Koragraph Research
Our research.
The experiments behind our claims, released with the corpus and the oracles so anyone can re-run them against us.
Papers
2 papers94% of Frontier Retrieval Quality from a 494M-Parameter Model
Method summarisation for code knowledge graphs in 2.43 GPU-hours, and the point where more training data stops paying
A 494M-parameter model fine-tuned for 2.43 T4-hours, about $0.85 of commodity GPU time, writes the method descriptions a code knowledge graph is searched on at 93.8% of the frontier teacher’s retrieval quality, and beats a hosted model Koragraph itself was paying for.
93.8%
of frontier teacher retrieval quality, hit@8
$0.85
commodity GPU time to train the shipping model
91%
of the total gain from the first 2,500 examples
Read the paper →
Can Graph-Based Context Engineering Substitute for Model Scale?
Task Resolution and Cost for Cheap and Frontier Models on Repository-Level Code Tasks
A cheap model given graph-derived context solved 9 of 11 repository-level tasks at $0.027 per solved task. A frontier agentic system searching the same repositories with grep solved 9 as well, at $2.82.
2 → 9
cheap tier, tasks solved, grep → graph
9 → 9
frontier tier, tasks solved, grep → graph
105.9×
cost per solved task, frontier+grep vs cheap+graph
Read the paper →
What is next
A larger corpus, larger repositories, more languages. The task suite is built to grow, and independent replication is the reason it is public.