Learning Material Decisions from Heterogeneous Evidence

Materials decisions combine incomplete composition ranges, property tables, response curves, processing histories, and text. We propose a query-conditioned framework that retains each observation's value type, role, conditions, and provenance, learns to reconstruct whole withheld evidence views, and applies typed constraints to the completed responses. Existing corpus observations provide supervision without new manual labels for every task. The implemented interface composes trained tree-based, neural, and donor specialists for property completion, joint requirement selection, and response-to-region inference; a fully shared heterogeneous encoder remains an extension. The organizing principle is decision sufficiency: a unique material state is unnecessary when compatible states support the same action. The decision layer preserves joint constraints, nonzero composition or process ranges, and unresolved applicability, distinguishing source-supported conclusions from model-conditional recommendations without autoregressive text generation. Evaluations show that complementary evidence improves completion against strong context-only controls and that learned functional responses support non-zero operating windows. Useful coverage and requirement violations expose tradeoffs that regression error alone misses. Matched controls separate these gains from general parameter transfer and show that joint-decision training does not improve every seed. The result connects fragmented materials knowledge to evaluated decisions over ranges rather than isolated property predictions.