Enterprise AI Analysis

The system tackles long-horizon problems by inherently decomposing tasks into a sequence of spatially grounded micro-tasks. The Tower of Hanoi puzzle, for instance, is mapped onto a 64x64 PixelGrid, with each move corresponding to a unique grid location. This mirrors hierarchical planning where complex tasks are broken into smaller, verifiable subtasks, reducing error propagation and improving controllability. The curriculum ensures that agents master foundational tasks (central grid moves) before progressing to more complex, peripheral ones. This structured approach, combined with adaptive curriculum progression, addresses the limitations of static decomposition found in prior work like MAKER, ensuring robust execution over extended horizons.

Curriculum learning is central to our system's adaptive reasoning. Instead of a fixed difficulty progression, a Thompson Sampling curriculum manager dynamically selects training zones based on agent competence and Negative Log-Likelihood (NLL) signals. NLL acts as a measure of confidence, ensuring that agents are not just succeeding but doing so with calibrated certainty. This prevents premature advancement into harder regions when agents are internally uncertain, a common failure mode in LLM-based systems. By prioritizing regions where agents demonstrate genuine understanding and confidence, the curriculum fosters more stable convergence, improved robustness against noisy labels, and better generalization, leading to a confidence-aware learning trajectory for long-horizon tasks.