I think that evals matter now even more than before because we have a lot of models closed source, open weight models that are doing really great in the software engineering domain. And of course, you can rely on your gut feeling, vibe checks, or maybe one or two your most favorite questions to choose between the options. But everything is fun until you roll out something into the production and it just breaks down and clients are unhappy. So, I think that we need to evaluate everything. And before we will deep dive, uh I want to share a small fact about me. Uh so, actually, I have a very non-traditional background for AI research. I'm a dentist by training. That's me 10 years ago. And that's why on my Google Scholar, I have papers from like NeurIPS and ICML about RL and test-time scaling along with some psychotherapy or medical insurance problems in dentistry. And in the medicine, cost of every mistake is really high. And I think that uh for the AI domain, we also could say that uh cost of each mistake is higher than traditional software engineering. And actually, I should say that I believe that like dental pain and infrastructural pain are kind of similar because both of them uh will not let you sleep at night. But with dental pain, you could go to the dentist and he will uh cure you. But with the infrastructural pain, you need to do with it something by yourself.

So, Sweet Revenge, uh it's fresh real-world software engineering task on 30 models evaluated every month. So, what does it mean fresh? Most of the benchmarks, uh during their release, they release questions and solutions. So, implicitly or explicitly, this data can become a part of the pre-training of the next generation of models. So, if you want to build some open truly decontaminated benchmark, um time splits are the only way. That's why every month we collect only fresh problems from the previous month and then assess the models' capabilities. In terms of the real world, in pre-LLM era, there were a lot of benchmark about, for example, um some bracket sequence or ordering correctly adjectives in English, but now we need some natural problems that people could ask systems to do. And even more, some well-paid problems like software engineering, for example. Also, software engineering problems and task are not about the simple question answering. They are truly subtasks. So, it means that to solve the issue or implement the feature, you need to understand the structure of repository. You need to try to write some tests, implement the solution, run the test, uh reproduce the mistakes or a bugs. And also, there is some multi-turn and naturally long context task. So, it's not just concatenating some text or books. No, it's truly long context task. And also, it is about tool use harnesses. So, that's why I believe that software engineering domain is really valuable for evaluations. We also evaluate something like 30 models with the same harness, simple same harness. And for the reference, we also give uh some numbers for Claude Code, Codex, and Genie harnesses. And we'll add actually more and report a lot of stuff. And I always read all the comments on local llama sub Reddit and X and try to add most actual and interesting models. Of course, we get requests like, "Okay, can you please evaluate some obliterated role-play 69 billion parameters agent?" But we mostly stick to the most popular ones.

Um for any verifiable software engineering task, actually, we have three main components. It's similar for SWE-bench, SWE-bench other domains, terminal bench. You have some task description. For us, it's just original issue title and description uh from the given time frame um from some permissive but popular open-source repository. Uh for the sandbox, you can call it environment or real environment, sandbox snapshot, but basically, it's just an executable Docker image with the installed dependencies so we could run the test of the project. And the third one is a verifier. Basically, it's just a test from the pull request that solved some issue or implemented uh some feature. And here, I would say that there is actually two sets of test fail to pass. It is the test that should be failed before uh solving the issue, for example, and should be passed after. And pass to pass, it's something like a regression test. And also, uh it's important to say that every task is not just a question but mostly some Docker image of 1 or 10 GB. So you need uh good infrastructure, actually, to run everything. I think that this is one of the most important uh slides... the thing is that every month, we verify every task, and uh we have a really big uh bank of the problems with the task because I believe that it is not too easy to say what is it look like a perfect task, but we can say what makes it bad. So for problem description, you actually need something balanced, not too vague, not too over specified, not too easy, not too hard, because for too easy problems all the models will solve it and your effective size of benchmark will be less. For the verifier and test, here's one of the examples. So, usually software engineers write the test after implementing some solution. So, they may be some kind of over fitted. Here, for example, test require the agent to generate exact substring in the error message. So, even with the correct solution, this past will this test will not be passed. And you need a stable infrastructure because you need to minimize the infrastructural noise during your runs. For example, your test could connect to some external resources and it will be some dependency. Or we had a problem in one of pipelines. So, several images just get some default time like 1970s and some tests were light on that. So, we just get some problems with this kind of evaluations. In my opinion, for our benchmark collection is mostly a filtering problem because we have a really good source of task and information like GitHub. We use GitHub archive as main source for pull requests and issues for large-scale projects and just GitHub API for the smaller ones... at the end we try to choose sample that is 10% bigger than we need in our final runs because after running some models, you could face uh problems in terms of task quality that could be visible only after uh agents will try to solve it. And for the final set of task, we manually verify.

Here's the most favorite slides. So, we found at least two ways how models cheat. First one is well-known issue. It is all about Claude code here, but it will be also about Codex and other models as well. So, the thing is that during our runs before, when we build our Docker image, we do a checkout to the base commit before the solution was implemented. So, agent will start doing something there. And if you will run command git log with all flag, then you'll get an access to the overall git history. So, that's how, for example, Claude just look up to the future to the solution patch and copy pasted it and so successfully solved this issue. After that, we remove all the future Git history because previous Git history might be helpful to get some context working with the issue, but we need to remove the future one. After that, Claude code came up with the web patch tool. It has web patch tool, so it just went to GitHub repository original one to see the conversation in the original issue pull request and solved it. Okay, after that, we restricted web patch tool. So, Claude Okay, I have curl. Let's just use bash command with curl. We'll go to the original issue. Here you can see that actually Claude code also formatted the conversation to be more convenient and then just check the original test in the main and solve the issue. So, when models uh get better, actually, I believe that they may might like to tend to cheat even more and do some reward hacking. So, we solve only with some kind of post-processing and trajectory analysis and try to come up with uh new solutions as well.

I think that one of the main reasons why we made uh this benchmark and uh maintain it, we want to share some practical value with the real AI engineers and AI creators. So, that's why we report not only some mean resolved metric, but also um tokens per problem, tries per problem, and we do five runs per each uh task to report some confidence intervals and also pass at five, something like if uh model solved uh each task at least, we uh think that it's successful to give some kind of potential of the model. Also, you can check something like pass all five if you need reliability... Um after some analytics in terms of economics, tokens, and a price per problem, we also want to do something on trajectory level because I think that it is a source of a lot of insights about how some models uh work in our or external harnesses. And the next one is about if you know how to make well, evaluation or benchmark, you could use the same pipeline to collect some validation set, for example, and to think about training. And I don't say about like SFT or RL. At first, you could just try with choosing between models, harnesses, and parameters on your validation set. And then maybe do some kind of auto research or just update your prompts and tools. Then do some uh simple rejection sampling fine-tuning or distillating from the uh bigger models. And then move to more complex strategies like GRPO. So, we use the same pipeline that we use for SWE Bench leaderboard to make two big open-source releases. First one is uh SWE Bench. We released it uh last year. It is something like 30,000s of RL environments like real-world software engineering tasks with Docker images. And it was used by some frontier labs to train better models. And now we also released SWE Bench V2. It is something about uh software engineering tasks on 20 programming languages... I think that uh for the future, we need to think about more long-horizon tasks, more about something complex, and something about code quality, as well. Because if you will check any patch from SWE Bench submission or SWE Bench submission, you will see uh some problems that actually uh the real developers will not do and during the review you will say that okay, it's not how things work actually. For example, Gemini GLM GPT models, they tend to produce some reproduce tests or files and then just don't remove it... So, yeah, I think that we need to come with some long horizon task more trajectory analysis and then move on to training better models.