Applied research lab curating data solutions to accelerate foundation model development.
Joined February 2025
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Congratulations to @nvidia on the release of Nemotron 3 Ultra!
Ultra now leads GDPval-AA by a good margin among US open source models. @AfterQuery is proud to have provided the GDPval training data used to hill climb the benchmark.
Can AI agents run a business as conditions change?
Introducing StoreBench, a high-fidelity RL environment where agents manage a simulated online apparel store, with safeguards against reward hacking.
StoreBench includes 12 evaluation tasks spanning 30–365 simulated days. Across the 11 tasks lasting 30–45 days, all seven models score lower on average than human participants and smart-triage, a rule-based program that prioritizes urgent work.
Late restocking, unresolved refunds, and decisions based on outdated information show that sustained operations remain challenging for AI agents.
Check out the comments to read more.
GitHub: github.com/AfterQuery/storeb…
Paper: arxiv.org/abs/2610.10942
Blog: afterquery.com/blog/storeben…
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AfterQuery retweeted
Congrats to @cognition on SWE-2 Flash!
Exciting to see an increasing number of companies train models around the work they know best.
Strong open foundations like @thinkymachines' Inkling-Small enable teams to shape intelligence around their own needs.
Excited to have one of our post-training runs showcased by @thinkymachines.
Using Tinker, we post-trained gpt-oss-20b with SFT, then RL on our terminal agent data. Its Terminal-Bench 2.0 pass rate went from 3.1% to 17.0%.
@AfterQuery datasets are validated with Tinker against held-out benchmarks before they ship.
If you're building frontier models and want data with measured impact, reach out at research@afterquery.com.
AfterQuery retweeted
Can collecting robot data be as easy as 𝗽𝗿𝗲𝘀𝘀𝗶𝗻𝗴 𝗿𝗲𝗰𝗼𝗿𝗱?
Introducing UMI-Live, a 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 bimanual UMI we made that takes you from handheld demonstrations all the way to a trained robot policy.
It's built to be easy to use. Each gripper has its own camera, LiDAR, compute, and battery, so there are no base stations to mount and nothing to calibrate in the scene. You press one button and both hands start recording.
While you keep collecting, our pipeline processes finished episodes in parallel and turns them into LeRobot datasets, automatically uploaded on Huggingface. VLAs fine-tuned mostly on this data succeed in 𝟭𝟬/𝟭𝟬 (π0.5) and 𝟴/𝟭𝟬 (MolmoAct2) trials on a real bimanual task.
We're open-sourcing everything, from the hardware designs and build guides to the code and ~40 hours of data 🧵
We improved models on professional work tasks using NVIDIA AI’s xToken, NeMo Gym, and NeMo RL for cross tokenizer distillation.
Read the full blog below.
We used @NVIDIAAI's xToken recipe along with NeMo Gym and NeMo RL open post-training libraries to improve models on professional work tasks through cross tokenizer distillation.
Held-out GDP.pdf strict pass@1 (avg 5) in NeMo Gym:
- Nemotron 3.5 Super VL: 6.67% to 11.83%
- Qwen3.5-9B: 4.40% to 8.20%
Read the full blog below.
AfterQuery retweeted
Huge congrats to the @GoogleDeepMind team on Gemini 4 Carbon!
Impressive to see performance climb across many core capabilities.
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!
Congratulations to @Recursive_SI on the release of Flint!
Flint runs locally on your laptop and competes with models that require 10x the compute.
@AfterQuery is proud to support the @Recursive_SI team.
Today, we release our first model, Flint-40B-A2B, a fast local coding model built with our automated research system. huggingface.co/RecursiveSI/f…
1. Strong performance. 54.9% on Terminal-Bench 2.1, 89.7% on SWE-bench Pro v2, and 74.2% on SWE-bench Verified.
2. It is trained from scratch (tokenizer->Pretrain->Midtrain->SFT->RL).
3. Fast in your local laptop. Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark.
4. We build the complete model training pipeline in 4 month!
Frontier models nearly run through the public internet for pre-training alone. The knowledge they need next is locked inside businesses.
Today we're launching @AfterQuery Data Partnerships.
If you're a business owner who is curious, or hesitant, about licensing your data, we would love to hear from you.
Learn more in the replies.
Read the full blog: afterquery.com/blog/announci…
AfterQuery Data Partnerships: afterquery.com/data-partners…
This past week at GTC Berlin, @AfterQuery was one of the companies highlighted in the Nemotron partner ecosystem during @JensenHuang's keynote.
We're proud to be an official @NVIDIAAI post-training partner and look forward to supporting more enterprises across their entire post-training strategies!
Read the full blog below.
Full blog: afterquery.com/blog/post-tra…
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Congrats to @spectorb @amspector100 @aidanmantine and the @flappyairplanes team on the $1B Series A!
Excited to see what their work on GPU systems and learning algorithms unlocks next ✈️
AfterQuery retweeted
@AfterQuery is partnering with @NVIDIA to help enterprises post-train Nemotron 3.5 Super VL.
The future of enterprise agents is open. 💚
@AfterQuery just moved an open model past the cost-accuracy frontier in finance.
We post-trained @NVIDIAAI Nemotron 3.5 Super VL on expert credit tasks with @hebbia.
Perfect solved: 47% to 74%. That beats Sol 6, and lands within 3 points of Astra 6, at about 1/7 the cost.
Link to the full case study in replies.
Learn more: afterquery.com/blog/post-tra…
@AfterQuery’s throwing a sftw happy hour with @NVIDIAAI tonight at 6:30pm.
reply for an invite.
AfterQuery retweeted
@AfterQuery’s throwing a sftw happy hour with @NVIDIAAI tonight at 6:30pm.
reply for an invite.