Cognitive Loop via In-Situ Optimization: Self-Adaptive Reasoning for Science

The capacity for artificial intelligence (AI) to formulate, evolve, and test altered thought patterns
under dynamic conditions indicates advanced cognition that is crucial for scientific discovery. The
existing AI development landscape falls into two categories: 1) frameworks over non-reasoning models
that natively incorporate opinions on how humans think, and 2) reasoning models that abstract
precise control of the reasoning intuition away from end users. While powerful, for scientists to
maximize utility of AI in scientific discovery, they not only require accuracy and transparency in
reasoning, but also steerability. Hence, we introduce an alternative approach that enables deep and
precise control over the reasoning process called: a cognitive loop via in-situ optimization (CLIO).
CLIO enables large language models (LLMs) to self-formulate ways of approaching a problem, adapt
behavior when self-confidence is low, and ultimately provide scientists with a final belief or answer.
Through CLIO’s open design, scientists can observe uncertainty levels, understand how final belief
states are formulated using graph structures, and interject corrections. Without any further posttraining, OpenAI’s GPT-4.1 with CLIO yields an accuracy of 22.37% in text-based biology and
medicine questions on Humanity’s Last Exam (HLE). This yields a 13.82% net or 161.64% relative
increase when compared to the base GPT-4.1 model and surpasses OpenAI’s o3 performance in high
and low reasoning effort modes. We further discovered that oscillations within internal uncertainty
measures are key in determining the accuracy of CLIO’s results, revealing how its open design and
internal mechanisms can provide insight and control into scientific decision-making processes.