Semantic anchor checkpointing is the process of placing recurrent-state checkpoints specifically at special-token boundaries that denote semantic units, such as thinking blocks, tool calls and returns, and conversation turn transitions. Because agent harnesses systematically truncate or modify conversation history at these precise block boundaries—such as stripping past thinking segments or eliding older tool observations—checkpoints placed at arbitrary token offsets are routinely invalidated. By aligning checkpoints with semantic anchors, the unedited prefix cleanly terminates at a valid checkpoint, enabling full-attention layers to reuse their KV cache up to the boundary while recurrent layers resume directly from the saved anchor state, requiring re-prefill only for the new suffix.