Separating signal from noise in coding evaluations

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• Hacker News
• Interesting timing to release this just when SWE-1.7 and Grok 4.5 came out being much cheaper than GPT-5.5.
  by porphyra
• Aren’t we past the point of needing benchmarks? If we’re as close to AGI as Sam says then the proof should be in the pudding. OpenAI should build a competing CRM / Figma / Photoshop with a couple dozen engineers and a Dyson sphere’s worth of compute and just prove the capabilities.

  This all feels like a 2024 re-run. Oh, ChatGPT is going to cure cancer? Then find ONE rare cancer and CURE IT. OpenAI has access to the best models and compute - so cure fucking cancer! What the fuck are you waiting for?

  by therobots927
• Translation: other labs have learned to benchmaxx SWE-Bench Pro better than they do
  by 2001zhaozhao
• Lately my benchmark is build123d - trying to force them to build me functional parts only by the description. All of the models don't perform well.
  by ReptileMan
• IDK, sounds like it has brute forced my password already.
  by mgiampapa
• Seems like depending on your field these days, the hot thing to do is build your own private benchmarks.

  In my own testing, no frontier model knows how to replicate an original 1990s Super Soaker prototype design, which for the most part, should be almost completely possible with Home Depot parts.

  They just don't understand PVC parts, triggers, etc.

  by bellowsgulch
• Or you’re getting steered into la la land because of your prompt
  by thierrydamiba
• Or defensively expect models to be stupid.

  Seems the smart thing to do is not assume an agent will do the right thing. But to create the scaffold / harness that enforces constraints to steer them towards a good result.

  Then you can swap out the really smart model for maybe something cheaper.

  by softwaredoug
• It's a "commonsense spatial reasoning/problem solving" kind of problem. LLMs fail at spatial reasoning forever.

  What humans "easily" solve in seconds with raw spatial reasoning LLMs often find easier to solve by invoking A* or a constraint solver.

  Might be that text data is particularly bad at teaching that to LLMs. Or that being good at spatial reasoning requires true recurrence, and autoregressive chain of thought is a poor substitute. Or it might be that human brain was optimized by evolution for solving spatial problems in open ended 3D environments for hundreds of millions of years, and only optimized for writing computer code for a few decades at most.

  The current frontier is halfway competent at benign closed 2D work, but still completely fumbles anything remotely close to open ended real world 3D work.

  by ACCount37
• DeepSWE is the one I generally trust: https://deepswe.datacurve.ai/
  by CSMastermind
• What is considered SOTA for SWE benchmarks now?
  by dandaka
• strawberry
  by carabiner
• FrontierBench
  by enraged_camel
• https://cognition.ai/blog/frontier-code (disclaimer - was on the team - but also we covered swebench pro/deepswe issues in here as well.)
  by swyx
• I've generally found DeepSWE[0] to be pretty true to reality.

  [0]: https://deepswe.datacurve.ai/

  by EuanReid
• Either DeepSWE [0] or FrontierCode [1], depending on personal goals and requirements. The later is more interesting for me personally, due to the design of the benchmark heavily grading "mergability", i.e. how the provided output is to review and whether a serious developer can easily parse it and'd be willing to merge the result. In my mind and with my private evals, for quite some time I've held firm that a model can have a higher ceiling but that has limited value if I do not feel truly confident in signing off on the code.

  [0] https://deepswe.datacurve.ai/

  [1] https://cognition.com/blog/frontier-code-1.1

  by Topfi
• This doesn’t seem like opportune timing to announce days before a new model drop
  by johngoode
• Achieving AGI will be more than just passing all benchmarks, it has to account for the unknown problems too.
  by xacky
• This ties into the bias-variance tradeoff (https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff) common with building non-LLM models. The solutions can only be a) figure out how to get LLMs smaller with similar performance so they don't memorize things/game the benchmarks and b) build benchmarks that are indeed comprehensive for all real-world data, which is infeasible.
  by minimaxir
• AGI is a long way off. Unless you’re talking about some unknown-to-me LLM marketing BS which is called “AGI” or something, I guess. Artificial general purpose intelligence is so different to LLMs or image AI that they are completely incomparable, except to say that they are all artificial. AGI will do a lot more than token prediction.
  by naikrovek
• they should be consulting Donald Rumsfeld and make sure they implement the Unknown-Unknowns benchmark, because thats how they get you
  by cyanydeez
• Unless they have something in the labs that massively departs from their current products, AGI isn't on the table and is purely hype for marketing purposes.
  by metalliqaz
• Studying for leetcode exams in the age of AI agent coding evaluations is a wild feeling.
  by Ancalagon
• It reads to me like "We did all the work you'd do to figure out how to fix the benchmark, then we decided to throw out the benchmark". Is there some reason the underlying data is so golden that it can't be patched? At the end they argue for a slightly more curated approach to benchmark generation, but my gut is that using messy ill-specified tests taken from real world data and patching them into fairness would be a pretty solid path to take.
  by jheitmann
• If they fixed it, then it wouldn't be SWE-Bench Pro anymore, right? It'd be "SWE-Bench-Pro-Fixed-OpenAI." I think it's better optics for the independence of the benchmark if the OpenAI team lets some third party do the fixing and release the improved benchmark.

  ...Although OpenAI did exactly that when they released SWE-Bench Verified, so maybe I'm talking out of my butt here.

  by Centigonal
• Pointing out problems (e.g., hidden tests that assume narrow implementation details) is much easier than fixing them (e.g., creating tests that work for any possible choice of implementation).
  by tedsanders
• Didn't we all know from the start that all of SWE-Bench was flawed? Even the authors concede the limitations and have long since moved on.
  by shay_ker
• SWE-Bench Pro was created to replace SWE-Bench and fix these problems.
  by paxys
• Based on the numbers here it seems there’s less than 800 tasks in the entire benchmark. That is enough for a handful of engineers to comb through in a week (which is what OpenAI eventually did here).

  On the one hand, kudos to them for actually doing that work.

  On the other hand, garbage in, garbage out. It’s a bit embarrassing for the original authors to have not actually checked, and it’s embarrassing for everyone downstream to have not checked either.

  Also if you check the article, although an LLM did find issues, it tended to underestimate issues that professional software engineers found.

  by janalsncm
• All of the benchmarks are pretty terrible when you look under the hood.

  For context, I've been iterating on a "supervisor" to replace a lot of the rigamarole spent when working with Codex/Claude Code, and recently ran this agent against Terminal Bench 2.1

  At first I was excited, because my spec-driven supervisor outperformed vanilla codex on a bunch of tasks, however as I looked deeper, I found a ton of issues with the tasks themselves.

  The main takeaway is that the instructions are often ambiguous while the test cases are overly specific.

  A few examples:

  - For `configure-git-webserver` the task includes language like "so that I can run" which blurs the line between what the agent should deliver vs. what should be removed. This causes an overthinking agent to configure the server, and then remove the exact files that the verifier checks, because if the user were to run the same commands, they would conflict.

  - For `make-mips-interpreter` the task includes the language "I will check that you booted doom correctly" which causes the agent to retain the generated file `/tmp/frame.bmp` because the supervisor expects the user to check that _it_ booted Doom correctly, not that Doom boots correctly in an isolated way. The verifier then fails to start Doom, because it exits when an existing `/tmp/frame.bmp` exists, not checking to see that it's created from the boot[0].

  - For `mcmc-sampling-stan` the supervisor agent often reached the right value, but produced a domain-specific numeric output in scientific notation, rather than a simple decimal form. The verifier fails because it parses the result incorrectly[1].

  These are just a few of the inconsistencies I've found, which leads me to believe that Terminal Bench 2.1 is already saturated, and the results from GPT-5.6 and Mythos are basically at the top of the expected threshold (88.8% and 88% respectively).

  The biggest issue, as I can tell, is that most benchmarks are "one-shot" and rarely test the model+harness on long iteration tasks, which is the primary way most users use these tools in practice.

  [0] https://github.com/harbor-framework/terminal-bench-2-1/issue...

  [1] https://github.com/harbor-framework/terminal-bench-2-1/issue...

  by jumploops