A high-signal data ecosystem leveraging data from industry & academia to deliver post-training to frontier labs, neolabs, & enterprises building advanced models
San Francisco, CA
Joined May 2026
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We're scientists and coders — geeks and nerds.
We specialize in post-training AI models with SciCode, CritPt, DeepSWE, and Terminal-Bench-Science style training tasks.
If you're a researcher and want to see samples, DM us here or checkout ReasonCore.ai.
ReasonCore AI has a data partnership with @PlusAI_Inc where we transform their autonomous vehicle stills and videos for RL for frontier and world model training. Gain access to millions of training sets with expert reasoning with demonstrated headroom!
benchmarks.reasoncore.ai/spa…
ReasonCore AI published a benchmark called "The SciCode 500." It's tested against Grok 4.5, Ox Alpha, DeepSeek V4 Pro, and Muse Spark 1.1.
The benchmark has wide range of solvability by design. The 100% headroom problems are verified solved by an off benchmark SOTA model.
SciCode is a specialty of ReasonCore and we have 5,000+ in inventory. We are building inventory in CritPt as well. Inquire within!
reasoncore.ai/scicode500
Most AI benchmarks are saturating. CritPt is not.
CritPt ("critical point") asks whether frontier LLMs can do real physics research. 50+ active physicists wrote 71 research-scale challenges from their own unpublished work. These are problems you'd hand an incoming PhD student, with nothing to memorize. Answers are guess-resistant and machine-verifiable: numerical arrays, symbolic expressions, working Python functions. No LLM-as-judge handwaving.
At launch, the best model scored 5.7% on full challenges. Models handled isolated checkpoints far better than composed research. The gap is sustained, integrated reasoning. Under a year later, top models are near 30%. Fast progress, with 70% of the map still dark.
This is what good evaluation looks like: novel, contamination-resistant, aimed where models actually break. It's also our specialty at ReasonCore, where we build frontier scientific benchmarks like CritPt and SciCode.
Paper: arxiv.org/abs/2509.26574
If recall is the bottleneck for parametric factuality, what does that mean for SciCode?
See @ReasonCoreAI's Dr. Anshuman Lall's interpretation below.
👉 If you are an LLM researcher, contact us for samples.
💡 If you are a PhD researcher in STEM, contact us to help build AI for Science.
linkedin.com/posts/anshumanl…
With AI inference costs getting more scrutiny in companies (and your personal credit card bill!), here are tips on how best to use the 3 modes of Claude to save money and time.
