Meituan open-sources LoHoSearch, a search-agent benchmark
10/03/2026 — 10/03, 11:12·1 sources·1 reports
Story overview
On October 3, 2026, Meituan's technical team announced LoHoSearch, an evaluation benchmark for search agents, and released it as open source. The team frames it as a next-generation benchmark for search agent evaluation that uses a knowledge graph to calibrate how AI capability is understood.
In the same announcement, the team looked back at how Search Agent capability has evolved over the past year. On benchmarks such as BrowseComp, top models climbed from accuracy in the 30% range to above 90%. From that, the team concludes that once a benchmark saturates this quickly, its value for distinguishing between models declines — the context it offers for building LoHoSearch.
That is where the public information currently stops. The benchmark is open source, but the available material does not describe its structure, how many tasks it contains, or which dimensions it measures, and it lists no model scores on the new benchmark. The knowledge-graph element appears only in that single phrase, with no further explanation of how it is used.
AI-generated from 1 reports · updated 2 hours ago
Latest turnMeituan has open-sourced LoHoSearch, a next-generation benchmark for search agents that uses a knowledge graph to calibrate how AI capability is measured. The team notes that on benchmarks such as BrowseComp, top model accuracy climbed from the 30% range to over 90% in the past year, eroding their value for telling models apart.
Reports on this story headlines open the original
Meituan has open-sourced LoHoSearch, a next-generation benchmark for search agents that uses a knowledge graph to calibrate how AI capability is measured. The team notes that on benchmarks such as BrowseComp, top model accuracy climbed from the 30% range to over 90% in the past year, eroding their value for telling models apart.
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