Frontis-MA1 Paper: Training an AI4AI Model for Recursive Self-Improvement
mark_k · x · 2026-08-02
This post shares the paper details for Frontis-MA1. The paper explains that Recursive Self-Improvement (RSI) requires systems that improve the process of building AI (AI4AI), with Machine Learning Engineering (MLE) serving as an executable testbed.
The researchers introduced OpenMLE, an open full-stack system covering task environments, operator learning, and long-horizon search. The trained 35B agent utilizes execution feedback and asynchronous search to hit 71.21% on MLE-Bench Lite, beating GPT-5.5 + Codex and approaching Kimi K3. On the held-out NatureBench Lite, swapping in this model raises Match-SOTA from 50% to 70%, proving its generalization and self-evolution capabilities.
Related event: OpenMLE: An Open-Source Stack for Recursive AI Self-Improvement(3 posts)→
More from coding & agent
- Reviewing AI-Generated Code: Judging Intent Beyond the Diff — IronCuk · 2026-08-04
- OpenAI Member Hints Codex Will Evolve Radically, Next-Gen Models Need More Than Laptops — soumitrashukla9 · 2026-08-04
- Strategies for Getting Your MCP Server Promoted by Anthropic — potozig · 2026-08-04
- Pydantic AI Harness v0.16.0 Released: Introduces Guardrails and Context Compaction — solyarisoftware · 2026-08-04
- Open-Source Watchtower: LLM-Orchestrated Penetration Testing with LangGraph — tom_doerr · 2026-08-04
- MulticaAI Demo: Building Multi-Model Collaborative Agent Teams for Coding — jiayuan_jy · 2026-08-04