Compressing long multi-turn conversations requires deciding not only how to compress context, but which signal should rank memory candidates under a fixed budget. We study a sparse keep/compress/drop (KCD) codec and compare geometric, semantic, and hybrid scoring signals across conversational memory regimes. On a stress-test benchmark designed to expose support-turn failures, a geometry-scored codec substantially outperforms a semantic signal swap inside the same codec. On MSC (1,000 conversations), we show that geometry-based compression methods substantially outperform LongLLMLingua on geometric fidelity (ΔL2 ≈ +130) while LongLLMLingua modestly improves answer NLL—a geometry-behavior divergence confirming that behavioral metrics alone are insufficient to evaluate memory compression. Among KCD signal variants on MSC (1,000 conversations, Qwen2.5-1.5B), query-conditioned geometry achieves the best combination of geometric fidelity and behavioral quality simultaneously: at budget 0.50 it reduces logit L2 by 109.4 units versus semantic KCD (p<0.001, conversation-level) while also achieving best answer NLL across all budgets. On LongMemEval-S (100 full-length conversations), all KCD variants significantly improve behavioral quality over uniform compression. A Llama-3.2-3B cross-model check shows that the geometry signal advantage is model-family-dependent: semantic compression dominates geometry compression on behavior across all budgets for Llama-3B (p≤0.01, conversation-level). The paper’s central claim is that geometry-behavior divergence is real and consequential, and that query-conditioned geometry is the signal that best resolves it on Qwen2.5 models.