Papers with addressing complex
Self-Evolving Multi-Agent Systems via Textual Backpropagation (2026.findings-acl)
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Xiaowen Ma, Yunpu Ma, Chenyang Lin, Sikuan Yan, Jinhe Bi, Zixuan Cao, Yijun Tian, Volker Tresp, Hinrich Schuetze
| Challenge: | Large Language Models (LLMs) have proven effective for addressing complex, high-dimensional tasks, but current approaches rely on static, manually engineered multi-agent configurations. |
| Approach: | They propose a framework that conceptualizes multi-agent collaboration as a layered neural network architecture. |
| Outcome: | The proposed framework surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements. |
Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems (2025.acl-long)
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| Challenge: | Existing frameworks prioritize structural architectures and role assignments but neglect granular mechanics of agent collaboration. |
| Approach: | They propose to use centralized governance, instructor-led participation, ordered interaction patterns to optimize task accuracy and computational efficiency. |
| Outcome: | The proposed model improves task accuracy and computational efficiency under two context-dependent scenarios. |