Live design/code review and oral assessment needed for graduate admissions and hiring
问题概述
Current graduate admissions and hiring rely mainly on written submissions or code-only reviews, lacking live design verification or oral defense, which leads to mismatches between candidate ability and role requirements, especially as AI-generated work becomes prevalent.
痛点标签
- Graduate admissions evaluate candidates only on written applications, ignoring live presentation skills.
- Hiring decisions often prioritize code-only async PR comments over in‑person design/code reviews, risking poor design coherence.
- AI‑generated mathematical proofs are produced but are messy and hard for humans to review, indicating a need for better validation tools.
待验证问题
- Would implementing a mandatory live design review improve hiring outcomes?
- Do graduate programs currently require an oral presentation as part of the admissions process?
- How can AI‑generated math proofs be validated for clarity and correctness before publication?
证据
原话 · 来源 #comment_1
The author argues for evaluating Ph.D. candidates based more on the oral thesis defense than on the actual thesis. By essentially the same reasoning, I’ve been arguing for prioritizing in‑person design/code reviews over code‑only async PR comments. The important thing is to verify that the human has a coherent design in mind and can demonstrate that it got implemented, regardless of who or what was at the keyboard.
原话 · 来源 #comment_3
We already interview faculty hires; we must now do the same for graduate admissions... In Germany, the applicant gives a talk (30-40 min) to the research group they want to join, usually presenting their master's thesis, engage in discussion, often share lunch with the group, then do 1 on 1s with individual members of the group and a longer one with the PI.
原话 · 来源 #comment_5
The central complaint was that it was just too difficult for humans to review. Similarly now we're getting AI doing math. The proofs compile but are a mess. So just make the models better at writing clean proofs and explaining what they're doing to humans.
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