Public articles linked to the same research event.
arXiv The work proposes Learner2Skill, which externalizes the simulation capability acquired from historical interactions into a persistent and reusable Simulation Skill that captures the learner's current learning state and recurring response patterns, evolves as new real interactions arrive, and can be adapted to a new LLM through lightweight executor calibration; experiments show it more faithfully reproduces fine-grained learner behavior while reducing overall token cost, and that the same constructed Skills can be effectively reused across different LLM executors.
The work proposes Learner2Skill, which externalizes the simulation capability acquired from historical interactions into a persistent and reusable Simulation Skill that captures the learner's current learning state and recurring response patterns, evolves as new real interactions arrive, and can be adapted to a new LLM through lightweight executor calibration; experiments show it more faithfully reproduces fine-grained learner behavior while reducing overall token cost, and that the same constructed Skills can be effectively reused across different LLM executors.
The work proposes Learner2Skill, which externalizes the simulation capability acquired from historical interactions into a persistent and reusable Simulation Skill that captures the learner's current learning state and recurring response patterns, evolves as new real interactions arrive, and can be adapted to a new LLM through lightweight executor calibration; experiments show it more faithfully reproduces fine-grained learner behavior while reducing overall token cost, and that the same constructed Skills can be effectively reused across different LLM executors.
The work proposes Learner2Skill, which externalizes the simulation capability acquired from historical interactions into a persistent and reusable Simulation Skill that captures the learner's current learning state and recurring response patterns, evolves as new real interactions arrive, and can be adapted to a new LLM through lightweight executor calibration; experiments show it more faithfully reproduces fine-grained learner behavior while reducing overall token cost, and that the same constructed Skills can be effectively reused across different LLM executors.