LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning
- URL: http://arxiv.org/abs/2511.01448v1
- Date: Mon, 03 Nov 2025 11:02:40 GMT
- Title: LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning
- Authors: Zhengjun Huang, Zhoujin Tian, Qintian Guo, Fangyuan Zhang, Yingli Zhou, Di Jiang, Xiaofang Zhou,
- Abstract summary: 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.
- Score: 15.189701702660821
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Model (LLM) agents exhibit remarkable conversational and reasoning capabilities but remain constrained by limited context windows and the lack of persistent memory. Recent efforts address these limitations via external memory architectures, often employing graph-based representations, yet most adopt flat, entangled structures that intertwine semantics with topology, leading to redundant representations, unstructured retrieval, and degraded efficiency and accuracy. To resolve these issues, we propose LiCoMemory, an end-to-end agentic memory framework for real-time updating and retrieval, which introduces CogniGraph, a lightweight hierarchical graph that utilizes entities and relations as semantic indexing layers, and employs temporal and hierarchy-aware search with integrated reranking for adaptive and coherent knowledge retrieval. 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. Our official code and data are available at https://github.com/EverM0re/LiCoMemory.
Related papers
- HyMem: Hybrid Memory Architecture with Dynamic Retrieval Scheduling [7.24393498822329]
HyMem is a hybrid memory architecture that enables dynamic on-demand scheduling through multi-granular memory representations.<n>We show that HyMem achieves strong performance on both the LOCOMO and LongMemEval benchmarks, outperforming full-context while reducing computational cost by 92.6%.
arXiv Detail & Related papers (2026-02-15T00:06:19Z) - Graph-based Agent Memory: Taxonomy, Techniques, and Applications [63.70340159016138]
Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks.<n>Among diverse paradigms, graph stands out as a powerful structure for agent memory due to the intrinsic capabilities to model relational dependencies.<n>This survey presents a comprehensive review of agent memory from the graph-based perspective.
arXiv Detail & Related papers (2026-02-05T13:49:05Z) - 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) - SwiftMem: Fast Agentic Memory via Query-aware Indexing [45.27116353623848]
We propose SwiftMem, a query-aware agentic memory system that achieves sub-linear retrieval through specialized indexing over temporal and semantic dimensions.<n>Our temporal index enables logarithmic-time range queries for time-sensitive retrieval, while the semantic DAG-Tag index maps queries to relevant topics through hierarchical tag structures.<n> Experiments on LoCoMo and LongMemEval benchmarks demonstrate that SwiftMem achieves 47$times$ faster search compared to state-of-the-art baselines.
arXiv Detail & Related papers (2026-01-13T02:51:04Z) - 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) - Implicit Graph, Explicit Retrieval: Towards Efficient and Interpretable Long-horizon Memory for Large Language Models [26.694294747866834]
We propose LatentGraphMem, a memory framework that combines implicit graph memory with explicit subgraph retrieval.<n>LatentGraphMem stores a graph-structured memory in latent space for stability and efficiency.<n> Experiments on long-horizon benchmarks across multiple model scales show that LatentGraphMem consistently outperforms representative explicit-graph and latent-memory baselines.
arXiv Detail & Related papers (2026-01-06T21:10:10Z) - Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents [57.38404718635204]
Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows.<n>Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components.<n>We propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy.
arXiv Detail & Related papers (2026-01-05T08:24:16Z) - Evaluating Long-Term Memory for Long-Context Question Answering [100.1267054069757]
We present a systematic evaluation of memory-augmented methods using LoCoMo, a benchmark of synthetic long-context dialogues annotated for question-answering tasks.<n>Our findings show that memory-augmented approaches reduce token usage by over 90% while maintaining competitive accuracy.
arXiv Detail & Related papers (2025-10-27T18:03:50Z) - LightMem: Lightweight and Efficient Memory-Augmented Generation [72.21680105265824]
We introduce a new memory system called LightMem, which strikes a balance between the performance and efficiency of memory systems.<n>Inspired by the Atkinson-Shiffrin model of human memory, LightMem organizes memory into three complementary stages.<n>Experiments on LongMemEval with GPT and Qwen backbones show that LightMem outperforms strong baselines in accuracy (up to 10.9% gains) while reducing token usage by up to 117x.
arXiv Detail & Related papers (2025-10-21T17:58:17Z) - ArcMemo: Abstract Reasoning Composition with Lifelong LLM Memory [21.4675019810992]
Concept-level memory is reusable, modular abstractions distilled from solution traces and stored in natural language.<n>We evaluate on ARC-AGI, a benchmark that stresses compositional generalization and abstract reasoning.<n>We find abstract concepts to be the most consistent memory design, outscoring the baseline at all tested inference compute scales.
arXiv Detail & Related papers (2025-09-04T17:54:19Z) - Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents [19.04968632268433]
We propose a hierarchical memory architecture for Large Language Model Agents (LLM Agents)<n>Each memory vector is embedded with a positional index encoding pointing to its semantically related sub-memories in the next layer.<n>During the reasoning phase, an index-based routing mechanism enables efficient, layer-by-layer retrieval without performing exhaustive similarity computations.
arXiv Detail & Related papers (2025-07-23T12:45:44Z) - MemOS: A Memory OS for AI System [116.87568350346537]
Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI)<n>Existing models mainly rely on static parameters and short-lived contextual states, limiting their ability to track user preferences or update knowledge over extended periods.<n>MemOS is a memory operating system that treats memory as a manageable system resource.
arXiv Detail & Related papers (2025-07-04T17:21:46Z) - From Single to Multi-Granularity: Toward Long-Term Memory Association and Selection of Conversational Agents [79.87304940020256]
Large Language Models (LLMs) have been widely adopted in conversational agents.<n>MemGAS is a framework that enhances memory consolidation by constructing multi-granularity association, adaptive selection, and retrieval.<n> Experiments on four long-term memory benchmarks demonstrate that MemGAS outperforms state-of-the-art methods on both question answer and retrieval tasks.
arXiv Detail & Related papers (2025-05-26T06:13:07Z) - Learning to Ignore: Long Document Coreference with Bounded Memory Neural
Networks [65.3963282551994]
We argue that keeping all entities in memory is unnecessary, and we propose a memory-augmented neural network that tracks only a small bounded number of entities at a time.
We show that (a) the model remains competitive with models with high memory and computational requirements on OntoNotes and LitBank, and (b) the model learns an efficient memory management strategy easily outperforming a rule-based strategy.
arXiv Detail & Related papers (2020-10-06T15:16:31Z)
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.