Robots that read intent: logic-consistency bias in attention cuts contradictions by a third
A neuro-symbolic graph transformer feeds a human reasoning graph into self-attention as learnable topological biases and adds a differentiable first-order logic loss over four intention levels, raising logic consistency by 3.26 percentage points to 96.67% on the synthesized NTU-Hierarchy dataset.