Caleb Capoccia

We are releasing OptimusKG, a modern multimodal knowledge graph. Learn more at: optimuskg.ai Graph AI has enabled advances across science and medicine (e.g., see our review at doi.org/10.1146/annurev-biod…). Integrating KGs with LLMs improves generalization, planning, reasoning, and zero-shot learning, and can enable RL reward modeling for reasoning models. Graph AI depends on high-quality underlying graphs. For example, in 2023, our lab (@payal_chandak, @KexinHuang5) introduced PrimeKG, which has now been downloaded 116K+ times and cited 450+ times. However, current biomedical KGs suffer from several limitations: ⏱️ Out-of-date ⚠️ Lack of standardized schema 🛠️ Unavailable or irreproducible construction code 📊 No metadata about nodes and edges 🤷‍♂️ No provenance information 📋 No quality checks We introduce OptimusKG, a modern open-source multimodal biomedical knowledge graph that integrates molecular, anatomical, clinical, and environmental data. OptimusKG has reproducible, modular, FAIR-compliant code; an ontology-grounded, interoperable schema; rich metadata; and tracked provenance. ✅ 190K nodes across 10 types ✅ 21.8M edges across 26 types (5.5x greater than PrimeKG) ✅ 110.3M properties across 150 keys ✅ 65 source datasets and 18 ontologies A frontier multimodal research agent (@EdisonSci) assessed whether data points in OptimusKG are supported by evidence from the scientific literature, identifying support for 70.0% of sampled true edges; by contrast, 83.4% of sampled false edges had no supporting evidence. OptimusKG also captures experimental data that may precede synthesis in the scientific literature. To learn more about OptimusKG and download the graph: 🌐 Website and documentation: optimuskg.ai 👨‍💻 Code: github.com/mims-harvard/Opti… 📎 Pre-print: arxiv.org/abs/2604.27269 🐍 Python client: pypi.org/project/optimus (run pip install optimuskg) 📩 Dataset: doi.org/10.7910/DVN/IYNGEV We are excited for the community to use and build upon this resource to enable innovation in graph AI. More below! 👇🏽 1/6

I'm visiting exceptional startups in New York this week on the Harvard Startup Trek organized by my brilliant friends @kelschen99, @jonathanlei0, and @evatuecke. Today, we... – Visited Embedded Intelligence (ei.inc) and @Medal_TV, thank you @PimDeWitte and team for an exciting demo. – Visited @bubble, thanks @joshhaas and @emmanuel_s for insights into your founding journey, 7 years of bootstrapping, and challenges of company building (over great chocolate chip coconut cookies too 🍪)! – Visited Moment (withmoment.com) for a deep dive on fixed income trading. Fixed income is the world's most traded asset class, more than 3x size of global equities market, yet surprisingly illiquid. That may soon change, thanks to Moment (who recently partnered with @apexfintech). – Brief pit stop at the @haizelabs office and reunion with @leonardtang_ and @steveshenli. – Visited @withgraphite, thank you to @gregmfoster and @TomasReimers for sharing your origin story building a company amidst a global pandemic, pivoting based on customer feedback, and finding PMF. – Ending the day with a @generalcatalyst x @SunoMusic AI social. Thank you @jonnyjzhang, @Nim_Ravid1, @apjacob03, @KonstDaskalakis, @max_rimpel and team for hosting us at the GC NYC office and for throwing an awesome party. All this and we've only been in NYC for < 24 hours. If you're around, please reach out – would love to meet up while we're here. Everyone else on the Harvard Startup Trek: @calebcapoccia, @evatuecke, @grx_xce, @InakiArango, @jonathanlei0, @jujipotle, @kelschen99, @Nim_Ravid1, @kineticyang, Peggy Yin, Prin Pulkes