arXiv:2510.06824v2 Announce Type: replace Abstract: To drive progress in science and engineering, large language models (LLMs) must be able to process large amounts of numerical data and solve long calculations efficiently. This is currently only possible through the use of external tools or extensive reasoning chains, either weakening the numerical representations of LLMs or limiting the length of problems they can solve. We show that frontier LLMs require excessive amounts of reasoning tokens to solve even basic calculations, which is exacerbated by their tokenization strategies that split s
Ongoing research into LLM limitations necessitates continuous efforts to improve their core capabilities, particularly in areas like numerical reasoning, which is a known weakness.
Improving LLMs' numerical efficiency directly addresses a key constraint in their application to scientific and engineering problems, expanding their utility and reducing operational costs.
This research suggests a more robust way for LLMs to handle numerical data, potentially moving away from reliance on external tools or inefficient internal reasoning chains for calculations.
- · AI research institutions
- · LLM developers
- · Scientific computing sector
- · Engineering firms using LLMs
- · Companies relying on third-party calculators for LLMs
- · LLMs without optimized numerical processing
LLMs become more capable of complex numerical tasks without external tools.
Reduced computational costs and increased efficiency for numerical applications of LLMs across various industries.
Accelerated discovery in science and engineering fields due to more powerful and autonomous AI analysis.
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Read at arXiv cs.LG