Unlocking self-adaptive cognitive behavior that is more controllable and explainable than reasoning models in challenging scientific domains. Long-running LLM agents equipped with strong reasoning, planning, and execution skills have the potential to transform scientific discovery with high-impact advancements, such as developing new materials or pharmaceuticals. As these agents become more autonomous, ensuring effective human oversight and clear accountability becomes increasingly important, presenting challenges that must be addressed to unlock their full transformative power. Today’s approaches to long-term reasoning are established during the post-training phase, prior to end-user deployment and typically by the model provider. As a result, the expected actions of these agents are pre-baked by the model developer, offering little to no control from the end user.
https://www.microsoft.com/en-us/research/blog/self-adaptive-reasoning-for-science/
