The arXiv abstract introduces CB-Agent, a method for concept-based in-context learning aimed at sequential action selection. It builds on standard in-context learning (ICL), which allows large language models to incorporate demonstrations without modifying their parameters.
For interactive agents, the abstract notes, demonstrations take the form of trajectories that include reasoning, actions, and observations. The text is cut off mid-sentence, so the full description of how these trajectories are used is not available in the abstract.
Based on the provided portion, the work focuses on applying ICL to the specific challenge of choosing actions in sequence, with an emphasis on concepts. No experimental results or comparisons are included in the abstract excerpt, so the effectiveness of the approach is not yet reported.