AriadneMem: Threading the Maze of Lifelong Memory for LLM Agents

Long-horizon LLM agents require memory systems that remain accurate under fixed context budgets. However, existing systems struggle with two persistent challenges in long-term dialogue: (i) $\textbf{disconnected evidence}$, where multi-hop answers require linking facts distributed across time, and (ii) $\textbf{state updates}$, where evolving information (e.g., schedule changes) creates conflicts with older static logs. We propose $AriadneMem$, a structured memory system that addresses these failure modes via a decoupled two-phase pipeline. In the $\textbf{offline construction phase}$, AriadneMem, employs entropy-aware gating to filter noise and low-information message before LLM extraction and applies conflict-aware coarsening to merge static duplicates while preserving state transitions as temporal edges. In the $\textbf{online reasoning phase}$, rather than relying on expensive iterative planning, AriadneMem executes algorithmic bridge discovery to reconstruct missing logical paths between retrieved facts, followed by single-call topology-aware synthesis. On LoCoMo experiments with GPT-4o, AriadneMem improves $\textbf{Multi-Hop F1 by 15.2\%}$ and $\textbf{Average F1 by 9.0\%}$ over strong baselines. Crucially, by offloading reasoning to the graph layer, AriadneMem reduces $\textbf{total runtime by 77.8\%}$ using only $\textbf{497}$ context tokens.