🤖🧪 𝘈𝘨𝘦𝘯𝘵 𝘓𝘢𝘣𝘰𝘳𝘢𝘵𝘰𝘳𝘺: 𝘜𝘴𝘪𝘯𝘨 𝘓𝘓𝘔 𝘈𝘨𝘦𝘯𝘵𝘴 𝘢𝘴 𝘙𝘦𝘴𝘦𝘢𝘳𝘤𝘩 𝘈𝘴𝘴𝘪𝘴𝘵𝘢𝘯𝘵𝘴 🧪🤖
#for_ai_scientists
#for_ai_researchers
#for_ai_architects
#did_you_know_that researchers at
@AMD, The Johns Hopkins University and ETH Zürich built an LLM-powered research co-pilot that generates a lit-review, writes & runs ML experiments, and spits out a ready-to-submit paper Git repo—for about $2.33 a pop (-84 % vs. prior Auto-Research systems)?
🧠✨ 𝙒𝙝𝙖𝙩’𝙨 𝙉𝙚𝙬?
➊ 3-Phase Pipeline. Literature Review → Experimentation (mle-solver) → Report Writing (paper-solver). Each stage staffed by PhD, Postdoc, ML-Engineer & Prof agents with tool calls and role-specific memories.
➋ mle-solver Tree-Search. EDIT & REPLACE operations explore code space; self-reflection reward model keep only top programs. Beat AIDE, OpenHands & MLAB on MLE-Bench—earning 2 Gold 1 Silver 1 Bronze medals.
➌ Co-Pilot Mode. Human feedback checkpoints raise overall reviewer score from 3.8 → 4.4 / 10 while users rate usability 4 / 5 and pledge to keep using the system.
➍ Backend Showdown. o1-preview writes the clearest papers; o1-mini nails SOTA code; gpt-4o wins on speed (≈ 19 min end-to-end).
🔍📊 𝙀𝙭𝙥𝙚𝙧𝙞𝙢𝙚𝙣𝙩 𝙎𝙥𝙤𝙩𝙡𝙞𝙜𝙝𝙩
• Cost Crunch: gpt-4o paper = $2.33; o1-preview = $13.10.
• Success Rate: 95 % of subtasks finish first try with o1-preview.
• Quality Bump: Co-pilot papers score 0.75 on “quality” & 0.48 on “soundness” vs. autonomous.
📈🚀 𝙒𝙝𝙮 𝙄𝙩 𝙈𝙖𝙩𝙩𝙚𝙧𝙨
1️⃣ Time back to humans. Agents handle data munging & latex wrangling so researchers stay creative.
2️⃣ Compute-flex friendly. Runs from CPU-only to GPU clusters with adaptive step caps.
3️⃣ Blueprint for domain labs. Swap mle-solver for chem-solver, robo-solver, etc.—pipeline stays.
Thanks to Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu and Emad Barsoum for their paper:
arxiv.org/abs/2501.04227
⭐ Star my repo:
github.com/mahmoudrabie/agen…
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