駕馭記憶：記憶智能體中記憶基質的整體性評估

摘要

記憶正逐漸成為長時程大型語言模型（LLM）代理的核心基礎設施，然而現有的評估對於在不同運行模式下應使用何種記憶載體（即記憶表徵與儲存的底層媒介）所提供的指引仍然有限。我們提出了一個針對記憶增強型代理之記憶載體的受控測試框架評估，涵蓋密集與稀疏索引、文字記錄、結構化儲存、階層化儲存、基於精煉的記憶、參數更新，以及與激活相容的上下文機制。我們跨越三個骨幹模型與四套基準測試套件（涵蓋以使用者為中心的問答與以代理為中心的決策），在統一的測試框架下量測了26項效能與效率指標。我們的結果顯示，沒有任何單一載體能持續佔據主導地位：廣泛的檢索有益於長上下文的事實問答，但過度的檢索可能透過將注意力從對行動至關重要的情境中移開，而損害序列決策。可擴展性引入了另一個路由維度，因為在中等歷史長度下表現良好的載體，在更長的時程中可能變得昂貴或脆弱。這些發現促使載體路由成為自適應代理記憶系統的必要組成部分，並為設計高效、可靠且具運行模式感知的LLM代理長期記憶提供了實證指引。程式碼將於論文接受後提供。

English

Memory is becoming core infrastructure for long-horizon LLM agents, yet existing evaluations offer limited guidance on which memory substrate, namely the underlying medium in which memory is represented and stored, should be used under different operating regimes. We present a controlled harness evaluation of memory substrates for memory-augmented agents, covering dense and sparse indices, text records, structural stores, hierarchical stores, refinement-based memories, parametric updates, and activation-compatible context mechanisms. Across three backbone models and four benchmark suites spanning user-centric question answering and agent-centric decision-making, we instrument 26 performance and efficiency metrics under a unified harness. Our results show that no single substrate consistently dominates: broad retrieval benefits long-context factual QA, while excessive retrieval can harm sequential decision-making by shifting attention away from action-critical context. Scalability introduces a further routing axis, as substrates that perform well at moderate history lengths can become costly or brittle at longer horizons. These findings motivate substrate routing as a necessary component of adaptive agent memory systems and provide empirical guidance for designing efficient, reliable, and regime-aware long-term memory for LLM agents. Code will be made available upon acceptance.