Semantic parsing (SP)-based knowledge base question answering aims to answer natural language questions by generating executable logical forms (LFs) over knowledge bases (KBs).
When applying Large Language Models (LLMs) to this task, a key challenge over large, heterogeneous KBs is selecting question-related schema elements (i.e., relations and classes) and composing them into complex LFs.
Recent LLM-based methods often make early discrete commitments to schema elements during intermediate reasoning, allowing incorrect intermediate schema decisions to propagate and finally result in incorrect LFs.
To overcome this limitation, we propose SALR, a schema-anchored latent reasoning method for LF construction.
It performs multi-step reasoning by generating continuous thoughts in the model's hidden states, thereby delaying the explicit commitment to LF decisions.
To ground this latent reasoning process in the corresponding KB schema, SALR aligns continuous thoughts with a codebook of KB schema elements through an alignment objective supervised by schema traces deterministically derived from gold LFs.
It then incorporates the aligned schema codes into inputs for subsequent reasoning steps.
This schema-mediated feedback guides LF generation without requiring the model to emit an explicit textual reasoning trajectory.
Experiments on GrailQA and WebQSP show that SALR achieves consistent overall gains over strong baselines.
Notably, on compositional questions from GrailQA, SALR outperforms TIARA, a strong SP-based baseline, by 2.86 F1 points.
Further analyses show that schema-mediated feedback affects LF generation and that schema information is recoverable from the latent states.