# AIBuildAI-2.5 ranks first on MLE-Bench with 73.3% medal rate

Digest AI · Research · published 2026-09-23T04:00:00Z

Canonical: https://digestai.news/story/aibuildai-2-5-ranks-first-on-mle-bench-with-73-3-medal-rate

## Summary

AIBuildAI-2.5 is an autonomous agentic system designed to build AI models by treating model construction as a tree‑search problem. The authors identify three efficiency gaps in prior agents—limited candidate execution, lack of resource‑aware scheduling, and reliance on a single powerful LLM for all tasks—and propose solutions.

The new system adds an LLM‑guided tree search where a judge scores each candidate on expected improvement, grounding, and feasibility, and a selector ranks the pool using those scores and the current search state. A scheduler launches training jobs based on real‑time hardware availability, while a router assigns cheaper LLMs to low‑complexity steps, reserving the strongest model for the hardest sub‑tasks. In benchmark evaluation, AIBuildAI-2.5 achieved a 73.3% medal rate, placing first on MLE‑Bench, and outperformed a strong baseline across six autonomous AI research tasks from AIRS‑Bench.

## Key points

- AIBuildAI-2.5 introduces LLM‑guided tree search that scores candidates on expected improvement, grounding, and feasibility
- The system adds a resource‑aware scheduler and a router that delegates low‑cost LLMs to simple tasks
- It achieved a 73.3% medal rate, ranking first on MLE‑Bench and beating a strong baseline on six AIRS‑Bench tasks

## Why it matters

Improving the efficiency of autonomous AI research agents can lower compute costs and broaden access to AI model development for scientific and engineering teams.

## Sources

1. [AIBuildAI-2.5: Efficient Autonomous AI Model Development Through LLM-Guided Tree Search](https://arxiv.org/abs/2609.25047) (arXiv cs.CL, 2026-09-23, primary source)

## Cite

Digest AI, "AIBuildAI-2.5 ranks first on MLE-Bench with 73.3% medal rate", 23 September 2026, https://digestai.news/story/aibuildai-2-5-ranks-first-on-mle-bench-with-73-3-medal-rate

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