Code Compression Breakthrough: LLMs Excel Where Math Struggles

research #llm 🔬 Research|Analyzed: Feb 19, 2026 05:02•

Published: Feb 19, 2026 05:00

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1 min read

Analysis

This research reveals a fascinating 'perplexity paradox' where code prompts are remarkably robust to compression compared to math problems in Large Language Models (LLMs). The development of Task-Aware Adaptive Compression (TAAC) offers a promising approach, reducing costs while maintaining impressive quality for these powerful Generative AI models. It showcases advancements in prompt engineering for LLMs.

Key Takeaways

•Code prompts show resilience to compression, unlike Chain of Thought reasoning.
•A 'perplexity paradox' highlights preservation of code syntax vs. pruning of math values.
•Task-Aware Adaptive Compression (TAAC) reduces costs by 22% while maintaining quality.

Reference / Citation

View Original

"First, we validate across six code benchmarks (HumanEval, MBPP, HumanEval+, MultiPL-E) and four reasoning benchmarks (GSM8K, MATH, ARC-Challenge, MMLU-STEM), confirming the compression threshold generalizes across languages and difficulties."

ArXiv NLPFeb 19, 2026 05:00

* Cited for critical analysis under Article 32.

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