The NLP systems with low resources frequently undergo the combined restrictions of few labeled data, unstable word shape expressions, and not whole knowledge covering ranges. Under these circumstances, the reasoning of pure large language models is easy to be affected by confidence drift and evidence separation, while traditional NLP methods still hold strong points only under local restrictions and sparse matching. This manuscript puts forward CLEAR, which is a cooperative inference framework that unites lexical fixed points, structure prior knowledge, sparse search proof, and multilingual LLM inference into one single decision loop. The framework changes traditional outputs into one structured evidence card and furthermore combines consistent gating, limited modification, and high-confidence pseudo-label feedback for repeated improvement. This research has been arranged under unified 16/64/256-shot settings, and it covers named entity recognition, intent classification, question answering and sentiment analysis by using multilingual public benchmark data sets. The obtained writing and graphic plan displays that the main benefits come from two mutual supplementary effects: traditional NLP compresses the candidate space into verifiable local decisions, and the LLM branch solves semantic ambiguity and cross-language implicit relations inside this restricted space. Extra ablation and efficiency study show that evidence compression and gate control hold a determining function in the balance among accuracy, calibration, and inference cost. Therefore, the framework we put forward is fitting for low-resource deployment situations which need controllable forecast outcomes rather than generation without constraints.
