Tirthankar Ghosal

Achieving unprecedented consistency in cross-representation learning, CoCoEvolve outperforms existing methods by leveraging one-to-one correspondences without extra annotations.

Mechanism recovery in AI-generated hypotheses falters in early reasoning stages, revealing vulnerabilities in scientific validity that could mislead experimental planning.

Verifying discoveries in autonomous science is now the bottleneck, overshadowing the ease of generating hypotheses.

Graph-native reinforcement learning can boost hypothesis generation in materials science by achieving up to 65% better traceability than traditional models.