AutoResearch at Production Scale: Failure Modes and a Multi-Agent Framework

arXiv:2609.30541 · cs.LG, cs.IR · Submitted 2026-09-24 · Read on arXiv

cs.LG, cs.IR

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/karpathy/autoresearch

Terminology

• Embeddings — 14× in this paper · explained in 2 episodes
• Failure Modes — 12× in this paper · explained in 3 episodes
• Scaffolding — 6× in this paper · explained in 3 episodes
• Core — 5× in this paper · explained in 2 episodes
• Memory Decay — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Agent Memory — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Failure Taxonomy — 2× in this paper · explained in 2 episodes
• Information Gain — 2× in this paper · explained in 2 episodes
• Multi-Agent Framework — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Hallucination — 1× in this paper · explained in 4 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Phase Transitions — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
• ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence
• AIDE: AI-Driven Exploration in the Space of Code
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
• MemGPT: Towards LLMs as Operating Systems
• Representation Learning with Contrastive Predictive Coding

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