Ofir Press

@ofirpress.bsky.social

I develop tough benchmarks for LMs and then I build agents to try and beat those benchmarks. Postdoc @ Princeton University. https://ofir.io/about

We evaluated the new GPT models with a minimal agent on SWE-bench verified. GPT-5 scores 65%, mini 60%, nano 35%. Still behind Opus 5 (68%), on par with Sonnet 4 (65%). But a lot cheaper, especially mini! Complete cost breakdown + details in 🧵

SWE-bench scores (same as text)

Do language models have algorithmic creativity? To find out, we built AlgoTune, a benchmark challenging agents to optimize 100+ algorithms like gzip compression, AES encryption and PCA. Frontier models struggle, finding only surface-level wins. Lots of headroom here! algotune.io

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I have a post where I talk about how to build good LM benchmarks. I've had to edit the part where I talk about how I think you should try to make your benchmark hard, multiple times now, since LM abilities are accelerating so rapidly.

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I prompted Claude 3.7 to use Javascript to animate a ride in the Kingda Ka rollercoaster at Six Flags in New Jersey. I did not give it any images/videos from the ride, or any other additional info, and it has no web access.

We just updated the SWE-bench leaderboard with *14* new submissions! Congrats to blackbox.ai & aide.dev on setting new SoTA scores on Lite & Verified! Congrats to Google on your first submission! Credit to John Yang, Carlos E. Jimeneze & Kilian Lieret who maintain SWE-bench/SWE-agent

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When we started working on SWE-agent the top score on SWE-bench was 2%. I told the team that if we got 6%, we'd have a good paper, and I'd buy everyone gelato. I thought 6% was very ambitious but doable. We ended up getting 12% 🤯 so I cooked dinner for everyone.

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Until recently, we didn't have models that were *trained* for tough multi-turn tasks like SWE-bench. My hypothesis: now, with the new Gemini and O3, we're finally seeing how much accuracy can improve when models are trained for these tasks. This is just the beginning!

When ChatGPT came out, and for a year afterwards, it seemed like OpenAI had a huge moat that was insurmountable. Now Anthropic is matching their performance, Google and Meta are close, and it seems like the academic/open source community understands how to build these models ->

The team behind the Chatbot Arena LLM leaderboard just released a new variant: web.lmarena.ai This one tests models on their React, TypeScript and Tailwind abilities. My notes (including a leaked copy of the system prompt) here: simonwillison.net/2024/Dec/16/...

WebDev Arena

New leaderboard from the [Chatbot Arena](https://lmarena.ai/) team (formerly known as LMSYS), this time focused on evaluating how good different models are at "web development" - though it turns out t...

simonwillison.net

We're presenting SWE-agent tomorrow (Wed) at the 11AM poster session, East Exhibit Hall A-C #1000. We're going to talk about a lot of upcoming SWE-agent features. Join @jyangballin @_carlosejimenez @KLieret and me. I also have a bunch of SWE-agent stickers to hand out :)

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EnIGMA sets new state-of-the-art results on @stanfordnlp.bsky.social's CyBench, which tasks LMs to find security vulnerabilities. Such Capture The Flag tasks make for challenging benchmarks—demanding high-level reasoning, persistence and adaptability. Even expert humans find them hard!

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I'm on the academic job market! I develop autonomous systems for: programming, research-level question answering, finding sec vulnerabilities & other useful+challenging tasks. I do this by building frontier-pushing benchmarks and agents that do well on them. See you at NeurIPS!

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The Amazon thing that summarizes all of the reviews into both a sentence and a bullet point list is so awesome and I wish I could figure out a way to make a benchmark out of it :)

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