New theory links LLM reasoning to De Bruijn graphs

Researchers propose that pattern recognition and step-by-step reasoning in large language models exist on a spectrum. They introduce the concept of a De Bruijn graph to model reasoning traces, suggesting that LLMs learn step-by-step reasoning when data is structured such that the next token depends on minimal preceding context. Empirical tests on Qwen2.5-1.5B-Instruct showed that a moderate density of states balances accuracy and robustness, while Qwen3 models retained significant accuracy even with restricted attention windows. AI

IMPACT Proposes a new theoretical framework for understanding and improving LLM reasoning capabilities, potentially leading to more sample-efficient training.

RANK_REASON The cluster contains a single arXiv paper detailing a new theoretical framework and empirical results for LLM reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• De Bruijn graph
• GPQA Diamond
• GSM8K
• Math-500
• Qwen2.5-1.5B-Instruct
• Qwen3-14B
• Qwen3-32B

• paper
• model release

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