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LLMs show promise but struggle with formal proofs for program termination

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the capabilities of large language models (LLMs) in addressing the Halting Problem, a fundamental undecidable problem in computer science. The study evaluated models like GPT-5 and Claude Sonnet 4.5 on their ability to determine program termination, finding their performance comparable to specialized verification tools. However, the research highlights a significant gap: while LLMs can often recognize termination, they struggle to generate formal proofs, indicating a limitation in symbolic reasoning. AI

RANK_REASON The cluster contains an academic paper detailing research on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs show promise but struggle with formal proofs for program termination

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The cluster contains an academic paper detailing research on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Oren Sultan, Jordi Armengol-Estape, Pascal Kesseli, Julien Vanegue, Dafna Shahaf, Yossi Adi, Peter O'Hearn ·

    LLMs versus the Halting Problem: Characterizing Program Termination Reasoning

    arXiv:2601.18987v5 Announce Type: replace-cross Abstract: Determining whether a program terminates is a central problem in computer science. Turing's Halting Problem established termination as undecidable, showing that no algorithm can universally determine termination for all pr…

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