Advancing intelligence.
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Proximal builds infrastructure that enables AI to improve from real-world experience.
In the past year, we have grown to more than $200M in annualized revenue helping frontier labs and enterprises improve coding agents. Now, we are expanding beyond software engineering.
Recent progress in mathematics demonstrates what happens when models are trained in domains with massive amounts of public data and easy verifiability, making it easy to find weaknesses, generate training tasks, and iterate quickly.
We build infrastructure that enables this feedback loop in other domains: we are excited about a world in which AI systems design targeted drugs, accelerate chip design, and rewrite legacy software that still runs hospitals, power grids, governments, and other critical infrastructure.
Since starting, we have assembled a small team of researchers and engineers and built world-class infrastructure for post-training and synthetic data research to overcome the challenges of scaling manual data curation.
Our team comes from Cursor, Google DeepMind, Meta Superintelligence, Prime Intellect, Citadel, and Jane Street. More than half are former founders.
We’re fortunate to be backed by investors who share that vision, including @generalcatalyst who led our $15M Seed Round at a $300M valuation, as well as @chemistry, @svangel, @dvlamoen and individuals like @LiamFedus, @kevinweil, and @bernhardsson
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If you are interested in:
- Post-Training
- Data Research
- Infra at tremendous scale
or just generally want to know more about what we do @ProximalHQ, please reach out, I would love to chat :)
Excited to share i’ve joined @ProximalHQ here in SF! I think that there is a ton of interesting open problems related to data and post training at large. There is no better team to work with than the one we have and I am extremely excited to share our work :)
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Many cybersecurity evals use differential execution to test whether agents can reproduce known software vulnerabilities
Without added constraints, this creates fairness issues: Our evaluation shows that CyberGym is effectively saturated on a verified task subset
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For more reliable cybersecurity evals, most robust graders are required:
SEC-Bench Pro achieves high precision/recall on vuln-repro tasks with LLM-as-judge, and ExploitGym additionally uses a Capture-the-Flag approach for clear certainty
We consider these benchmarks high quality and recommend using them for cybersecurity evaluations
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Gemini 4 Argon scores 55.0% on FrontierSWE
The model is close to Fable 5.1 and only outperformed by Opus 5.5 and GPT-6 Astra
Introducing Gemini 4 Argon – our new frontier model.
It’s built for complex workflows across coding, enterprise knowledge work, and cybersecurity defense – rolling out today to a set of trusted testers through our Fairwind Program.
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Proximal retweeted
The models that will accelerate drug discovery, chip design, and energy are limited by the data they learn from. We led @ProximalHQ’s $15M seed as they build the data engine for the next generation of frontier AI and treat training data with the same rigor as model architecture.
Watch @quentinclark in conversation with @calvinchen.
Chapters
00:00 — Meet Proximal
03:45 — A Rainy Afternoon in South Park
05:19 — From Sneaker Bots to a First Exit
08:14 — Why Suffering Equals Growth
10:13 — The Path to Proximal
11:57 — How Models Learn
15:17 — Training Data as a Research Problem
23:24 — Building the Team in India
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Proximal builds infrastructure that enables AI to improve from real-world experience.
In the past year, we have grown to more than $200M in annualized revenue helping frontier labs and enterprises improve coding agents. Now, we are expanding beyond software engineering.
Recent progress in mathematics demonstrates what happens when models are trained in domains with massive amounts of public data and easy verifiability, making it easy to find weaknesses, generate training tasks, and iterate quickly.
We build infrastructure that enables this feedback loop in other domains: we are excited about a world in which AI systems design targeted drugs, accelerate chip design, and rewrite legacy software that still runs hospitals, power grids, governments, and other critical infrastructure.
Since starting, we have assembled a small team of researchers and engineers and built world-class infrastructure for post-training and synthetic data research to overcome the challenges of scaling manual data curation.
Our team comes from Cursor, Google DeepMind, Meta Superintelligence, Prime Intellect, Citadel, and Jane Street. More than half are former founders.
We’re fortunate to be backed by investors who share that vision, including @generalcatalyst who led our $15M Seed Round at a $300M valuation, as well as @chemistry, @svangel, @dvlamoen and individuals like @LiamFedus, @kevinweil, and @bernhardsson
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We’d like to thank everyone who has backed us and helped us get to where we are today:
@generalcatalyst, @svangel, @dvlamoen, @chemistry (@kshenster), @chrisfhoward, @Liquid2V, @FirstMarkCap (@ROWGHANI / @JdotJdotF), @Mascobot, @silasalberti, @LiamFedus, @kevinweil, @bernhardsson, @theodormarcu, @mobav0, @khoomeik, @aryaman2020, @pk_iv, @josephofiowa, @0interestrates , @calixo888, @ekaurghar, @forwarddeploy, @ericzakariasson, @danielxberrios, @johnlqian, @djstrouse
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Read more in our announcement:
proximal.ai/blog/new-frontie…
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Claude Opus 5.5 ranks #2 on FrontierSWE
The model achieves a score of 62.3%, closely behind GPT-6 Astra (65.5%) and clearly surpassing Fable 5.1 (56.3%) and Opus 5 (52.0%)
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We see large improvements in tasks that require vision capabilities - Opus strongly improves on TORCS Racing Bot, Flight-Sim Renderer in OpenGL and Fitness Recap Video in Remotion.
For more information on Opus 5.5 on FrontierSWE v2, check out the model card
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Learn more about FrontierSWE here
frontierswe.com/
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We partnered with @FireworksAI_HQ to bring FrontierSWE v2 to the Specialized Intelligence Index
Evals are essential for improving and safely deploying AI. We're excited to support the initiative!
Today we're launching the Specialized Intelligence Index (SII): one destination for real-work benchmarks across industries, built by the teams that use them every day.
Hear from Fireworks co-founder @the_bunny_chen on the importance of specialized benchmarks:
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