Current transformer architectures represent information as a flat landscape: all tokens exist on the same plane, connected only by similarity. Query (Q), Key (K), and Value (V) vectors enable sophisticated pattern matching but lack a dimension for encoding what matters more than what. This paper proposes S (Significance) as a fourth vector type that transforms the representational space into a topographic landscape—with peaks for high-importance entities and valleys for peripheral information—where attention flows through terrain shaped by learned hierarchical judgments about consequence, relevance, and structural criticality. S-vectors address systematic failures in identity stability, entity tracking, and reference continuity across domains including code generation (variable misbinding), long-form reasoning (character drift), multi-agent systems (action attribution), and academic writing (citation hallucinations). We formalize significance-weighted attention as $\text{Attention}_{Q,K,S}(Q, K, V, S) = \text{softmax}((QK^T/\sqrt{d_k}) + S) \times V$, where the S-matrix encodes both absolute importance (corpus-learned base significance) and relational structure (pairwise anti-drift constraints, task-specific modulation, hierarchical precedence). This architectural modification prevents hallucinations arising from Q/K misalignment along weak semantic axes by stabilizing load-bearing representations through persistent significance encoding. The shift from flat to topographic representation is not merely technical—it constitutes the difference between pattern matching and reasoning. Intelligence requires maintaining stable judgments about what matters more than what. S-vectors provide the missing architectural primitive for encoding, persisting, and applying those judgments. While S-vectors cannot be retrofitted into existing transformers, they represent a necessary evolutionary step toward systems capable of genuine understanding rather than statistical correlation.