--- schema: 1 type: video title: 'How Autoresearch is changing ML research — Zhengyao Jiang, Weco' description: >- Earlier this year, OpenAI ran Parameter Golf, a model-training competition that doubled as a hiring filter. Over 1,000 researchers competed to train the best small language model under a 16MB cap. The top contributor was the one candidate OpenAI couldn't hire. Our autonomous research agent Aiden finished with 7 merged records, more than twice as many as any other contributor, and ended up the most-cited participant in the community. This talk is about what those 22 days showed. I'll cover on high level how does it works and which of its ideas produced the records. But the part worth more than the leaderboard is the collaboration itself, the community and AI agent building on each other's work, the largest natural experiment in human-AI collaboration I've seen run in public. I'll close with what it tells us about where humans and autonomous research each still matter for the foreseeable future. 1:57 PM # An AI Agent Became the #1 Contributor in OpenAI's Hiring Challenge **Location:** Main Stage **When:** Day 3 - July 1, 2026 · 1:55pm-2:15pm ## Speakers ### Zhengyao Jiang CEO & Cofounder · Weco AI [X/Twitter](https://x.com/zhengyaojiang) · [LinkedIn](https://www.linkedin.com/in/zhengyao-jiang-387b44145/) · [Website](https://zhengyaojiang.github.io/) Cofounder & CEO @WecoAI - automated hill climbing with LLMs. Previously: PhD in ML at UCL Timestamps 0:00 Introduction to Parameter Golf and the Aiden agent 1:06 Defining the challenge: Auto-research vs. human community 1:47 About Weco AI and the development of Aiden 3:07 Evaluating Aiden's impact and H-index in the community 4:01 Why autonomous AI is powerful: Throughput and efficiency 5:21 Human-AI collaboration: How ideas move the frontier 6:32 Case study: Combining research, architecture, and tokenization 7:41 Summary of auto-research strengths: Execution and search 9:06 The role of human design in competition 10:04 The Andrej Karpathy metaphor: Gradient descent and coding 11:19 Auto-research as training a model: Evals and abstractions 13:36 Case study: Improving data pipelines via strict API abstractions 14:38 Conclusion: The new craft of the AI engineer --- Watch along with the corrected transcript, summary, timestamps and resources on this talk's official AIE page: https://ai.engineer/talks/iCj_ATyThvc-how-autoresearch-is-changing-ml-research-zhengyao-jiang-weco-ai Join our upcoming AI Engineer conferences (see https://ai.engineer landing page): - AIEi Shanghai, November 5–6 — the first for meeting top Chinese labs and engineers - AIE CODE in SF, November 10–12 — the top AI Coding conference returns - AIEi Sydney, December 7–8 — the first AIEi run alongside NeurIPS 2026 in Sydney --- canonical: >- https://liquidgold.cc/channel/aidotengineer/explore/video/how-autoresearch-is-changing-ml-research-zhengyao-j-c4659071eaa2 markdown: >- https://liquidgold.cc/channel/aidotengineer/video/how-autoresearch-is-changing-ml-research-zhengyao-j-c4659071eaa2.md llms: null parent: 'https://liquidgold.cc/channel/aidotengineer.md' channel: aidotengineer updated: '2026-10-24T07:36:01.168Z' language: null children: [] videoId: iCj_ATyThvc durationSeconds: 976 publishedAt: '2026-07-16T18:08:16.000Z' searchChunks: - ordinal: 0 text: null startSeconds: 12 endSeconds: 23 watch: 'https://liquidgold.cc/watch/iCj_ATyThvc?t=12' heldReason: rights - ordinal: 1 text: null startSeconds: 23 endSeconds: 134 watch: 'https://liquidgold.cc/watch/iCj_ATyThvc?t=23' heldReason: rights - 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ordinal: 18 text: null startSeconds: 816 endSeconds: 828 watch: 'https://liquidgold.cc/watch/iCj_ATyThvc?t=816' heldReason: rights - ordinal: 19 text: null startSeconds: 828 endSeconds: 976 watch: 'https://liquidgold.cc/watch/iCj_ATyThvc?t=828' heldReason: rights - ordinal: 20 text: null startSeconds: 828 endSeconds: 976 watch: 'https://liquidgold.cc/watch/iCj_ATyThvc?t=828' heldReason: rights --- # How Autoresearch is changing ML research — Zhengyao Jiang, Weco Earlier this year, OpenAI ran Parameter Golf, a model-training competition that doubled as a hiring filter. Over 1,000 researchers competed to train the best small language model under a 16MB cap. The top contributor was the one candidate OpenAI couldn't hire. Our autonomous research agent Aiden finished with 7 merged records, more than twice as many as any other contributor, and ended up the most-cited participant in the community. This talk is about what those 22 days showed. I'll cover on high level how does it works and which of its ideas produced the records. But the part worth more than the leaderboard is the collaboration itself, the community and AI agent building on each other's work, the largest natural experiment in human-AI collaboration I've seen run in public. I'll close with what it tells us about where humans and autonomous research each still matter for the foreseeable future. 1:57 PM # An AI Agent Became the #1 Contributor in OpenAI's Hiring Challenge \*\*Location:\*\* Main Stage \*\*When:\*\* Day 3 - July 1, 2026 · 1:55pm-2:15pm ## Speakers ### Zhengyao Jiang CEO & Cofounder · Weco AI \[X/Twitter]\(https://x.com/zhengyaojiang) · \[LinkedIn]\(https://www.linkedin.com/in/zhengyao-jiang-387b44145/) · \[Website]\(https://zhengyaojiang.github.io/) Cofounder & CEO @WecoAI - automated hill climbing with LLMs. Previously: PhD in ML at UCL Timestamps 0:00 Introduction to Parameter Golf and the Aiden agent 1:06 Defining the challenge: Auto-research vs. human community 1:47 About Weco AI and the development of Aiden 3:07 Evaluating Aiden's impact and H-index in the community 4:01 Why autonomous AI is powerful: Throughput and efficiency 5:21 Human-AI collaboration: How ideas move the frontier 6:32 Case study: Combining research, architecture, and tokenization 7:41 Summary of auto-research strengths: Execution and search 9:06 The role of human design in competition 10:04 The Andrej Karpathy metaphor: Gradient descent and coding 11:19 Auto-research as training a model: Evals and abstractions 13:36 Case study: Improving data pipelines via strict API abstractions 14:38 Conclusion: The new craft of the AI engineer --- Watch along with the corrected transcript, summary, timestamps and resources on this talk's official AIE page: https://ai.engineer/talks/iCj_ATyThvc-how-autoresearch-is-changing-ml-research-zhengyao-jiang-weco-ai Join our upcoming AI Engineer conferences (see https://ai.engineer landing page): - AIEi Shanghai, November 5–6 — the first for meeting top Chinese labs and engineers - AIE CODE in SF, November 10–12 — the top AI Coding conference returns - AIEi Sydney, December 7–8 — the first AIEi run alongside NeurIPS 2026 in Sydney --- Canonical: https://liquidgold.cc/channel/aidotengineer/explore/video/how-autoresearch-is-changing-ml-research-zhengyao-j-c4659071eaa2 · Channel: [AI Engineer](https://liquidgold.cc/channel/aidotengineer.md) > Search chunk text is held until public-source provenance is verified. ## Document chunks > Missing: no stored document chunks are available.