arXiv AI
TAGGRAPH: Tag-Augmented Graphs for Graph Retrieval of Agent Persistent Histories
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arXiv AI
Aug 13
EvoGraph-Mem: Failure-Aware Editable Graph Memory for Long-Term Language Agents
arXiv:2608. 11248v1 Announce Type: new Abstract: Long-term memory is essential for language agents operating across extended interactions and evolving tasks.
arXiv AI
Jul 31
A Graph-Native Bitemporal Memory Store for Conversational AI Agents
arXiv:2607. 26520v1 Announce Type: cross Abstract: Conversational AI agents commonly lack persistent memory across sessions.
arXiv AI
Sep 1
Selective Forgetting: A Graph-Based Memory Framework for Long-Term LLM Agents
arXiv:2608.28978v1 Announce Type: new Abstract: Knowledge graphs have been proposed as a structured alternative to flat retrieval-augmented generation for long-term agent memory, on the assumption th...
arXiv Computation and Language
Aug 31
Entity-Memory Graph Retrieval Improves Evidence Coverage in Long-Conversation Question Answering
Entity-Memory graph retrieval preserves dialogue turns as verbatim memory nodes, links repeated mentions via shared entities, and connects adjacent memories with chronological edges. During retrieval, the system gates through entities, fuses semantics, and performs one‑hop chronological recovery before dense backfill, allowing it to keep neighboring memories that dense cosine ranking might miss. On 1,986 questions from ten LoCoMo conversations, this graph retrieval method increases official evidence recall at top‑k 25 from 79.7468 % to 84.4842 %, with the advantage extending from top‑k 5 to 50, though it does not improve overall final‑answer F1.
arXiv AI
Jun 30
Mandol: An Agglomerative Agent Memory System for Long-Term Conversations
arXiv:2606. 29778v1 Announce Type: cross Abstract: Long-term conversational agents need to remember and query cross-session, multi-typed information with complex correlations.
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.