, such as using the reasoning and action histo
Key takeaways
- arXiv:2610.07118v1 Announce Type: new Abstract: 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 and corrective feedback, and track progress across actions.
- Several approaches have been proposed to achieve this without the need for maintaining the entire execution history in context, such as using the reasoning and action histo
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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