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

Chambana, IL
Joined December 2022
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…
Introducing Devin Oncall: Devin automatically investigates alerts and bugs, helping your team diagnose and resolve production incidents. Devin Oncall already handles thousands of alerts every day for some of the world’s largest companies. Let it become your SRE today.
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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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Check out our latest installment in our Jev for SRE blog series!
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
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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Thank you so much for sharing our work! We’ve been having a lot of fun with Jev over here on the SREGym team. Have to give props to @SaadMRP and @Yiming_Su3 for pushing this direction. @typesafeai absolutely rocks, and we have some more work incoming. Stay tuned!
SREGym's blog post on using Jev to troubleshoot incidents is a perfect example of intelligent graph traversal. A lot of debugging an incident is just following the bread crumb trail. sregym.com/blog/jev-driven-s…
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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. xitter.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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SREGym 🤝 Datadog Very impressive results from @datadoghq on #SREGym. Extremely excited to see how domain-specific models can push the frontier for SRE!
Introducing Scout 1.0, @datadoghq's first model post-trained for investigating production incidents. Scout matches frontier-model investigation quality at a fraction of the cost, which makes it practical to investigate more issues, earlier. We trained Scout on realistic simulations built from real-world incidents and production telemetry, using reinforcement learning to reward accurate root causes backed by evidence.
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Thank you so much for featuring our blog! More on the way soon :)
Jev-Driven SRE Diagnosis: What Worked and What Failed sregym.com/blog/jev-driven-s…
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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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I had a wonderful at at the Frontier Data Summit presenting #SREGym. Thank you so much for hosting us @SnorkelAI and for putting on a great event! It was so nice to meet @ajratner, @vincentsunnchen and the rest of the Snorkel team in person and to see familiar faces like @bradenjhancock. Many bigs things on the way!
Last Thursday, I went to the Frontier Data Summit with @HacksonClark to present #SREGym. It was fun. We wrote down our experience and our next step (SREGym 2.0) which we shared at the summit. Thanks @SnorkelAI for having us, especially @_Incynthia, Francesca, @bradenjhancock and @ajratner. Pushing the AI frontier is like climbing mountains. We're always excited to aim for the next peak. It's great to do it with the open community #SREGym has been building. We'll make it! See our blog post, sregym.com/blog/frontier-dat… (Thanks @SaadMRP for editing it)
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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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So cool!
Following up on the crazy idea in our first ever blog, we tested an SRE diagnosis pipeline **entirely driven by Jev**, with no LLM agent. It passed 80/105 diagnoses (76.2%) across 21 fault scenarios with a median diagnosis time: **14.6 seconds**. 1/7
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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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I'm excited to share that SREGym.com has been accepted to NeurIPS 2026! 🎉 Huge thanks to my co-lead @Yiming_Su3, our co-authors @SaadMRP, @lilygn6, Yifan Tian, Hans-Arno Jacobsen, @yinfang_chen , @TianyinXu, and our 60+ contributors! We have benchmarks for agents that write code. We built one for what happens after you deploy it. Can AI resolve production issues? 🧵 [1/N]
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CC @tianyin_xu somehow I messed up my own advisors handle 🤦
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