基於檔案系統的LLM智慧體記憶：組織、演化與永續性

摘要

已部署的LLM代理日益將其長期記憶保存為檔案系統：一個由Markdown檔案組成的目錄樹，代理本身透過通用檔案工具來讀取、寫入與重新組織這些檔案。然而，研究在很大程度上忽略了這種媒介：先前的系統設計了特製的記憶表徵，並研究在這些表徵上的檢索，使得此預設方法的兩項運作假設未經檢驗：其一，代理能否在記憶不斷累積、發生衝突與過時的情況下，持續維持一個不斷成長的儲存庫的組織性；其二，這種組織是否值得付出代價。我們提出了首個針對LLM代理基於檔案系統記憶的系統性探索。我們將此設定形式化為圍繞單一記憶檔案系統的三個角色：管理代理整合並組織進入的內容，搜尋代理以引用的來源回答查詢，執行代理提供任務軌跡並將其蒸餾為技能，從而在單一儲存庫中統一陳述性記憶與技能。在長對話基準測試與具身任務中，我們變異了記憶形狀（代理組織的階層結構、逐字傾印、區塊檢索）、串流規模、工具框架（沙盒shell、記憶工具風格的函式、多樣化的搜尋工具），以及管理代理和搜尋代理的強度，在記憶成長的過程中追蹤答案品質、成本與儲存庫健康度。組織所能穩定買到的是搜尋經濟性：當資料量大時，有組織的儲存庫約可將檢索成本減半。然而，當今的代理未能兌現此預設方法的承諾：在我們的成長研究中，除了最強的管理代理之外，所有情況下的組織性都會逐漸侵蝕，而且我們所測量的代理中，沒有任何一個能將組織本身轉化為更好的答案。此外，模型並非影響儲存庫形狀的唯一槓桿：僅更換工具集對儲存庫形狀的改變程度，與更換模型一樣顯著。本研究將檔案系統的預設做法從一個假設轉變為代理記憶的設計空間。

English

Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generic file tools. Yet research has largely passed over this medium: prior systems design bespoke memory representations and study retrieval over them, leaving the default's two working assumptions untested: that an agent can keep a growing store organized as memories accumulate, conflict, and go stale, and that this organization pays. We present the first systematic exploration of filesystem-based memory for LLM agents. We formalize the setting as three roles around one memory filesystem: a management agent integrates and organizes incoming content, a search agent answers queries with cited sources, and an execution agent supplies task trajectories that are distilled into skills, unifying declarative memory and skills in a single store. Across long-conversation benchmarks and embodied tasks, we vary memory shape (agent-organized hierarchy, verbatim dump, chunk retrieval), stream scale, tool harness (sandboxed shell, memory-tool-style functions, varied search tooling), and the strengths of the management and search agents, tracking answer quality, cost, and store health as memory grows. What organization reliably buys is search economy: organized stores roughly halve retrieval cost where material is large. Today's agents, however, fall short of the default's promise: in our growth study, organization erodes for all but the strongest management agent, and no agent we measure converts organization itself into better answers. And the model is not the only lever over a store's shape: changing the tool set alone reshapes the store as strongly as swapping the model. The study turns the filesystem default from an assumption into a design space for agent memory.