Rohan Paul@rohanpaul_ai
This paper builds a tough test for proactive agents and shows current models still fail often.
The test is called PROBE (Proactive Resolution of Bottlenecks) and it checks 3 steps, search, identify the real problem, execute the fix.
Each task gives a big pile of emails, docs, and calendar items with 1 hidden bottleneck.
A model must find the right evidence, name the exact blocker and task, then pick 1 action with full parameters.
The dataset has 1000 cases with long, noisy context so shortcuts break.
Top models reach about 40% on full end to end success.
Agent frameworks that chain tools do worse because retrieval misses key evidence.
Most errors come from wrong root cause, wrong person, missed deadlines, or missing action parameters.
The main weakness is turning messy evidence into a precise, complete action.
So proactive help is far from solved in real work like settings.
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Paper – arxiv. org/abs/2510.19771
Paper Title: "Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents"
