SPARKIT
Scientific rigorin a research agent
One API call. Hours of research, delivered in two minutes. Papers read, analyses run, claims cited.
30% off Pro for founding members
Lock in 30% off Pro for your full first year — use code FOUNDING30 at checkout.
Very accurate info, and I appreciate the fact that it cited very recent papers.
Where SPARKIT fits
Plug into the workflow you already have.
From a single API call inside a Jupyter notebook to a callback-driven pipeline that informs your next wet-lab experiment.
From RNA-seq hits to prioritized knockouts.
Run your standard differential-expression pipeline. Hand the top genes to SPARKIT for pathway annotation, literature precedent, and known co-regulators. Walk into the next lab meeting with a ranked, cited list of knockout candidates instead of a raw spreadsheet.
Research agent preview
See the agent in action.
Three real questions where direct Opus 4.8 and GPT-5.5 missed and SPARKIT landed the answer — same UI you'll use after signing up.
Answer
D
Reasoning
Based on Tomé et al. (2011, Human Molecular Genetics, doi:10.1093/hmg/ddr099), the only in vivo mammalian study that directly addresses LIG1 function in CTG instability in DM1: DM1 transgenic mice (DM300-328, >300 CTG) were crossed with mice carrying the 46BR LigI hypomorphic allele (~3-5% residual activity — functionally equivalent to a knockout, used because complete LIG1 knockout is embryonic lethal at the organismal level). The authors explicitly state: "Neither male transmissions nor somatic CTG instability was affected by the 46BR LigI" and "We observed no change in the levels of somatic instability between LigI mutant mice and wild-type mice." LIG1 deficiency specifically affects MATERNAL germline transmission (reducing expansions, increasing contractions) but has no effect on age-dependent somatic CTG instability in tissues. Therefore the answer is D (No impact).
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Benchmarks
How SPARKIT compares on public evaluations.
HLE-Gold
vs. frontier models
149 questions · biology / medicine + chemistry
The gold-standard subset of Humanity's Last Exam.
GAIA
vs. search APIs
127 questions
Requires real-world search and multi-step reasoning.
What's different
Research agents,
without the engineering
Multi-step research
Each call runs an agent that thinks, searches, reads, and reasons before answering. Multi-step work, not a single LLM turn.
Built for agent loops
Submit, walk away. Push results to your callback_url or poll for them, ~110s median end-to-end. Designed to be called from your own agent or backend, not a chat tab.
Measurably better
On HLE-Gold (Humanity's Last Exam, gold subset, n=149), SPARKIT scores 53.0% vs 34.9% for both direct GPT-5.5 and Claude Opus 4.8. The lift comes from the research process, not a bigger model.