Curriculum Guided Massive Multi Agent System Solving For Robust Long Horizon Tasks

• URL: http://arxiv.org/abs/2512.08545v1
• Date: Tue, 09 Dec 2025 12:40:39 GMT
• Title: Curriculum Guided Massive Multi Agent System Solving For Robust Long Horizon Tasks
• Abstract summary: This work introduces a hierarchical multi-agent architecture that distributes reasoning across a 64*64 grid of lightweight agents.<n>A spatial curriculum progressively expands the operational region of the grid, ensuring that agents master easier central tasks before tackling harder peripheral ones.<n>Results demonstrate improved stability, reduced oracle usage, and stronger long-range reasoning from distributed agent cooperation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models and multi-agent systems have shown promise in decomposing complex tasks, yet they struggle with long-horizon reasoning tasks and escalating computation cost. This work introduces a hierarchical multi-agent architecture that distributes reasoning across a 64*64 grid of lightweight agents, supported by a selective oracle. A spatial curriculum progressively expands the operational region of the grid, ensuring that agents master easier central tasks before tackling harder peripheral ones. To improve reliability, the system integrates Negative Log-Likelihood as a measure of confidence, allowing the curriculum to prioritize regions where agents are both accurate and well calibrated. A Thompson Sampling curriculum manager adaptively chooses training zones based on competence and NLL-driven reward signals. We evaluate the approach on a spatially grounded Tower of Hanoi benchmark, which mirrors the long-horizon structure of many robotic manipulation and planning tasks. Results demonstrate improved stability, reduced oracle usage, and stronger long-range reasoning from distributed agent cooperation.

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