# Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents

arXiv: https://arxiv.org/abs/2601.07468 · HTML: https://mlsanity.com/paper/2601.07468/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Agent memory systems, one of 16 directions in LLM agents and tool use.
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 18 of 18)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Zep: A Temporal Knowledge Graph Architecture for Agent Memory](https://mlsanity.com/paper/2501.13956/) (2025)
- [Memory in the Age of AI Agents](https://mlsanity.com/paper/2512.13564/) (2025)
- [MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents](https://mlsanity.com/paper/2506.15841/) (2025)
- [LightMem: Lightweight and Efficient Memory-Augmented Generation](https://mlsanity.com/paper/2510.18866/) (2025)
- [Memory OS of AI Agent](https://mlsanity.com/paper/2506.06326/) (2025)
- [OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation](https://mlsanity.com/paper/2505.23885/) (2025)
- [From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs](https://mlsanity.com/paper/2504.15965/) (2025)
- [Hello Again! LLM-powered Personalized Agent for Long-term Dialogue](https://mlsanity.com/paper/2406.05925/) (2024)
- [MemGen: Weaving Generative Latent Memory for Self-Evolving Agents](https://mlsanity.com/paper/2509.24704/) (2025)
- [In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents](https://mlsanity.com/paper/2503.08026/) (2025)
- [Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents](https://mlsanity.com/paper/2507.22925/) (2025)
- [Retrieval-Augmented Generation with Hierarchical Knowledge](https://mlsanity.com/paper/2503.10150/) (2025)
- [Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue](https://mlsanity.com/paper/2509.10852/) (2025)

## Built on by (most cited first, 14 of 14)

- [ExpSeek: Self-Triggered Experience Seeking for Web Agents](https://mlsanity.com/paper/2601.08605/) (2026)
- [GAM: Hierarchical Graph-based Agentic Memory for LLM Agents](https://mlsanity.com/paper/2604.12285/) (2026)
- [Beyond the Context Window: A Cost-Performance Analysis of Fact-Based Memory vs. Long-Context LLMs for Persistent Agents](https://mlsanity.com/paper/2603.04814/) (2026)
- [TOKI: A Bitemporal Operator Algebra for Contradiction Resolution in LLM-Agent Persistent Memory](https://mlsanity.com/paper/2606.06240/) (2026)
- [Opal: Private Memory for Personal AI](https://mlsanity.com/paper/2604.02522/) (2026)
- [Total Recall at What Cost? Benchmarking the Serving Cost of Agentic Memory Systems](https://mlsanity.com/paper/2608.11879/) (2026)
- [Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents](https://mlsanity.com/paper/2608.15008/) (2026)
- [Agents in the Large: Perception-Centered Architecture for Persistent Agents](https://mlsanity.com/paper/2608.30478/) (2026)
- [Memory Consolidation Flattens the Temporal Shape of User Facts](https://mlsanity.com/paper/2609.36457/) (2026)
- [From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning](https://mlsanity.com/paper/2605.23382/) (2026)
- [Language Has Two Parameters: Narrative-Induced Semantic Plasticity and Phase-Sensitive Interpretation](https://mlsanity.com/paper/2608.18041/) (2026)
- [AdaptArena: Evaluating Test-Time Personalization of Web Agents](https://mlsanity.com/paper/2609.36488/) (2026)
- [Personalized State-Transition-Aware Memory for Clinical Agents](https://mlsanity.com/paper/2609.38490/) (2026)
- [Stale, Misattributed, or Late: Where Personal Memory Fails Before Generation](https://mlsanity.com/paper/2610.10265/) (2026)

## Abstract (arXiv)

Memory enables Large Language Model (LLM) agents to perceive, store, and use information from past dialogues, which is essential for personalization. However, existing methods fail to properly model the temporal dimension of memory in two aspects: 1) Temporal inaccuracy: memories are organized by dialogue time rather than their actual occurrence time; 2) Temporal fragmentation: existing methods focus on point-wise memory, losing durative information that captures persistent states and evolving patterns. To address these limitations, we propose Temporal Semantic Memory (TSM), a memory framework that models semantic time for point-wise memory and supports the construction and utilization of durative memory. During memory construction, it first builds a semantic timeline rather than a dialogue one. Then, it consolidates temporally continuous and semantically related information into a durative memory. During memory utilization, it incorporates the query's temporal intent on the semantic timeline, enabling the retrieval of temporally appropriate durative memories and providing time-valid, duration-consistent context to support response generation. Experiments on LongMemEval and LoCoMo show that TSM consistently outperforms existing methods and achieves up to 12.2% absolute improvement in accuracy, demonstrating the effectiveness of the proposed method.