Edison Scientific, Inc

We are excited to announce a strategic collaboration with @EdisonSci to employ the Kosmos AI platform across Incyte's discovery and development lifecycle. Read more. bit.ly/4dvd37o

We live in a golden age of biology. So why are people still dying from disease? Because discovery and development move slower than they should. Today, we’re partnering with Incyte to change that. Kosmos is now the first agent that can compress months of drug development into weeks, from the earliest stages of scientific discovery through to FDA approval. @Incyte will be the first company to deploy it across their pipeline. Work that used to take a team of scientists months now happens in weeks. Patients can't wait, and neither can we.

Read more: edisonscientific.com/articles/accel…

This week at GTC, we announced our partnership with @nvidia. Edison is committed to build at the frontier of scientific reasoning. We partner with NVIDIA across the AI stack in training and benchmarking capabilities to accelerate scientific research. Our latest partnered release is on BixBench-Hypothesis, a new benchmark focused on analytical judgment under ambiguity, or the ability to pursue a hypothesis with an open-ended goal. See our release blog post in the comments. More from our CEO @SGRodriques on why this partnership matters:

We've been testing Nemotron 3 Super for a bit prerelease and it's very competitive with top open models while also being EXTREMELY fast. Congrats to the team! I'm very optimistic about the seriousness of NVIDIA nemotron team and this is great news for open models

Nemotron 3 super is out today. It is blazingly fast, has long context, and is very competitive. Nvidia has a very serious team working on Nemotron, and we have a deep partnership with Nvidia training specialist agents on their models. We're excited to continue. x.com/ArtificialAnly…

For fun, we let Edison and Gemini 3 Pro simulate trades by predicting drug approval events. After 3 months of events, Edison made 6 trades and earned 26% returns, while Gemini made 16 trades and lost 43%. Next time with real money...

Edison agents are now available by email. You can email ask@askedison.com to start an agent job, and you can include it on threads and add attachments. Try forwarding it an email with your latest data and say "analyze this!"

See the link to the paper: nature.com/articles/s4158… And the analysis: edisonscientific.com/articles/repro…

Edison Analysis Agent can access publicly hosted RNA-seq datasets, perform transcriptomic analyses, and draw accurate biological conclusions. Here we use the Edison Analysis Agent to reanalyze a recently released dataset on Autism Spectrum Disorder (ASD). Identifying how risk-associated mutations mechanistically alter brain development and contribute to ASD remains a significant challenge. In their recent Nature paper, Gordon et al. tackled this problem using patient iPSC–derived cortical organoids and time-resolved bulk RNA-seq, revealing genotype-linked expression similarities of ASD forms and their strongest divergence from normal organoids at the earliest stages. In our new post, the Edison Analysis Agent autonomously reanalyzed the public dataset and reproduced both key results, recovering genotype-driven clustering of ASD forms and independently recapitulating their “early divergence, later convergence” trajectory across development.

Here's a detailed write-up of how we used Nemotron-Parse to extract images/tables/equations from research papers while making PaperQA3. Thanks to the NVIDIA team! They were really helpful for getting us from prototype to scale of 100s of pages/s. developer.nvidia.com/case-studies/s…

Read more here: edisonscientific.com/articles/ediso…

Not ready for a full Kosmos run? Start with a mini-discovery in Edison Analysis. We asked Edison Analysis to explore the Genomics of Drug Sensitivity in Cancer (GDSC) public dataset. In a single run, it retrieved and explored the data for sensitivity patterns, formulated a BCL2-dependency hypothesis in hematological cancers, and tested the hypothesis using independent CCLE expression data.

After surprisingly long amount of work, our literature agent can finally read figures and tables from >150M papers and patents. We've open-sourced our readers from the many experiments and written up some of our findings.

Today we're releasing PaperQA3, our latest literature search agent, which has the best multimodal capabilities among deep research agents today by a large margin. Edison Literature and Kosmos can now read figures and tables from over 150M full-text research papers, as well as patents, clinical trials, and more. Additionally, we’ve updated the underlying algorithm of our literature agent (which is open source at github.com/Future-House/p…) to improve its ability to answer more complex questions. The result of these improvements show Edison Literature as one of the strongest deep research agents across benchmarks, beating out current-day frontier deep research agents. The PaperQA3-backed version of Edison Literature is available today on our platform and API as literature-20260216, as is Edison Literature High (literature-20260216-high), a high-effort variant for best performance.

Read more here: edisonscientific.com/articles/ediso… Try our platform here: platform.edisonscientific.com

Today we are announcing a major update to Edison Literature, our scientific deep research agent. It represents a significant advancement over our previous PaperQA2 algorithm: it enables deeper reasoning over 100s of scientific documents, can retrieve information from figures and tables, and is state-of-the art among other deep research systems on scientific tasks.

This morning at 11am PST, our very own James Braza (@semajazarb) will be doing a livestream with NVIDIA showing how we do document parsing in the latest version of open source PaperQA! Take a look: youtube.com/watch?v=HRPklL…

See the link to the paper: nature.com/articles/s4159… And the analysis: edisonscientific.com/articles/bioph…

Real-time single molecule sensing with nanopores has the potential to transform several fields including precision medicine and diagnostics, drug discovery, protein characterization, and chemical manufacturing. In a recent pioneering work advancing the abilities of nanopores, Zhang et al. (2024) introduced a custom-engineered nanopore capable of trapping single amino acids and demonstrated that molecular volume is the primary determinant of ionic current blockade. Here, we show that the Edison Analysis Agent can autonomously reproduce this key biophysical finding using the authors’ published nanopore time-series recordings—recapitulating the quantitative relationship between amino acid volume and blockade amplitude. Starting from raw current traces, the agent identified open-pore baselines and blockade levels, computed amino acid–specific blockade current amplitudes, integrated canonical biophysical properties of amino acids from the literature, and performed correlation and regression analyses to independently identify molecular volume as the dominant predictor of current blockade as individual amino acids pass through the nanopore.