Mechanova: An Autonomous Scientist for Discovering Falsifiable Mechanisms of Immune-Cold-to-Hot Conversion in Tumors

Autonomous scientific discovery requires agents to revise causal explanations in response to evidence and translate those revisions into new experiments. We introduce Mechanova, an autonomous scientist for discovering falsifiable mechanisms of immune-cold-to-hot conversion in tumors. Mechanova couples competing causal graphs to experimental evidence and counterevidence, selects discriminating experiments, revises mechanistic structure in response to prediction–observation discrepancies, and compiles surviving explanations into testable intervention programs. Its Mechanism Evidence Chain (MEC) grounds causal claims and licenses structural edits through context-matched experimental evidence. Biological and operational world models predict intervention effects and research-action outcomes, respectively. Provenance-aware memory and observation-conditioned hierarchical control sustain adaptive, long-horizon investigation, while offline policy learning improves execution across investigations. In a comparison on 125 paper-context tasks, the rule-assisted system achieves 60.8% mechanism identification and 52.0% pre-observation intervention agreement, exceeding the strongest completed comparator for each endpoint by 19.6 and 12.4 percentage points. A separate matched-prompt study on 88 source families isolates structural editing under identical downstream probability and action rules: enabling edits reduces native Brier by 25.3%, from 0.1119 to 0.0835 (paired difference −0.0284; 95% CI [−0.0439, −0.0146]). Graph-replacement controls further demonstrate that downstream outputs depend on the revised mechanism. Supporting studies raise evidence recall from 25.2% to 36.8% with zero measured unsupported assertions and useful execution completion from 50% to 100%. Mechanova establishes a mechanism-centered approach to autonomous scientific discovery in which evidence governs causal claims, structural revision improves prediction, and surviving explanations generate explicit experimental commitments.