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SWE-bench Verified no longer measures frontier coding capabilities

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Re: SWE-bench Verified no longer measures frontier coding capabilities

#51
post #49
post #42

Earlier quoted context omitted.

The problem isn’t that the tasks are impossible to solve, it’s that they’re underspecified and/or impossible to solve consistently (ex. because a test is expecting the solution function to have a specific name that wasn’t specified in the task itself). So maybe Anthropic runs Mythos through the benchmark 10000 times and takes the highest score, who knows?

We actually know that a "100% pass rate" is trivially possible: https://rdi.berkeley.edu/blog/trustworthy-benchmarks-cont/ Anthropic p-hacking the benchmark strikes me as cheating, and somewhat unlikely. Mythos figuring out how to cheat at the benchmark strikes me as much more likely. But if that hypothesis is the explanation the interesting part is Opus 4.7 (but not 4.6) seems to be doing the same.

>Mythos figuring out how to cheat at the benchmark strikes me as much more likely.

Define "cheat". If it's just hacking the test harness to return "PASSED", surely this would be easily detected with some human auditing? It sounds far more likely their solution are designed to pass the incorrect tests. That might be considered bad in a SWE context, but it's not exactly cheating either. It might even be considered a good thing, eg. in the context of backwards compatibility.

[1] https://learn.microsoft.com/en-us/troubleshoot/microsoft-365...

Re: SWE-bench Verified no longer measures frontier coding capabilities

#52

Why don't they ask their premier model to generate a bench for them? Jokes aside, a benchmark I look forward to is ARC-AGI-3. I tried out their human simulation, and it feels very reasoning heavy. Leaderboard: https://arcprize.org/leaderboard (Most premier models don't even pass 5 percent.)

They focus on minimizing the number of moves and don't allow any harness whatsoever, putting the bar extremely high. The current top verified contender (Claude Opus 4.6) is at only 0.45%. But with how new it is, I expect a lot of improvement in the next generation of models.

Optimal for judging actual reasoning ability rather than an LLM's ability to regurgitate knowledge from a necropost on HN/Reddit/Twitter from 2018.

Re: SWE-bench Verified no longer measures frontier coding capabilities

#53

A better benchmark needs to be objectively scored, have multi-disciplinary, breadth, and be scalable (no single correct answer). That's what we designed at https://gertlabs.com . We put a lot of thought into it, and kept it mostly (not fully) related to problem solving through coding.

Wow. This benchmark definitely feels more accurate than the other rankings I've seen. My experience with gpt 5.4/5.5 is that they are technically flawless and if there are any technical issues that is because the input didn't provide enough clarity; that's not to say that it doesn't autonomously react to any issues during bug fixes or implementations, but it'll tend to nail its tasks without leaving behind gaps.

Opus otoh is overrated in terms of its technical ability. It is certainly a better designer/developer for beautiful user experiences, but I'll always lean on gpt 5.5 to check its work.

The biggest surprise in the benchmark is Xiao-Mi. I haven't tried it yet, but I will be after looking at this.

Grats on your team for putting together something meaningful to make sense of the ongoing AI speedrun! Great work!

Re: SWE-bench Verified no longer measures frontier coding capabilities

#55

Earlier quoted context omitted.

They focus on minimizing the number of moves and don't allow any harness whatsoever, putting the bar extremely high. The current top verified contender (Claude Opus 4.6) is at only 0.45%. But with how new it is, I expect a lot of improvement in the next generation of models.

Optimal for judging actual reasoning ability rather than an LLM's ability to regurgitate knowledge from a necropost on HN/Reddit/Twitter from 2018.

a small harness that stores text files and manages context could be useful, otherwise you lose all ability to measure that skill (and that's important because it represents real world use cases on large code bases)

Re: SWE-bench Verified no longer measures frontier coding capabilities

#56

> We audited a 27.6% subset of the dataset that models often failed to solve and found that at least 59.4% of the audited problems have flawed test cases that reject functionally correct submissions, despite our best efforts in improving on this in the initial creation of SWE-bench Verified. Is this saying a quarter* of the questions and answers were wrong, this whole time?! If so, how was this ever, in any way, a va…

Imagenet is one of the most popular datasets on the planet. Turns out, a significant fraction of its images are mislabeled. In the limit case the model would have to fit towards wrong answers to get higher than a certain percentage. The answer is “it works because ML wants to work.” It’s surprising how far you can get with something flawed. It’s also why such huge breakthroughs are possible by noting flaws others hav…

Has it been reasonably possible to overfit to the errors in ImageNet, or are they effectively random noise?

Re: SWE-bench Verified no longer measures frontier coding capabilities

#57

Issue with these benchmark also is that they measure a model you are unlikely going to be routed to. My experience with Anthropic is that despite using Opus 4.6 and 4.7, most of the time the performance is matching low B parameter Qwen. I think there should be a way to verify what model is actually being used to process prompts - that should be independently verified. At the moment it is so bad, you have to ask verif…

> My experience with Anthropic is that despite using Opus 4.6 and 4.7, most of the time the performance is matching low B parameter Qwen.

Is this just the next level of the "they're serving quantized models!" theory?

Re: SWE-bench Verified no longer measures frontier coding capabilities

#58

A better benchmark needs to be objectively scored, have multi-disciplinary, breadth, and be scalable (no single correct answer). That's what we designed at https://gertlabs.com . We put a lot of thought into it, and kept it mostly (not fully) related to problem solving through coding.

Wow. This benchmark definitely feels more accurate than the other rankings I've seen. My experience with gpt 5.4/5.5 is that they are technically flawless and if there are any technical issues that is because the input didn't provide enough clarity; that's not to say that it doesn't autonomously react to any issues during bug fixes or implementations, but it'll tend to nail its tasks without leaving behind gaps. Opus…

Much appreciated! MiMo V2.5 Pro is by far the most underrated recent release (probably because it wasn't open weights from the start).

Re: SWE-bench Verified no longer measures frontier coding capabilities

#60
post #25

Curiously Opus 4.7 claims to have a 87.6% pass rate and Mythos claims to have a 93.9% pass rate... leading to the conclusion that it's actually possible to "solve" the problems that OpenAI claims are incorrect.

Part of the issue they mention is contamination - the tests are in the training data.

The other issue they mention is being overly constrained vs. what is asked for - such as requiring specific class or function names to pass that were not part of what was specified.

It might be possible that even to the extent they are not contaminated Claude is better at predicting what sort of function names would be used in the repository (this fits my experience in using it on a number of projects with very different styles - I've found it to be good at "when in Rome") - this is a laudable trait, but it's also not what SWEbench claims to be measuring.

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