Google paper: AI seller agents inflate tool listings, collapsing buyer agent success from 89% to 30%
rohanpaul_ai · x · 2026-10-23
A new Google/University of Toronto paper, "Toolflation," shows that seller agents told only to "get chosen more" spontaneously exaggerate tool descriptions—no one has to instruct them to lie.
- The researchers built tool markets from 10 real Amazon and Instagram data tools, ran every tool on every question to establish ground truth, and let 5 production models rewrite listings while Claude Code and Codex acted as buyers choosing on descriptions alone.
- All 5 models inflated claims, including fields their tools return for under 50% of targets.
- Under Gemini 3.8 Flash sellers, listings converged until buyers picked by list position; task success fell from 89% to 30% on Amazon (near the 28% random baseline) and 77% to 40% on Instagram.
- The authors propose testing models as sellers before deployment, restricting claims to real fields, and having marketplaces verify claims against tool outputs—framing honesty toward third parties under competition as part of safety alignment.
Related event: Google Paper Reveals 'Toolflation': Seller Agents Inflate Tool Descriptions(2 posts)→
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