Enterprise document security classification is a selective-decision problem. Rare high-risk labels require evidence and auditability, yet judging every document with an LLM is costly and adds model-specific variance to cases that precedents already settle. We introduce CARE (Cost-Aware Resource-adaptive Evidence), a bounded, fixed commit–verify–adjudicate controller that routes each case by precedent trust. It forms a zero-LLM evidence vote, commits when a learned reliability gate predicts low vote-error risk, verifies uncertain cases through verifier–evidence agreement, and spends heterogeneous panel judgment only on the residual conflicts where evidence and verification disagree. Across two contrasting evidence regimes that defeat different fixed baselines, the same controller selects route depth document by document. On WikiLeaks, where contrastive retrieval already supports strong performance without LLM calls, CARE preserves the zero-call evidence endpoint within bootstrap uncertainty while staying above every always-LLM decoder. It reaches 0.9824 Macro-F1 and 0.9624 SECRET F1, with only 44 of 2,863 documents reaching any LLM stage. This requires 55 invocations in total, 1.9% of a one-call-per-document budget. On DISC, where the rare TOP SECRET tail destabilizes sparse and LLM decoders, it gives the strongest row in our controlled comparison (0.8785 Macro-F1, 0.8444 TOP SECRET F1) using 496 physical model invocations against 789 for a same-evidence always-LLM policy, 63% of that budget, with only 81 documents reaching the panel. CARE thus turns security classification into an auditable policy for deciding when evidence is enough and when judgment is worth spending.

Pre-trained chemical language models (CLMs) excel in the field of molecular property prediction, utilizing string-based molecular descriptors such as SMILES for learning universal representations. However, such string-based descriptors implicitly contain limited structural information, which is closely associated with molecular property prediction. In this work, we introduce Moleco, a novel contrastive learning framework to enhance the understanding of molecular structures within CLMs. Based on the similarity of fingerprint vectors among different molecules, we train CLMs to distinguish structurally similar and dissimilar molecules in a contrastive manner. Experimental results demonstrate that Moleco significantly improves the molecular property prediction performance of CLMs, outperforming state-of-the-art models. Moreover, our in-depth analysis with diverse Moleco variants verifies that fingerprint vectors are highly effective features in improving CLMs’ understanding of the structural information of molecules.