SAGE-nano

We introduce SAGE-nano, a compact reasoning model that achieves state-of-the-art performance on complex reasoning tasks, aided with what we call inverse reasoning. SAGE-nano utilizes bidirectional chain-of-thought processing to enhance reasoning capabilities in resource-constrained environments. Traditional language models process information unidirectionally, limiting their ability to refine and validate reasoning steps. SAGE-nano introduces a novel architectural approach that combines forward reasoning with backward verification, creating a bidirectional thought process that significantly improves accuracy while maintaining computational efficiency. Our inverse reasoning mechanism allows the model to work backward from potential conclusions to validate logical consistency, identifying and correcting errors in reasoning chains before producing final outputs. This dual-pathway approach enables SAGE-nano to achieve performance comparable to models 10x larger while using only 4 billion parameters.