PGMem introduces a persona‑memory graph that tightly couples user persona signals with the events that support or revise them, using typed provenance and evidence edges. This design addresses gaps in existing memory systems where personas are stored as flat, detached profiles. Experiments on three benchmarks show that PGMem outperforms various baseline memory approaches, especially as dialogue context length increases.
arXiv:2608.22310v1 Announce Type: new Abstract: Long-term memory is crucial for personalized responses and long-horizon agent interactions. Existing methods often rely on LLMs to compress or rewrite...
Large Language Model (LLM) agents are evolving from single-session tools toward long-term personal assistants that must adapt to individual users across tasks, contexts, and interactions. This shift m...
arXiv:2609.08599v1 Announce Type: new Abstract: Large Language Model (LLM) agents are evolving from single-session tools toward long-term personal assistants that must adapt to individual users acros...
The paper introduces CogMem, a cognitive memory architecture for long‑term dialogue agents that uses a PEC$^2$F graph schema to separate claims, facts, and events. Dialogue turns are incrementally turned into provenance‑aware graph records, consolidated into higher‑level facts, and reconciled into temporally scoped claim views when conflicting updates arise. Retrieval is performed by a rule‑based controller that applies deterministic graph operators—anchoring, traversal, intersection, and evidence grounding—guided by LLM intent parsing, achieving strong performance on multi‑hop, temporal, and knowledge‑update tasks in LoCoMo and LongMemEval benchmarks.
An agent that interacts with users over long periods must recall facts, preferences, events, and changes from a continuously growing interaction history. Existing memory systems often compress interac...
arXiv:2607. 05794v1 Announce Type: new Abstract: Long-term user memory is essential for personalized conversational agents, yet many memory systems still expose memory through passive retrieval interfaces, making the model a consumer of pre-selected evidence.
The paper introduces Dual-Path Parametric Memory (DPPM), a method for long‑term personalization of language models that leverages interaction history without requiring it in the inference context. DPPM uses two complementary paths: an Evidence path that pools historical representations to retain earlier evidence, and a Delta path that sequentially updates an associative state to capture changes. By fusing these outputs into history‑conditioned LoRA adapters, DPPM achieves superior performance on PersonaMem‑v2 (54.22%) and PrefEval (86.79%) compared to existing baselines.
arXiv:2608. 03463v1 Announce Type: new Abstract: Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history.
The paper introduces QGMem, a memory framework for conversational agents that organizes dialogue history into event-indexed atomic units and consolidates related units into dynamic memory traces. When a query is posed, QGMem retrieves complementary candidate memories, reranks them to form a compact working memory, and represents this working memory as a local graph to expose relational dependencies. Experiments on six benchmarks demonstrate consistent improvements in retrieval, multi‑hop evidence composition, conflict resolution, and ultra‑long dialogue reasoning while keeping context size and inference cost moderate.
EnSIMem is an entity‑structured long‑term memory architecture designed for agents that interact with users over extended periods. It organizes interactions into theme‑coherent episodes and creates dialogue‑grounded index entries of the form [entity][entity type][property:value], preserving source turns, temporal data, and multimodal fields. During online interaction, the agent decomposes requests into evidence requirements, performs entity‑property lookup, and retrieves the necessary evidence to generate responses directly from preserved source material rather than lossy summaries.
arXiv:2607. 20482v1 Announce Type: new Abstract: Recent advances in large language models have enabled web agents to autonomously execute complex tasks.
The paper introduces HiPS, a hierarchical strategy co‑evolution framework for memory‑augmented agents that separates memory management into a globally shared foundation and a user‑specific adaptive tier. HiPS uses a Universal Strategy to capture shared principles from cross‑persona trajectories, Persona Delta Distillation to create tailored rules for users deviating from general patterns, and Cross‑Level Rule Flow to dynamically adjust the boundary between global and personal rules. Experiments show that this approach consistently outperforms existing memory‑augmented baselines.