And how is this comment relevant here? The abstract lists the digestible model names, and you can find the details in the supplementary text:

> To evaluate user-facing production LLMs, we studied four proprietary models: OpenAI’s GPT-5 and GPT- 4o (80), Google’s Gemini-1.5-Flash (81) and Anthropic’s Claude Sonnet 3.7 (82); and seven open-weight models: Meta’s Llama-3-8B-Instruct, Llama-4-Scout-17B-16E, and Llama-3.3-70B-Instruct-Turbo (83, 84); Mistral AI’s Mistral-7B-Instruct-v0.3 (85) and Mistral-Small-24B-Instruct-2501 (86); DeepSeek-V3 (87); and Qwen2.5-7B-Instruct-Turbo (88).

edit: It looks like OP attached the wrong link to the paper!

The article is about this Stanford study: https://www.science.org/doi/10.1126/science.aec8352

But the link in OP's post points to (what seems to be) a completely unrelated study.

Generally, published papers don't give a damn about reproducibility. I've seen it identified as a crisis by many. Publishers, reviewers, and researchers mostly don't care about that level of basic rigor. There's no professional repercussions or embarrassment.

Agreed - if I was a reviewer for LLM papers it would be an instant rejection not listing the versions and prompts used.

I think it’s very important to be clear what studies like this are actually doing.

This study, although it has been produced by a computer science department, belongs more to the field of sociology or media studies than it does to computer science.

This is a study about the way in which human beings consume a particular media product - a consumer AI chatbot - not a study about the technological limitations or capabilities of LLMs.

The social impact of particular pieces of software is a legitimate field of study and I can see the argument that it belongs in the broadly defined field of computer science. But this sort of question is much more similar to ‘how does the adoption of spreadsheet software in finance impact the ease of committing fraud’ or ‘how does the use of presentation software to condense ideas down to bulletpoints impact organizational decision making’. Software has a social dimension and it needs to be examined.

But the question of which models were used is of much less relevance to such a study than that they used ‘whatever capability is currently offered to consumers who commonly use chat software’. Just like in a media studies investigation into how viewing cop dramas impacts jury verdicts the question is less ‘which cop dramas did they pick to study?’ So long as the ones they picked were representative of what typical viewers see.

It’s as if they are testing “AI” and not specific agents.

I wonder if that is left over from testing people. I have major version numbers and my minor version number changes daily, often as a surprise. Sometimes several times a day. So testing people is a bit tricky. But AIs do have stable version numbers and can be specifically compared.

Any paper like this would easily take a year or more to write and go through the submission/review/rebuttal/revision/acceptance process. I don't understand why the models being a year or two old now is worth noting as though it's a clear weakness? What should they do, publish sub-standard results more quickly?

How many people using AI are actually paying for it (outside of people in tech)?

I find the free models are much more psychophantic and have a higher tendency to hallucinate and just make shit up, and I wonder if these are the ones most people are using?

If they’re reaching the same results across a variety of the most popular public models, it doesn’t seem like that big a deal to know if it was Opus 4 or Opus 4.5