arXiv cs.CLPaper
Multi-Step Tool-Calling over Korean Open Public APIs: A Benchmark and a Data-Synthesis Recipe
This is a sharp paper on a bottleneck: open-source models consistently fail on multi-step tool-calling, and there's been no good way to measure or improve that failure. EDGE's trick is elegant: build a dependency graph of how each API's output feeds another, keep only the verified links, and synthesize trajectories along those paths. A 9B model fine-tuned on EDGE data nearly matches a 27B baseline. If you're building agents that chain tools in real systems, this technique for generating training data is immediately useful. For non-agent builders, this is less relevant.