In the rapidly evolving world of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities in reasoning, creativity, and task execution. However, a subtle yet critical flaw plagues most AI agents: context collapse. As interactions lengthen or environments become volatile, interpretive layers begin to drift. Temporal anchors loosen, relevance signals distort, and causal chains fracture. What starts as coherent reasoning often ends in fragmented, incoherent ou