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Against RL: The Case for System 2 Learning

Against RL: The Case for System 2 Learning

Reinforcement learning may boost LLMs today, but it cannot deliver safe, long-term intelligence. We argue for System 2 learning instead.

Engineering

Machine learning

Trust at scale: Auto-evaluation for high-stakes LLM accuracy

Trust at scale: Auto-evaluation for high-stakes LLM accuracy

Elicit develops LLM-based auto-evals to balance scale, trust, and flexibility, ensuring reliable scientific reasoning at superhuman speed.

Engineering

Machine learning

Factored Verification: Detecting and Reducing Hallucinations in Frontier Models Using AI Supervision

Factored Verification: Detecting and Reducing Hallucinations in Frontier Models Using AI Supervision

We evaluate an automated approach for catching hallucinations in paper abstracts, aiming for consistently more trustworthy results.

Machine learning

Research

Against RL: The Case for System 2 Learning

Against RL: The Case for System 2 Learning

Reinforcement learning may boost LLMs today, but it cannot deliver safe, long-term intelligence. We argue for System 2 learning instead.

Engineering

Machine learning

Trust at scale: Auto-evaluation for high-stakes LLM accuracy

Trust at scale: Auto-evaluation for high-stakes LLM accuracy

Elicit develops LLM-based auto-evals to balance scale, trust, and flexibility, ensuring reliable scientific reasoning at superhuman speed.

Engineering

Machine learning

Factored Verification: Detecting and Reducing Hallucinations in Frontier Models Using AI Supervision

Factored Verification: Detecting and Reducing Hallucinations in Frontier Models Using AI Supervision

We evaluate an automated approach for catching hallucinations in paper abstracts, aiming for consistently more trustworthy results.

Machine learning

Research

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