TrajWiki: Source-Grounded Memory Trajectories for Long-Horizon Dialogue Agents

• URL: http://arxiv.org/abs/2608.00967v1
• Date: Sun, 02 Aug 2026 03:36:03 GMT
• Title: TrajWiki: Source-Grounded Memory Trajectories for Long-Horizon Dialogue Agents
• Abstract summary: We propose TrajWiki, a trajectory-based memory framework for long-horizon conversational agents.<n>Instead of treating memory as static entries, TrajWiki represents each memory as a source-grounded evolution trajectory.<n>TrajWiki further introduces Memory Wiki, a persistent intermediate layer that incrementally compiles dialogue history into structured and interlinked wiki pages.
• Score: 17.027152676146592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model agents have shown strong capabilities in generating coherent and contextually appropriate responses, yet robust long-horizon dialogue remains limited by the lack of external memory that is traceable, updatable, and diagnostically transparent. Existing memory-augmented agents often store memories as isolated records or overwritable states, making it difficult to preserve how information originates, evolves, conflicts, or becomes obsolete over time. We propose TrajWiki, a trajectory-based memory framework for long-horizon conversational agents. Instead of treating memory as static entries, TrajWiki represents each memory as a source-grounded evolution trajectory, maintained through immutable episodic snapshots and claim-level operations such as ADD, REVISE, and DEPRECATE. To reduce fragmentation and retrieval cost, TrajWiki further introduces Memory Wiki, a persistent intermediate layer that incrementally compiles dialogue history into structured and interlinked wiki pages capturing salient entities, events, quantities, topics, and conflicts. At inference time, queries are routed hierarchically from relevant wiki pages to linked memory trajectories, then to corresponding snapshots and source messages for evidence-grounded answer synthesis. Experiments on LoCoMo and MedMT show that TrajWiki improves long-horizon dialogue performance across both open-source and closed-source LLM backbones, while providing greater interpretability and diagnostic visibility into memory evolution, retrieval failures, and answer generation.

Related papers

• CueMem: Cue-Guided Context Reconstruction for Long-Term Conversational Memory [38.76918926805768]
  CueMem is a cue-guided framework that treats extracted memory records as retrieval cues.<n>It reconstructs query-relevant dialogue context from their source turns.<n>Experiments on LoCoMo and LongMemEval show that CueMem consistently outperforms representative long-term memory baselines.
  arXiv  Detail & Related papers  (2026-09-11T02:27:22Z)
• REAL: A Reasoning-Enhanced Graph Framework for Long-Term Memory Management of LLMs [17.526686616588794]
  Large Language Models (LLMs) are increasingly expected to interact with users over long time horizons.<n>LLMs cannot retain all past interactions, making long-term memory management essential for storing, updating, and retrieving historical information.<n> REAL constructs long-term conversational memory as a temporal and confidence-aware directed property graph.
  arXiv  Detail & Related papers  (2026-06-09T10:53:10Z)
• Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents [51.30250860677378]
  We propose MRAgent, a framework that combines an associative memory graph with an active reconstruction mechanism.<n>Running on a Cue-Tag-Content graph, our active reconstruction mechanism integrates reasoning directly into memory access.<n>Experiments on the LoCoMo benchmark and LongMemEval benchmark demonstrate significant improvements over strong baselines.
  arXiv  Detail & Related papers  (2026-06-04T11:29:46Z)
• Memory Shot for Long-Term Dialogue [27.934717347947352]
  We propose MemShot, which renders local contiguous dialogue spans into structured visual memory units.<n>MemShot achieves stable and competitive performance on both LoCoMo and LongMemEval.<n>Further analysis reveals that MemShot enhances the localization and utilization of historical evidence.
  arXiv  Detail & Related papers  (2026-05-30T01:59:52Z)
• SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent [51.274445160155864]
  Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and partial conclusions.<n>Existing approaches address this difficulty by truncating the interaction history, compressing it into shorter surrogates, or retrieving selected parts of it for reuse.<n>We propose State-Adaptive Memory, a standalone framework that consolidates ongoing interaction into compact memory cues while preserving raw trajectory pages for intent-driven recall.
  arXiv  Detail & Related papers  (2026-05-23T08:37:16Z)
• Rethinking How to Remember: Beyond Atomic Facts in Lifelong LLM Agent Memory [88.81430082035617]
  LLM agents require a memory system that can faithfully store, efficiently retrieve, and deeply reason over accumulated dialogue history.<n>We propose TriMem, which maintains three coexisting representation granularities, including raw dialogue segments anchored by source identifiers for storage fidelity.
  arXiv  Detail & Related papers  (2026-05-19T15:05:06Z)
• AMA: Adaptive Memory via Multi-Agent Collaboration [54.490349689939166]
  We propose Adaptive Memory via Multi-Agent Collaboration (AMA), a novel framework that leverages coordinated agents to manage memory across multiple granularities.<n>AMA significantly outperforms state-of-the-art baselines while reducing token consumption by approximately 80% compared to full-context methods.
  arXiv  Detail & Related papers  (2026-01-28T08:09:49Z)
• The AI Hippocampus: How Far are We From Human Memory? [77.04745635827278]
  Implicit memory refers to the knowledge embedded within the internal parameters of pre-trained transformers.<n>Explicit memory involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations.<n>Agentic memory introduces persistent, temporally extended memory structures within autonomous agents.
  arXiv  Detail & Related papers  (2026-01-14T03:24:08Z)
• Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning [55.251697395358285]
  Large language models (LLMs) are increasingly deployed as intelligent agents that reason, plan, and interact with their environments.<n>To effectively scale to long-horizon scenarios, a key capability for such agents is a memory mechanism that can retain, organize, and retrieve past experiences.<n>We propose CompassMem, an event-centric memory framework inspired by Event Theory.
  arXiv  Detail & Related papers  (2026-01-08T08:44:07Z)
• LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning [15.189701702660821]
  LiCoMemory is an end-to-end agentic memory framework for real-time updating and retrieval.<n>CoGraph is a lightweight hierarchical graph that utilizes entities and relations as semantic indexing layers.<n>Experiments on long-term dialogue benchmarks, LoCoMo and LongMemEval, show that LiCoMemory not only outperforms established baselines in temporal reasoning, multi-session consistency, and retrieval efficiency, but also notably reduces update latency.
  arXiv  Detail & Related papers  (2025-11-03T11:02:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.