Have You Tried Turning It Off and On Again? A Transformer-Based Foundation Model That Achieves State-of-the-Art Helpdesk Performance by Suggesting Exactly That

Background. Large language models excel at many tasks, yet helpdesk resolution remains stubbornly hard. We hypothesized that most of the benchmark could be solved with a single, time-honored instruction.

Methods. We pre-trained REBOOT-1, a 70-billion-parameter transformer, on 14 years of support-ticket transcripts. At inference the model is constrained to two responses. Evaluation was conducted on the HELPME-bench suite (n = 40,000 tickets).

Results. REBOOT-1 resolved 61% of tickets by replying 'Have you tried turning it off and on again?' and a further 22% with 'It works on my machine.' Weights are released openly, behind a login that reliably times out. Latency was dominated by the model pausing for effect.

Conclusion. State-of-the-art performance is achievable without understanding the problem, consistent with the field's longstanding practice. Code and a 404 page are available.

Conflict of Interest. The authors have previously been on the receiving end of this advice and remain bitter.

Funding. Supported by an internal grant that was approved by replying-all to the wrong distribution list.

Ethics. No humans were helped during this study. IRB approval was pending and remains so.

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