Graph-Based Task Decomposition for Parallel Tool Execution in Orchestration Models

TL;DR: What if ToolOrchestra could solve parts of a problem at the same time using different tools—like a chef prepping many ingredients at once? We propose equipping ToolOrchestra with graph-based planning to enable parallel tool invocations for independent sub-tasks, and will test whether this boosts efficiency on multi-step benchmarks.

Research Question: Can explicit graph-based task decomposition and parallel tool execution further improve the efficiency and scalability of model orchestration?

Hypothesis: Integrating explicit dependency graphs and parallel tool calls will significantly reduce task completion time and resource usage on complex, multi-step agentic tasks.

Experiment Plan: - Extend the Orchestrator to generate task dependency graphs as in the GAP framework (Wu et al., 2025).

• Enable parallel tool execution where dependencies permit.
• Evaluate on multi-hop QA and tool-use benchmarks (e.g., MHQA, Seal-Tools), measuring latency, resource use, and final accuracy.
• Analyze where parallelization yields the highest gains.

References:

• Su, H. et al. (2025). ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration.
• Wu, J., Zhao, Q., Chen, Z., Qin, K., Zhao, Y., Wang, X., & Yao, Y. (2025). GAP: Graph-Based Agent Planning with Parallel Tool Use and Reinforcement Learning. arXiv.org.