Before revision:  We define an equation reconstruction task based on formulating derivation segments as basic units of non-trivial state sequences, with the goal of reconstructing an unknown intermediate state equivalent to one-hop inference, extensible to the multi-hop case. We present a symbolic similarity-based heuristic approach to solve an equation reconstruction task on the PhysAI-DS1 dataset, which employs a set of actions, a knowledge base of symbols and equations, and a computer algebra system, to reconstruct an unknown intermediate state within a sequence of three equational states, grouped together as a derivation unit.
After revision:  We define an equation reconstruction task based on formulating derivation segments as basic units of non-trivial state sequences, with the goal of reconstructing an unknown intermediate equation posed as a state, taken from three consecutive equational states within a derivation. Derivation automation is achieved by applying string-based CAS-reliant actions to states, which mimic mathematical operations and induce state transitions. We implement a symbolic similarity-based heuristic approach to solve an equation reconstruction task on the PhysAI-DS1 dataset, which employs a set of actions, a knowledge base of symbols and equations, and a computer algebra system, to reconstruct an unknown intermediate state within a sequence of three equational states, grouped together as a derivation unit.