AriadneMem: Threading the Maze of Lifelong Memory for LLM Agents

arXiv:2603.03290v1 Announce Type: new
Abstract: 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 emph{entropy-aware gating} to filter noise and low-information message before LLM extraction and applies emph{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 emph{algorithmic bridge discovery} to reconstruct missing logical paths between retrieved facts, followed by emph{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. The code is available at https://github.com/LLM-VLM-GSL/AriadneMem.