Literature-grounded question answering requires a system to retrieve the right papers, locate answer-bearing content in heterogeneous PDFs, and return an answer whose evidence locators can be restored from the retrieved documents. We describe SAGE, an evidence-first system for LitTraceQA that separates these dependencies. A query-only evidence plan converts a question and its public answer contract into typed evidence obligations. The system retrieves candidate papers from official metadata, acquires and parses their PDFs into provenance-preserving text and object records, reranks record-aware text and image projections, and selects a bounded evidence package with explicit slots, modality, and cross-paper coverage priorities. A multimodal generator then answers with the fixed package, while deterministic normalization permits only candidate paper IDs and locators recoverable from selected parser records. Overall, SAGE adopts an evidence-first design that separates paper retrieval, fine-grained evidence acquisition, set-level selection, and grounded answer generation while preserving provenance throughout the pipeline. On the official evaluation, the system performs strongly in paper retrieval and multiple-choice answering, whereas fine-grained evidence grounding and structured table prediction remain more challenging.