An Information-Theoretic Approach to Agentic Scientific Exploration

Answering open scientific questions requires gathering evidence scattered across literature, databases, and search engines whose contents are too vast to model in advance. Retrieval-augmented generation selects actions by semantic similarity, treating each step independently and ignoring what the agent has already learned or still needs to resolve. We propose an information-theoretic framework that computes expected information gain over a set of competing hypotheses rather than over the unbounded space of possible observations, enabling principled action selection in open systems where observation-space information gain is intractable. The agent maintains an evolving memory graph, generates hypotheses, and selects actions by a multi-objective utility combining hypothesis-space information gain, novelty, coherence, and challenger-driven falsification pressure. Beliefs are updated by Bayesian scoring over accumulated evidence. On cancer-type prediction from gene coexpression networks, our agent is the only method to achieve balanced sensitivity and specificity and positive Matthews correlation, whereas strong agentic baselines (ReAct, Tree-of-Thoughts, MCTSr, and AlphaApollo) each collapse to a class bias, defaulting to affirmation or to rejection and achieving no better than chance-level discrimination. Ablations show that each utility term contributes to this balanced discrimination.