Emergent Steering Beyond Endpoint Alignment in Chemical Reaction Models

Machine learning models for chemical reaction prediction are usually evaluated by endpoint accuracy, but endpoint correctness alone does not reveal how a model represents chemical transformation or why it fails. We introduce $\textbf{UniRxnAxis}$, a unified forward--retrosynthesis framework that models both tasks as direction-conditioned endpoint redistribution in a shared latent space. By aligning source states, transformed states, and encoded target endpoints, UniRxnAxis enables directional auditing of learned latent updates. This shared latent geometry allows the learned displacement (from the encoder source state $(S_0)$ to the decoder-facing transformed state $(S_1)$) to be decomposed into an endpoint-parallel component and an endpoint-orthogonal residual, which we call $\textbf{emergent steering}$. Our evaluation shows that emergent steering is functionally important: in retrosynthesis, it recovers most of the model's utility, remains effective under random, shuffled, and prototype controls, and directly affects which bond edits are promoted or suppressed. The decomposition also supports a proxy taxonomy of model-side failures, showing how reaction models can be audited beyond endpoint accuracy.