Neurosymbolics for Data Engineering: Achieving Long Context Token Reduction Without Finetuning

Neurosymbolics for Data Engineering introduces a neurosymbolic layer that can be added to existing LLM backbones to improve logical reasoning and reduce long‑context token usage. The layer boosts accuracy by an average of 85% on benchmarks such as BIRD‑CRITIC and LiveSQLBench without any task‑specific finetuning or RLHF. It also cuts effective token usage by over 50% and lowers time complexity from O(n²) to roughly O(n) for long‑context tasks.

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• llms
• reinforcement-learning
• fine-tuning
• benchmarks
• data-engineering