LoHoSearch turns a 7.62M-entity knowledge graph into a harder benchmark for search agents

美团技术团队 · wechat · 2026-07-23

Meituan's LoHoSearch uses a 7.62M-entity knowledge graph to generate harder search tasks

Meituan's LongCat team released LoHoSearch, a new benchmark for search agents that automates question generation from a large Wikipedia knowledge graph instead of relying on human-designed prompts.

Built on a graph covering 7.62 million entities and 265 million directed edges.
Contains 544 manually verified questions across 11 domains.
Difficulty is controlled along two axes: search space size and structural complexity.
The pipeline includes graph construction, subgraph sampling, question generation, automatic verification, and manual review.

Performance is much lower than on BrowseComp:

The best model, GPT-5.5, scores only 34.74%.
DeepSeek-V4-Pro, Claude-Opus-4.6, and Kimi-K2.6 cluster around 15.53%–15.99%.
Other models are below 14%.

Additional findings:

Solving a LoHoSearch task requires more tool calls on average: 61 vs. 35 on BrowseComp.
Sampling helps, but even pass@16 tops out at 38.3%.
Common context-management tricks improve only 6.8 points, far less than on BrowseComp.

The team argues LoHoSearch is a better stress test for long-horizon search and context management, and the benchmark is open sourced.

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LoHoSearch turns a 7.62M-entity knowledge graph into a harder benchmark for search agents

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