Neurosymbolic AI: fundamental learning for context engineering in computational linguistics

Large language models (LLMs) make it possible to express symbolic structure directly in natural language, but most context engineering methods still assume that a human or an external pipeline already knows what contextual information the model will need. In this paper, we introduce fundamental learning , a two-stage context engineering framework in which the model first generates supportive lower-level analyses and then reuses them as auxiliary context to improve performance on a higher-level target task. We study this idea in computational linguistics, where intermediate analyses at the syntactic and semantic levels provide explicit context for downstream pragmatic interpretation. Using a unified instruction schema, we fine-tune Qwen3-8b with syntactic and semantic tasks and evaluate the approach on pragmatic tasks. Across experimental settings, requiring the model to explicitly infer these low-level intermediate labels before high-level task prediction consistently outperforms both a baseline model and a model trained on the same low-level tasks without being required to surface them at inference time. The results suggest that when context engineering cannot rely on ingenious human-designed prompts, retrieval pipelines, or hand-crafted reasoning procedures, fundamental learning offers a general alternative.