PrimeScientist: Strategic Allocation of Research Effort in Autonomous Research

arXiv:2609.17846 · cs.CL, cs.AI, cs.LG · Submitted 2026-09-15 · Read on arXiv

cs.CL, cs.AI, cs.LG

Submitted: 2026-09-15

Updated: 2026-09-15

Comments: 30 pages, 5 figures, 16 tables. Code and data: https://github.com/Henri-XYu02/PrimeScientist

Code: https://github.com/Henri-XYu02/PrimeScientist

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 16× in this paper · explained in 1 episode
• Benchmark — 15× in this paper · explained in 2 episodes
• Calibration — 13× in this paper · explained in 7 episodes
• Consistency — 13× in this paper · explained in 3 episodes
• alpha — 11× in this paper · explained in 1 episode
• Steering — 10× in this paper · explained in 2 episodes
• Sample Efficiency — 7× in this paper · explained in 4 episodes
• Hallucination — 6× in this paper · explained in 3 episodes
• In International Conference on Learning Representations — 6× in this paper
• Pruning — 6× in this paper · explained in 4 episodes
• AI Agents — 4× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Selection Bias — 3× in this paper · explained in 4 episodes
• AUC — 2× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adaptivity — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Activation Patching — 1× in this paper · explained in 2 episodes
• Activation Steering — 1× in this paper · explained in 4 episodes
• Agentic AI — 1× in this paper · explained in 1 episode
• Agentic Workflows — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Latent Visual Reasoning — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Self-Evolving Agents — 1× in this paper · explained in 2 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• MARS: Modular Agent with Reflective Search for Automated AI Research
• Reasoning Models Don't Always Say What They Think
• SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning
• Rational Metareasoning for Large Language Models
• Token-Budget-Aware LLM Reasoning
• AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs
• FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration
• Pancake: Hierarchical Memory System for Multi-Agent LLM Serving
• TritonDFT: Automating DFT with a Multi-Agent Framework
• GEAR: Genetic AutoResearch for Agentic Code Evolution
• AIDE: AI-Driven Exploration in the Space of Code
• GPT is becoming a Turing machine: Here are some ways to program it
• Spend Less, Reason Better: Budget-Aware Value Tree Search for LLM Agents
• Budget-Aware Tool-Use Enables Effective Agent Scaling
• The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
• Bilevel Autoresearch: Meta-Autoresearching Itself
• Learning Modal-Mixed Chain-of-Thought Reasoning with Latent Embeddings
• Effort Allocation for Deadline-Aware Task and Motion Planning: A Metareasoning Approach
• AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench
• DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling

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