Jackson Clark (@HacksonClark) header
Jackson Clark (@HacksonClark) profile picture

Research Scientist Intern at Meta Superintelligence Labs | CS PhD Student @ UIUC Teaching AI to fix bugs. Wired in.

Chambana, IL
Joined December 2022
Photo by Jackson Clark (@HacksonClark)Photo by Jackson Clark (@HacksonClark)Photo by Jackson Clark (@HacksonClark)Photo by Jackson Clark (@HacksonClark)
Jackson Clark retweeted
You don't need an AI SRE. We took kubectl away from Claude Code and gave it only @RadarK8s MCP tools. It's now #1 on SREGym: 95.2% of 90 live Kubernetes faults diagnosed and fixed. #2 is at 72.2%. Writeup: radarhq.io/blog/sregym-leade… 41s to a diagnosis, on a quarter of the tokens. Thanks @tianyin_xu @HacksonClark @SaadMRP and everyone else building #SREGym in the open. At KubeCon next week? Come see it at booth 365.
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Jackson Clark retweeted
TLAPS-Bench aims to raise the standard of TLA+ spec from "checked" to "proved." We keep being impressed by the rapid advances in the proof capabilities of frontier models in the past 6 months. Today, we're able to prove most protocol-level specs in TLA+ with reasonable cost. System-level specs are still nontrivial, but honestly are not out of reach especially given the crazy speed of AI. What I like the most is Tilapia 🐟 TLA+ Proof Infrastructure for Agents 🐟 the name is coined by @muratdemirbas, and the proofs we generated using Tilapia for many system/protocol specs that were not proved before. No matter how models evolve, Tilapia will be a useful proof service for TLA+. The project is a huge effort across 11 organizations. I got to collaborate with many core @tlaplus colleagues I admire. @lemmster proposed the project in May and @qiancheng9788 drives it since then. Great work!
Meet TLAPS-Bench: A new benchmark of 265 real-world #TLAPlus proof targets to push AI agents to crack machine-checked correctness proofs using #TLAPS 44.3% rate at pass@3 46 bugs found Paper: arxiv.org/abs/2611.02849 Code: github.com/specula-org/TLAPS… lnkd.in/p/g3ET9E8H
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Meet TLAPS-Bench: A new benchmark of 265 real-world #TLAPlus proof targets to push AI agents to crack machine-checked correctness proofs using #TLAPS 44.3% rate at pass@3 46 bugs found Paper: arxiv.org/abs/2611.02849 Code: github.com/specula-org/TLAPS… lnkd.in/p/g3ET9E8H
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Jackson Clark retweeted
The full paper on our self-defining operator is now out. We show how an agentic operator that remembers past incidents and synthesizes its own playbooks cuts cost and MTTR by over half. arxiv.org/abs/2610.14547
Early report from the first project we are pursuing as part of the "self-defining systems" research, led by @sudopowr. So much potential to lower operational toil and MTTR!
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Jackson Clark retweeted
Congrats to the OpenAI team on a huge jump on SWE-sweep with Astra, for less than half the cost of 5.6! So much more to do here. SWE-sweep will help launch the era of proactive agents, hopefully leading to much less bugs & vulnerabilities in software.
Let's fix bugs before they cause a global crisis. Updated SWE-sweep leaderboard sees OpenAI and Anthropic models double the number of bugs found, while halving costs. Astra is the best model, but still finds less than 10% of all real-world bugs tested. Details in 🧵
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Jackson Clark retweeted
Let's fix bugs before they cause a global crisis. Updated SWE-sweep leaderboard sees OpenAI and Anthropic models double the number of bugs found, while halving costs. Astra is the best model, but still finds less than 10% of all real-world bugs tested. Details in 🧵
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Jackson Clark retweeted
Already some meaningful motion on the SWE-sweep leaderboard. Only up from here
Let's fix bugs before they cause a global crisis. Updated SWE-sweep leaderboard sees OpenAI and Anthropic models double the number of bugs found, while halving costs. Astra is the best model, but still finds less than 10% of all real-world bugs tested. Details in 🧵
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Jackson Clark retweeted
Students continue having fun playing with Decision Models for SRE tasks at #SREGym. @YukunHeZS came up with a new design where Decision Models drive the investigation and LLMs sit at the passenger seat. The combo makes failure diagnosis both fast and accurate! He also tried both @typesafeai Jev and @OpenAI GPT-6 Luna Decisions. Many cool results. Check it out: sregym.com/blog/decision-mod… Great work @YukunHeZS @SaadMRP @Yiming_Su3 @HacksonClark
1/8 In our latest experiment, we let Jev, a decision model, investigate system failures and asked an LLM to check its diagnosis. Across 21 SREGym-Lite faults, review raised Jev’s pass rate from 78.1% to 89.5%, with diagnoses averaging 38 seconds.
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Jackson Clark retweeted
TerminalBench is one of the better run eval programs, sad to see that there wasn't a correspondence. Some bad incentives to increase twitter drama instead of increasing quality of evals.
The @SnorkelAI team did an audit of these findings using our internal QC platform. Tl/dr: We agree with many of the findings, but ~55% of the fixes from @preferencemodel had their own issues with reward hacking or over-prescriptiveness. Additionally: ~40% of the issues found were already tracked in the upstream repo - including 21 tasks with outstanding fix PRs! Building datasets like this - and maintaining ambitious open benchmarks around them - is hard. We believe it requires open, collaborative review with community maintainers and relevant domain experts to get right. @terminalbench was one of the first community efforts to build an open, continuous benchmark, and drive a vision of how OSS contributes to the rapidly advancing frontier of AI. We hope that the community will invest in working collaboratively with benchmark maintainers and task authors to keep this vision alive. --- Some takeaways + details of our preliminary audit: (1) We confirm 100% of the reward hacking / verification gap findings using our QC system. However, we find that 17/44 (39%) of the corrected tasks are just as or more reward hackable. Additionally, we don’t yet see evidence that these verification gaps meaningfully affected benchmark rollouts within CIs, though our analysis here is still ongoing. (2) Our preliminary review raises questions about the underspecification findings for 22/41 (54%) of the flagged tasks, including 7 explicitly marked by task authors as intentional design choices. These cases warrant expert peer review and adjudication: balancing over/under-specification is a critical and tricky design decision - much of human intelligence concerns dealing correctly with reasonable and realistic underspecification - and we believe these corrections made to the tasks are likely too over-specified, reducing realism and difficulty relative to spec. (3) Around 40% of the issues found were already tracked in the upstream repo (including 21 tasks with outstanding fix PRs)! OSS projects require community efforts to correct identified open issues, and ideally audits like these are done collaboratively with task authors and maintainers directly, in the traditional community spirit that has kept OSS alive for decades.
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Jackson Clark retweeted
Introducing Slingshots // FOUR: 14 projects across eight top institutions. Self-improving agent systems and harnesses, maximally-open models and training data, applications for research and decision-making – plus kernels and more. Say hello to the batch 🧵
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Jackson Clark retweeted
Cognition (@cognition) just released #Devin Oncall and took #1 spot on #SREGym-Lite! It's mind blowing to see the pace of progress made on AI for SRE in the past months! We need to move faster to 2.0. Read the Devin blog, devin.ai/blog/introducing-de… What a day of AI SRE and #SREGym ! Huge congrats to @anhangz @yunpark93 and @jake_rachleff ! Thank you for always being kind and supportive to the students!
Huge congrats to @cognition for taking the #1 spot on #SREGym! 🎉 It’s been incredible to have a front-row seat to the progress in AI SRE. Shoutout to @anhangz, @yunpark93, and @jake_rachleff for an amazing release pushing the frontier of autonomous incident response. One step closer to a world where humans don't have to be on call. 🚀 SREGym 1.1 is out now, with 2.0 on the way to push the frontier even further. 📊 Follow the progress: sregym.com 📝 More on Devin Oncall: devin.ai/blog/introducing-de…
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Jackson Clark retweeted
3/ We also evaluate Scout on the public SREGym benchmark to show that it generalizes to a different harness and task distribution.
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Jackson Clark retweeted
Congrats @bthecohen and @datadoghq on releasing #Scout 1.0! I've been waiting for a post-trained SRE model for a while and #Scout makes my day. Thanks for trying #SREGym ! and thanks for writing a very informative tech report! web-assets.dd-static.net/425…
To check that Scout does not just fit our harness, we also ran the public SREGym benchmark: 90 faults, with the supplied Stratus harness. Scout 1.0 xhigh resolves 80 of 90 faults, competitive with the best frontier models at least 2× cheaper. Scout 1.0 medium beats GLM-5.3 at more than 4× lower cost.
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Jackson Clark retweeted
This is the #SREGym results. @HacksonClark. Very cool stuff. nitter.cf/bthecohen/status/21107…
To check that Scout does not just fit our harness, we also ran the public SREGym benchmark: 90 faults, with the supplied Stratus harness. Scout 1.0 xhigh resolves 80 of 90 faults, competitive with the best frontier models at least 2× cheaper. Scout 1.0 medium beats GLM-5.3 at more than 4× lower cost.
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Jackson Clark retweeted
so exciting to see our work SWE-fficiency used and some really incredible results on designing agents for codebase optimization! one step closer to self-maintaining codebases :)
AI is cracking math problems. What's holding it back in engineering? The cost of evaluation. Agents can generate ideas in seconds, but testing them can take hours or days. 💡 Meet DuoDiscover: where evaluation is part of what agents discover 👇🧵 Some highlights: 🏆 #1 on NVIDIA's SOL-ExecBench 🐍 2× the experts on SWE-fficiency 🚀 1.92× on vLLM; 1.44× on FreeToken 🔥 2.68× over production TPU kernels 🖥️ Beats the best human and AI-designed CPU prefetchers
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Jackson Clark retweeted
Muse Wonder! w/ ProgramBench! w/ a main metric (% almost resolved) reported! 🚀 It's *very* good at coding, and we'll keep making it better. Look out for updates to programbench.com later this week, the % resolved numbers will blow your mind :)
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Jackson Clark retweeted
It's always really cool to see how the community uses #SREGym for different kinds of analysis. I find @RadarK8s 's analysis rather interesting. I always wanted to look into more SRE agent products beyond raw coding agents, but never found the time. They did it. The comparative analysis on speed ⏱️, effectiveness 🎯, and cost💰makes a lot of sense, and the discussion on the interface (MCP vs kubectl) is something my colleagues and I have had many deep discussions. I'd love to see more and deeper analysis!
Before you buy an Al SRE, find out what the Al you already pay for can do. If you have Claude Code, you have half of an Al SRE already. We broke a live Kubernetes cluster 50 ways (public SREGym benchmark), then had the same coding agent investigate every failure under different setups. One - with standard kubectl CLI access. It reached a correct diagnosis within two minutes 60% of the time. Already not bad. Two - when we also gave it a clear view of the cluster, like what was failing and what had changed recently (with Radar MCP), it was 86%. Same model. Three - tried a few Al SRE tools. Some of the common names you'd expect if you Google it (full details in radar site if you're curious). None of them reached the same accuracy as Claude Code with Radar MCP, and all of them were at least twice as slow, some much worse. They also burned a *lot* more tokens doing it. When you have an outage, you don't want your agent spending 10 minutes gathering context, building the model of what is going on using tools that weren't designed for it and getting lost in piles of YAML. and not even getting a better result in the end. So what does an Al SRE get you? Some general prompt on how to debug kubernetes, triggers on alerts, and maybe a nicer Ul than claude's terminal output (I'm exaggerating a bit, but you get the point). Guess what? Radar Cloud now gives you all of this as well, and cheaper than any Al SRE tool out there that publishes pricing.
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Jackson Clark retweeted
I'll present TLAPS-Bench at the Frontier Data Summit (thanks @SnorkelAI @_Incynthia), in the hope to let people know about the benchmark and find resources to run it. TLAPS-Bench is a challenging and long-horizon proof benchmark. The TLA+ specs in the benchmark come from real-world systems and protocols, so proving them has great practical values. It's expensive and still hard for frontier AI to do autonomously. We hope that with TLAPS-Bench and its toolchains, every important TLA+ spec can be formally proved beyond MC. The benchmark is a team effort of the @tlaplus foundation, the #Specula team, and many collaborators. While I'm physically the closest to SF, @lemmster initialized the project and @qiancheng9788 did the major technical work; Ruize Tang, @SaadMRP @lemmster Stephan Mertz, Igor Konnov, Chris Newcombe, @Yiming_Su3 @MunimThahmid MR Sukanya, @xu_dong_sun @disalg_spec @HacksonClark all contributed in different ways. Special thanks to @leochen_ai and @JiaweiLiu_ for supporting the benchmark, without whom we would not survive and see the unexpected interests in TLA+. For AI companies, we'd love to run your models/agents if you could throw us some tokens.
TLAPS-Bench uses the TLA+ Proof System to formally prove the correctness of complex protocols and systems. It also provides a rich, extensible, and scalable benchmark suite for frontier AI. From @tianyin_xu, one of 25+ posters at Frontier Data Summit. frontierdatasummit.ai/
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Jackson Clark retweeted
This post is long overdue, which I owe to the students from the UIUC++ SRSE program. We finally wrote a blog on how we curated a set of high-quality SRE benchmark problems by turning real-world postmortem reports into reproducible SRE problems atop #SREGym. It may give a good example of community-based data curation. Check it out, sregym.com/blog/postmortems-… The fundamental challenge of AI-for-SRE benchmarks is the quality of the problems, e.g., in terms of realism and the ability of challenging frontier AI. This experience enabled us to understand many faces of the challenge and reflect on important tradeoffs between realism, reproducibility, cost, and utility. We learned many lessons, which would guide the effort on building #SREGym 2.0. Big thanks to @SaadMRP who (unexpectedly) spent his entire summer managing the program with a heroic effort. I list all the student contributors at the end of the post. Many of them are on Twitter: @12_AbdAllAh_12 @ermiasmulu19 @sharq_fr @M_ABDz_ @MunimThahmid @Sai_Hari_g @ibnAmjid @SuMaya971503 @TalhaAsif25 @tanzimhromel @tejaspkshukla @Varunihk. They did great work to achieve high-quality problems that challenge AI frontier. If you're looking for students or employees, they are good candidates!
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Jackson Clark retweeted
Thanks for the shoutout to TLA+ @bcherny ! As suggested by Markus Kuppe, let me showcase the brilliant work done by colleagues at Specula and the TLA+ community. While Specula (github.com/specula-org) can effectively model system code in TLA+ (and then use model checkers to find hundreds of deep bugs), we can further prove the correctness of system specs using TLAPS. The TLAPS-Bench (github.com/specula-org/TLAPS…) project aims to offer a proof service for any TLA+ spec of important systems/protocols. Basically, Specula writes the spec; TLAS-Bench generates the proof. AI writes amazing proofs! We had to retire all Proof-Completion tasks in TLAPS-Bench as they're too easy for frontier AI. We also proved classic protocols and algorithms like 2PC, Paxos, TCP, etc. However, proving low-level specs of real-world system code _from scratch_ is still non-trivial. @qiancheng9788 ran a subset of TLAPS-Bench using Claude Code w/ Opus 5. The success rate is merely 51.4%. @Muse is great, but Muse Spark 1.3 can only achieve 16.7%. Ruize has a bag of tricks to push AI agents. Once he pushed Codex to prove one of the nine invariants for the ZooKeeper implementation. Codex struggled 5 days, burned $2000, and did it correctly. We ran out of money to continue. And, @xu_dong_sun shows us that liveness proofs are still difficult for AI without guidance. We have an exciting avenue, but still a lot of work to be done. If anyone wants to verify their protocols/systems, send us some tokens -- we can do it in TLAPS-Bench. @HacksonClark tells me that people are excited about long-horizon tasks -- TLAPS-Bench may be a perfect benchmark for that. @bcherny Consider further tuning Claude for TLA+, you may find more bugs and/or prove the correctness of SDK code more efficiently.
I used Opus 5.5 to formally verify the Claude Agent SDK using Lean. A couple short prompts = 16 PRs fixing various bugs and race conditions. Video attached. TLA+ also works well. I sometimes combine Lean and TLA+ to look for issues around data flow, concurrency, and state mgmt. I don't know either language well, but Claude is excellent at both. This approach is super useful for formally modeling your code and finding bugs that a human probably wouldn't have spotted. Is formal verification the future of coding (or at least, bug finding)?
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