arXiv Computation and Language By Weixian Waylon Li, Jiaxin Zhang, Xianan Jim Yang, Tiejun Ma, Yiwen Guo

Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory

Read the original on arXiv Computation and Language →

The paper introduces RoMem, a temporal knowledge graph module that treats time as continuous phase rotation rather than discrete labels. RoMem uses a Semantic Speed Gate to assign volatility scores to relations, allowing evolving facts to rotate quickly while persistent facts remain stable, thereby preventing the need for deletion or costly LLM calls. The method achieves state‑of‑the‑art performance on ICEWS05‑15 and improves temporal reasoning in agentic memory benchmarks such as MultiTQ, LoCoMo, and FinTMMBench.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv Computation and Language
Sep 1

Agent Zero Memory: Provenance-Aware Long-Term Memory for LLM Agents

Agent Zero Memory is a provenance‑aware long‑term memory system for large language model agents that distills user interactions into three parallel memory structures: an episodic timeline, an associative entity‑event knowledge graph, and a semantic, citation‑locked hierarchical documentary memory. Retrieval is performed via an intent gate, source router, and concurrent searches across the three systems, producing integrated, cited answers that exclude fabrication and require evidence the reader has opened. The system achieves state‑of‑the‑art performance on LongMemEval (95.60%) and LoCoMo (93.60%) while offering a favorable accuracy‑cost‑latency trade‑off across multiple backbone LLMs.

By Ming Wu, Pengyuan Zhu
arXiv AI
Jul 21

Accurate and Efficient Long-Term Memory for LLM Agents

arXiv:2607. 16211v1 Announce Type: new Abstract: LLM agents augmented with persistent memory can recall past interactions, but existing systems suffer from two limitations: flat, unstructured storage loses relational context needed for multi-hop and temporal reasoning, and reliance on expensive LLM-based classification makes them impractical for latency-sensitive deployment.

By Zicheng Zhao, Xinyang Guo, Luyao Lv, Menghan Wang, Ming Li, Shuaicheng Li