A multi-agent coding paper cuts API cost 35% with cohesion-aware task partitioning
blaizedsouza · x · 2026-07-21
A paper titled “When Parallelism Pays Off: Cohesion-Aware Task Partitioning for Multi-Agent Coding” argues that multi-agent coding only helps when task boundaries respect dependency structure.
The authors model multi-agent orchestration as a graph-partitioning problem and propose Cohesion-aware Coder (Co-Coder). It builds dependency graphs from static analysis, isolates structural hub files, partitions the graph with community detection, and runs work through a dependency-aware scheduler.
On 28 real-world tasks from DevEval and CodeProjectEval, the approach:
- improves the Pareto frontier over sequential and file-based parallel baselines,
- lifts pass rate by up to 14.0%,
- achieves up to 2.10× wall-clock speedup,
- and cuts API cost by up to 35%.
The main takeaway is that parallel coding agents are not automatically better; they work best when orchestration is guided by code cohesion and dependency structure.
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