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Kilian Lieret

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@KLieret

Meta Superintelligence, prev. Princeton. SWE-bench multilingual/multimodal, (mini-)SWE-agent, SWE-smith, CodeClash, ProgramBench, SWE-sweep
New York github.com/klieret Joined May 2021
295 Following 3.1K Followers 700 Posts

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Kilian Lieret Verified account @KLieret
Can LMs discover & fix bugs if you don't tell them what went wrong? To proactively maintain a repo, agents need to find problems before users do.

SWE-sweep benchmarks this on 100 repos, 22 languages, 4k real bugs (numpy, php interpreter, lean kernel). Top models get <5%.
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Kilian Lieret Verified account @KLieret
Amazing comparison of harnesses across tasks (and I'm super happy to see mini-swe-agent on the top)
Joël Niklaus Verified account @joelniklaus 2d
I burned a shitload of compute so you don't have to. Releasing the Harness Leaderboard.

Which harness should you run your open model in? I ran 28,849 trials to find out.

The leaderboard compares nine coding-agent harnesses on open models. In each comparison only the harness changes: the model, provider and reasoning effort stay fixed, and every task gets three attempts.

The trials ran GLM-5.3-Flash, DeepSeek-V4.1-Flash and Flint on Terminal-Bench 2.1 and DeepSWE, for $4,746 of inference.

Averaged over the three models, mini-swe-agent (@KLieret), a bash-only agent, solves 51.4% of tasks and @openclaw 27.3%. Four others are statistically tied with mini-swe-agent.

One of them is Pi (@pidotdev), at 49.4% for $0.29 per solved task, about 45% less than mini-swe-agent's $0.53.

The best harness depends on the model: mini-swe-agent on GLM-5.3-Flash, Hermes Agent (@NousResearch) on DeepSeek-V4.1-Flash and Kimi Code (@Kimi_Moonshot) on Flint.

@opencode costs $3.35 per solved task, 11.7 times as much as Pi.

See Harness Hub's Harness Lens for what the nine harnesses do differently. It documents the prompts, tools and compaction of 34 coding-agent harnesses and links every claim to its source.

Leaderboard: huggingface.co/spaces/joelnik…
Harness Lens: huggingface.co/spaces/harness…
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Kilian Lieret Verified account @KLieret
SWE-sweep is the least saturated benchmark on the model card. New SoTA is 9.6%! Congrats to the Gemini team
Sundar Pichai Verified account @sundarpichai Oct 28
I did say we'd be releasing our models periodically...introducing Gemini 4 Carbon, our next frontier model.

Following Argon's release just two weeks ago, Carbon shows the pace at which we’re pushing the frontier, it ranks #1 on the recently updated v5.0 of @ArtificialAnlys Intelligence Index at 59. It also leads across key cybersecurity evaluations scoring 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation, and 71.0% on CWE-bench for patching.

We’re seeing significant improvements in long-horizon tasks even compared to Argon, and we’ve strengthened frontier safeguards.

As we did with Argon, we’re going to roll it out gradually + responsibly. Some trusted cyber defenders will get access starting this week. We'll be expanding it to more partners through our Fairwind Program, ahead of wider availability soon, so hang tight!
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Kilian Lieret Verified account @KLieret
Congratulations to the Gemini team on reaching SoTA on SWE-sweep, scoring 9.6% (narrow leaderboard over Astra 6)! Hoping to update leaderboard with details soon!
Logan Kilpatrick Verified account @OfficialLoganK Oct 28
Introducing Gemini 4 Carbon, it's a big jump over Argon which we released just 2 weeks ago.

Gemini 4 Carbon is SOTA on cyber, long horizon tasks, ranks #1 on Artificial Analysis V5, significantly improves on usability in domains like coding, and is more aligned.
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Kilian Lieret Verified account @KLieret
Very interesting study about proactive bug finding agents. Recall and precision are with respect to a holdout set of bugs that humans found, findings is the total number of bugs found
Mark Müller Verified account @mnmueller Oct 28
New paper: Frontier Coding Agents Find Different Bugs Than Maintainers Fix

In proactive bug finding, agents report fewer than 20% of the bugs humans fixed. But they report a lot of other bugs, most of which are valid. This points towards an important distributional mismatch.
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