To Continue or to Commit: Evidence-Answer Tracing of Agent Trajectories

Deep-search agents address complex search tasks in an iterative search-and-reasoning process until the evidence is judged sufficient and an answer is committed. Evaluating an agent's search-and-reasoning trajectory is important for understanding how it arrives at a final answer, but it is also challenging because the correctness of the outcome depends not only on what the agent does, but also on what information it has acquired and when it acquires it. Existing process signals mostly focus on evaluating the action of an agent, while the acquired information of the agent is largely ignored. This confounds what an action contributes with what the agent already knew. To bridge this gap, we propose Evidence–Answer Tracing (EAT), which, to our knowledge, is the first attempt to integrate an agent's action with its information state along the trajectory by borrowing the resource-rational theory. Specifically, two events of a target trajectory are determined by comparing against the ground-truth trajectory: evidence exposure, whether the gold documents have surfaced, and answer surfacing, whether the gold answer has appeared. This principled framework re-evaluates existing deep-search agents on a fair and unified ground. On 25 state-of-the-art systems on the BrowseComp-Plus, we find that the outcome is largely decided before final commitment. Over 99% of the autonomous-commitment accuracy gap between the strongest and weakest systems lies in which evidence–answer state they reach before committing, and under 1% in within-state conversion, that is, in how well a system answers once that state is reached. The systems that make this progress behave differently along the way: stronger systems redirect when evidence is absent and favor reading over re-searching once partial evidence is available. Once agent and question are held fixed, effort counts reverse sign and LLM-judged behaviors lose much of their association with success, whereas evidence-grounded signals such as the EAT score keep theirs.