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The paper "No-Regret Language Generation" proposes the theoretical framework of "no-regret generation", establishing regret-bound analysis akin to online prediction for language generation tasks.
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10/19/2026
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UnverifiedThe proof agent employs an architecture in which a large language model interacts with a formal verifier: the LLM generates sequences of proof strategies, the Lean compiler verifies each reasoning step in real time, and upon failure adjusts based on error messages, forming a closed loop of generation–verification–correction.70% similarUnverifiedLarge language models have absorbed vast amounts of technical documentation and troubleshooting guides during training, forming a 'symptom–hypothesis–verification' reasoning pattern that can dynamically adjust diagnostic paths based on context.68% similarUnverifiedModern large language models generate code in an auto-regressive manner, predicting the next token each time by referencing only the existing context.67% similarVerifiedThe essence of large language models is learning the conditional probability distribution of what the next word is given the preceding text66% similarUnverified主流LLM的预训练采用下一个token预测作为目标，优化的是语言建模损失而非事实准确性66% similar
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