Designing Informative Actions: Action Construction for Long-Horizon Research Agents
Alkut Arxidin, Shaoning Zeng, Jianhang Zhou, Jianping Gou, Xinzhong Zhu
Abstract
Large language model agents support automated scientific research by connecting literature analysis and planning with experimentation. Evaluating which action an agent selects does not establish which experiment its instruction specifies, because omitted interventions or replication counts leave consequential choices to the executor. We introduce a reading-set framework for evaluating action construction under a common cost rule. Across 30 synthetic states, complete-design requests increase affordable efficiency by 465.98% and 327.91% relative to the action-name baseline in the two model families, respectively. An author-constructed research-action evaluation dataset provides 60 scenarios for complementary judgment evaluation. On this dataset, the scenario-level information-per-cost measure has a mean within-scenario correlation of 0.733 with judgments from 18 human participants. The framework makes design specification and execution ambiguity measurable, providing a reproducible basis for assessing how research agents translate scientific intent into informative, budget-feasible experiments.