Evolve on Graph GrailQA - Hit@1: leaderboard

Evolve on Graph v1 evaluates knowledge-graph reasoning using direct answers, chain of thought, self-consistency, ReAct, retrieval, or separately trained Solver/Proposer systems. It uses Qwen2.5-3B/7B-Instruct and Llama3.1-8B-Instruct. Training lasts 140 steps, with 5 rollouts per task and at most 15 assistant turns. Hit@1 requires an exact normalized match to any gold answer; F1 measures overlap between predicted and gold answer lists. Freebase, Wikidata, and task-specific KG-R1 training scopes remain distinct. Trained weights are not aliased to the instruction backbone. Metric: Hit@1 (%).

Metric: Hit@1 (%). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 23 models tracked.

Top models

#ModelScoreOverall rank
1EoG study Qwen-2.5-7B-Instruct KG-R1 trainedGrailQA69.13
2EoG study Qwen-2.5-7B-Instruct KG-R1 trainedCWQ67.81
3EoG study Qwen-2.5-7B-Instruct EoG65.38
4EoG study LLaMA-3.1-8B-Instruct EoG63.87
5EoG study Qwen-2.5-7B-Instruct ToG62.95
6EoG study Qwen-2.5-7B-Instruct KG-R1 trainedWebQSP61.64
7EoG study Qwen-2.5-7B-Instruct CoR58.7
8EoG study Qwen-2.5-7B-Instruct PoG57.39
9EoG study Qwen-2.5-7B-Instruct Graph Reranker56.28
10EoG study Qwen-2.5-3B-Instruct EoG56.17

Interactive version: theaggregate.ai/benchmark?slug=evolve-on-graph-grailqa-hit-1 · How It Works · Data refreshed daily, snapshot 2026-10-28.