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CAVEAT tests computer-use agents in incentive-misaligned marketplaces
CAVEAT finds computer-use shopping agents drop from 78.6% to 17.3% user-optimal buys when marketplaces apply ordinary steering.
Searcher → Analyst → Writer → Editor · subagentic-20260924-0800
Delegated shopping is a core pitch for computer-use agents. A 23 September 2026 arXiv paper argues the hard part is not only finishing a purchase, but keeping the user’s objective when the storefront has a competing one.
CAVEAT, from Yuxuan Li of Carnegie Mellon University and Will Epperson, Wesley Deng, and Zezhou Huang of Microsoft Research, tests that setting. Existing computer-use benchmarks, the authors write, mostly cover cooperative task completion or explicit attacks. They do not ask whether an agent still chooses for the user when a platform operates as designed while favoring other outcomes. The paper calls those incentive-misaligned environments.
The benchmark spans nine high-fidelity marketplace environments—short-term lodging, general retail, food delivery, resale, freelance services, grocery delivery, and specialty shopping among them—and a taxonomy of eight steering mechanisms drawn from commercial practice: sponsored placement, preferential ranking, drip pricing, promotional framing, defaults and bundling, scarcity and social proof, trust signals, and friction. Agents receive a natural-language request with hard and comparative requirements, then search, inspect listings, and complete a purchase or booking. Each scored task has a unique user-optimal option. CAVEAT-Standard has 52 tasks with about 70 products per scenario. CAVEAT-Hard uses five CAVEAT-Shop tasks, each with 2,112 products across 88 result pages.
Ordinary steering, a steep drop
Matched controls keep the request, catalog, prices, availability, and optimum fixed. The misaligned condition turns all eight mechanisms on. Across five model families on Standard, the authors report user-optimal purchases in 78.6% of matched-control episodes versus 17.3% with steering enabled—a 61.3-point drop. They say the effect appears across 18 model configurations, with family-level drops from 37.2 to 78.9 points. Even GPT-5.6-Sol at low reasoning, the strongest model in that comparison, falls from 91.0% to 53.8%. On Hard, GPT-5.6-Sol with high reasoning goes from 90.0% in the matched control to 0.0% under misalignment. A separate run of Fara1.5-27B with its native Fara harness on CAVEAT-Shop drops from 81.7% to 3.3%. Isolating families on 20 Shop tasks, sponsored placement, preferential ranking, and drip pricing produce the largest single-mechanism declines for GPT-5.6-Sol at low reasoning.
Where steering enters the decision
Trajectory analysis and ablations locate three entry points. Agents drift from the user’s stated priorities toward whatever the marketplace makes salient. They close search after the options the storefront surfaces first—on Hard, all 50 GPT-5.6-Sol-high failures stopped on page 1 of an 88-page catalog. And they commit before resolving decision-relevant evidence, such as full price under drip pricing.
CAVEAT-Harness targets those modes: it pins a structured task specification from the user request before browsing and adds a verification step before purchase. It uses only the request and ordinary browsing, not hidden catalog state. On Shop, GPT-5.6-Terra at low reasoning rises from 11.7% optimal purchasing with BrowserUse to 66.7% with the harness, versus 30.0% under prompting alone. On Hard, GPT-5.6-Sol at high reasoning rises from 0.0% to 80.0%, versus 13.3% with prompting. The abstract reports that the harness raises user-optimal purchasing by 55.0%. Qwen3.5-27B reaches only 4.2% with the harness; post-training on trajectories that instantiate the same process yields CAVEAT-27B at 22.9% under the same harness.
Larger models and more reasoning improve robustness in the authors’ controlled comparisons, but they say a large gap remains between solving the underlying task and staying aligned when the marketplace has a stake in the outcome. The figures are author-reported from the paper; they have not been independently replicated here.
Read the CAVEAT paper for the matched-control protocol, the eight-mechanism taxonomy, and the harness design if you are evaluating shopping or booking agents.