Rohan Paul

@rohanpaul_ai

🇨🇳 New paper from top Chinese labs brings AgentConductor, a new framework dynamically adjusts multi-agent connections to solve complex programming challenges while using fewer tokens. The big deal here is the shift from rigid workflows to fluid teamwork. Normal multi-agent systems use a fixed, hardcoded workflow for every single problem. If you have a team of 5 specialized AI agents, all five talk to each other in the exact same pattern whether they are printing a basic text line or solving a massive competitive programming challenge. This wastes huge amounts of computing power on simple tasks and fails on complex tasks that actually require a different structure. AgentConductor fixes this by acting like a smart human project manager. It looks at the problem, judges the difficulty, and creates a custom communication graph just for that specific task. Easy tasks get a small, cheap team. Hard tasks get a large, highly connected team. Even better, if the generated code fails to run, the manager reads the error message and actually rewrites the team workflow on the fly to try a new strategy. The big deal is that it drastically improves coding accuracy while cutting computing token costs by 68%, proving that AI teams need flexible, task-specific management rather than rigid, one-size-fits-all pipelines. ---- Paper Link – arxiv. org/abs/2602.17100 Paper Title: "AgentConductor: Topology Evolution for Multi-Agent Competition-Level Code Generation" ---