>To study how agent success on benchmark tasks relates to real-world usefulness, we had 4 active maintainers from 3 SWE-bench Verified repositories review 296 AI-generated pull requests (PRs). We had maintainers (hypothetically) accept or request changes for patches as well as provide the core reason they were requesting changes: core functionality failure, patch breaks other code or code quality issues.

I would also advise taking a look at the rejection reasons for the PRs. For example, Figure 5 shows two rejections for "code quality" because of (and I quote) "looks like a useless AI slop comment." This is something models still do, but that is also very easily fixable. I think in that case the issue is that the level of comment wanted hasn't been properly formalized in the repo and the model hasn't been able to deduce it from the context it had.

As for the article, I think mixing all models together doesn't make sense. For example, maybe a slope describe the increasing Claude Sonnet better than a step function.

"just don't do X" has basically never worked, it is not a serious solution to any problem.

Most of the students are here because they want to be in the companies, not for the joy of learning.

>This semester, Newton has removed the option to bring iPads to class, except for accessibility needs, as a part of the general movement in the “Reading and Writing the Modern Essay” seminars to “swim against the tide of AI use,” reduce “the infiltration of tech,” and “go back to pen and paper,” she said.

Is this about teaching efficiency or managing the teacher's feelings? If "the infiltration of tech" allowed for better learning, would this teacher even be open to it?

I don't think that's true? From what I understand most labs are making money from subscription users (maybe not if you include training costs, but still, they're not selling at a loss).

>(thankfully market dynamics and OSS alternatives will probably stop this but it's not a guarantee, you need like at least six viable firms before you usually see competitive behavior)

OpenAI is very aggressive with the volume of usage you can get from Codex, Google/DeepMind with Gemini. Anthropic reduced the token price with the latest Opus release (4.5).

I get the sentiment, but then, do you count all the other experiments that were done by that company before specifically trying to train this model? All the experiments done by people in that company at other companies? Since they rely on that experience to train models.

You could say "count everything that has been done since the last model release", but then for the same amount of effort/GPU, if you release 3 models does that divide each model cost by 3?

Genuinely curious in how you think about this, I think saying "the model cost is the final training run" is fine as it seems standard ever since DeepSeek V3, but I'd be interested if you have alternatives. Possibly "actually don't even talk about model cost as it will always be misleading and you can never really spend the same amount of money to get the same model"?

I think in that specific case that says more about LMArena than about the newer models. Remember that GPT 4o was so specifically loved by people that when GPT 5 replaced there was lots of backlash against OpenAI.

One of the popular benchmarks right now is METR which shows some real improvement with newer models, like Opus 4.5. Another way of getting data is anecdotes, lots of people are really impressed with Opus 4.5 and Codex 5.2 (but they're hard distangle from people getting better with those tools, the scaffolding (Claude code, Codex) getting better, and lots of other stuff). SWEBench is still not saturated (less than 75% I think).

What kind of absurd conclusions? And what kind of non absurd conclusions can you make when you follow your let's call it "mechanistic" view?

>It's an algorithm and a completely mechanical process which you can quite literally copy time and time again. Unless of course you think 'physical' computers have magical powers that a pen and paper Turing machine doesn't?

I don't, just like I don't think a human or animal brain has any magical power that imbues it with "intelligence" and "reasoning".

>A cursory read of basic philosophy would help elucidate why casually saying LLM's think, reason etc is not good enough.

I'm not saying they do or they don't, I'm saying that from what I've seen having a strong opinion about whether they think or they don't seem to lead people to weird places.

>What is thinking? What is intelligence? What is consciousness? These questions are difficult to answer. There is NO clear definition.

You see pretty certain that whatever those three things are a LLM isn't doing it, a paper and pencil aren't doing it even when manipulated by a human, the system of a human manipulating a paper and pencil isn't doing it.

Many people are throwing around that they don't "think", that they aren't "conscious", that they don't "reason", but I don't see those people sharing interesting heuristics to use LLMs well. The "they don't reason" people tend to, in my opinion/experience, underestimate them by a lot, often claiming that they will never be able to do .

To be fair, the "they reason/are conscious" people tend to, in my opinion/experience, overestimate how much a LLM being able to "act" a certain way in a certain situation says about the LLM/LLMs as a whole ("act" is not a perfect word here, another way of looking at it is that they visit only the coast of a country and conclude that the whole country must be sailors and have a sailing culture).

You could say the same about what people find on the web, yet LLMs are penalized more than web search.

>If you do not use LLMs and just misunderstood something, you will have an (flawed) justification for why you wrote this. If there's something flawed in an LLM, the likelihood that you do not have any justification except for "the LLM said so" is quite high and should thus be penalized higher.

Swap "LLMs" for "websites" and you could say the exact same thing.

The author has this in their conclusions:

>One clear conclusion is that the vast majority of students do not trust chatbots. If they are explicitly made accountable for what a chatbot says, they immediately choose not to use it at all.

This is not true. What is true is that if the students are more accountable for their use of LLMs than their use of websites, they prefer using websites. What is "more" here? We have no idea, the author didn't say so. It could be that an error from a website or your own mind is -1 point and from a LLM is -2, so LLMs have to make two times less mistakes than websites and your mind. It could be -1 and -1.25. It could be -1 and -10.

The author even says themselves:

>In retrospect, my instructions were probably too harsh and discouraged some students from using chatbots.

But they don't note the bias they introduced against LLMs with their notation.

>I thought this was fair. You can use chatbots, but you will be held accountable for it.

So you're held more accountable for the output actually? I'd be interested in how many students would choose to use LLMs if faults weren't penalized more.

Comments URL: https://news.ycombinator.com/item?id=46678998

Points: 1

# Comments: 0