analiza aiqbench
The paper introduces SlowLab, a benchmark that tests whether language-model agents can run a year-long, irreversible experimental campaign in simulated greenhouses. In a preregistered study, three low-cost agents ended near a standard grower setting and below a Bayesian-optimisation baseline, and in-season measurements did not improve any method's results. For readers, the useful signal is that current agents struggle with slow, costly, hidden-information experiments, and that the bottleneck appears to lie in their initial hypotheses and experiment choices rather than in how they analyse evidence.
SlowLab: Benchmarking Autonomous AI Agents on a Year of Experiments They Cannot Undo
arXiv:2610.34644v1 Announce Type: new Abstract: AI agents are increasingly proposed as autonomous experimenters, yet the benchmarks that test them mostly offer cheap experiments that answer at once. Experiments in physical facilities are different: they take months, occupy scarce equipment and cannot be undone. We present SlowLab, a benchmark in which an agent runs a one-year campaign in four simulated greenhouse compartments on a site whose crop, facility and prices are hidden. The agent decides when to plant which climate policy, whether to stop a crop and which measurements to read while crops grow, and at the end recommends one policy, which a private evaluator scores in weather the agent never saw. In a preregistered study on 48 unseen sites, three low-cost language-model agents ended near a standard grower setting and clearly below a Bayesian-optimisation baseline that starts from that setting. They ran the facility as two synchronous batches, so measurements taken during the season never changed what they planted, and these measurements improved no method's result. Letting other methods re-read the evidence a campaign had collected did not yield significantly better recommendations, which points to where the models start and which experiments they try. Code is released under the MIT license.
Abstract:AI agents are increasingly proposed as autonomous experimenters, yet the benchmarks that test them mostly offer cheap experiments that answer at once. Experiments in physical facilities are different: they take months, occupy scarce equipment and cannot be undone. We present SlowLab, a benchmark in which an agent runs a one-year campaign in four simulated greenhouse compartments on a site whose crop, facility and prices are hidden. The agent decides when to plant which climate policy, whether to stop a crop and which measurements to read while crops grow, and at the end recommends one policy, which a private evaluator scores in weather the agent never saw. In a preregistered study on 48 unseen sites, three low-cost language-model agents ended near a standard grower setting and clearly below a Bayesian-optimisation baseline that starts from that setting. They ran the facility as two synchronous batches, so measurements taken during the season never changed what they planted, and these measurements improved no method's result. Letting other methods re-read the evidence a campaign had collected did not yield significantly better recommendations, which points to where the models start and which experiments they try. Code is released under the MIT license.
16 pages (9 main text), 7 figures, 5 tables. Code: this https URL
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
arXiv:2610.34644 [cs.AI]
(or arXiv:2610.34644v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.34644 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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