Apple research asks: how much harness does a strong agent need for autonomous ML engineering?
Apple ML Research · rss · 2026-10-01
Apple ML Research published a paper asking how much scaffolding a strong agent really needs for autonomous ML engineering (MLE).
- Recent MLE agents have climbed public leaderboards, often justified by progress stagnation over long-horizon cycles and limited LLM primitives, leading to increasingly elaborate harnesses: multi-agent orchestrators, dedicated retrieval subagents, and more.
- In contrast, primitive but improved coding agents—where the LLM directly accesses the execution environment via read, write, and bash primitives—have received little attention.
- The paper examines whether simpler harnesses can match elaborate machinery when the underlying agent is strong enough.
More from coding & agent
- Dev Has Claude Build a Custom Jump Animation Editor to Speed Up Game Feel Iteration — andrew_n_carr · 2026-10-02
- Translating an entire book with DeepSeek: pennies and under an hour, decent quality — teortaxesTex · 2026-10-02
- OmniSeek turns Omni-LLMs into agents that actively seek audio-visual evidence — Haibo Wang · 2026-10-02
- Microsoft's ActiveSaddler Uses Automated Curriculum Learning to Boost Agent Harnesses by 7.5 Points — microsoft · 2026-10-02
- Alibaba's PoS Maintains Explicit Belief States to Fix Long-Horizon Agent 'Belief Trapping' — alibabagroup · 2026-10-02
- IntentFlux Benchmarks 'Intent Drift' in LLM Agents: Scores Fall from 0.476 to 0.384 as Users Change Their Minds — Yanjie Zhang · 2026-10-02