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.

RSSM is a single layer version of our model.

Last week, we discussed Multi-class SVM loss; specifically, the hinge loss and squared hinge loss functions. In reality, these values would not be randomly generated -- they would instead be the output of your scoring function f. Let's exponentiate the output of the scoring function, yielding our unnormalized probabilities: Figure 2: Exponentiating the output values from the scoring function gives us our unnormalized probabilities. Figure 4: Taking the negative log of the probability for the correct ground-truth class yields the final loss for the data point. To examine some actual probabilities, let's loop over a few randomly sampled training examples and examine the output probabilities returned by the classifier: Note: I'm randomly sampling from the training data rather than the testing data to demonstrate that there should be a noticeably large gap in between the probabilities for each class label.