Related papers: From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs

Related papers

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)
Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents [20.461904943047468]
SegTreeMem represents conversation history as a temporally ordered Segment Tree over utterances.<n>For retrieval, SegTreeMem propagates relevance scores through the tree to combine local semantic matching with hierarchical temporal context.<n>Additional temporal-order permutation analysis shows that the performance gain depends on preserving temporal order during memory construction.
arXiv Detail & Related papers (2026-06-03T07:42:15Z)
H-Mem: A Novel Memory Mechanism for Evolving and Retrieving Agent Memory via a Hybrid Structure [18.614170579610555]
H-Mem is a novel memory mechanism that can effectively model the evolution of agent memory over a long period of time.<n>It offers an effective memory retrieval approach by exploiting the hybrid structure of the tree and graph structures.
arXiv Detail & Related papers (2026-05-15T07:46:38Z)
MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search [59.98962867649009]
We propose MemSearch-o1, an agentic search framework built on reasoning-aligned memory growth and retracing.<n>MemSearch-o1 grows fine-grained memory fragments from memory seed tokens from queries, then retraces and deeply refines the memory via a contribution function, and finally reorganizes a globally connected memory path.
arXiv Detail & Related papers (2026-04-19T05:35:06Z)
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)
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)
AdmTree: Compressing Lengthy Context with Adaptive Semantic Trees [66.39371821756649]
We propose AdmTree, a novel framework for adaptive, hierarchical context compression.<n>AdmTree segments input based on information density, utilizing gist tokens to summarize variable-length segments as the leaves of a semantic binary tree.<n>By preserving fine-grained details alongside global semantic coherence, mitigating positional bias, and dynamically adapting to content, AdmTree robustly retains the semantic information of long contexts.
arXiv Detail & Related papers (2025-12-04T08:04:19Z)
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)
CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension [55.29309306566238]
Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents.<n>This challenge raises the imperative of a cohesive memory module, which can elevate vanilla LLMs into autonomous reading agents.<n>We draw inspiration from Jean Piaget's Constructivist Theory, illuminating three traits of the agentic memory -- structured schemata, flexible assimilation, and dynamic accommodation.
arXiv Detail & Related papers (2025-10-07T02:16:30Z)
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)
Structured Memory Mechanisms for Stable Context Representation in Large Language Models [16.929937978584917]
The model integrates explicit memory units, gated writing mechanisms, and attention-based reading modules.<n>A forgetting function is introduced to enable dynamic updates of memory content.<n>The model achieves clear advantages in text generation consistency, stability in multi-turn question answering, and accuracy in cross-context reasoning.
arXiv Detail & Related papers (2025-05-28T22:49:04Z)
DISRetrieval: Harnessing Discourse Structure for Long Document Retrieval [51.89673002051528]
DISRetrieval is a novel hierarchical retrieval framework that leverages linguistic discourse structure to enhance long document understanding.<n>Our studies confirm that discourse structure significantly enhances retrieval effectiveness across different document lengths and query types.
arXiv Detail & Related papers (2025-05-26T14:45:12Z)
Towards Multi-Granularity Memory Association and Selection for Long-Term Conversational Agents [73.77930932005354]
We propose MemGAS, a framework that enhances memory consolidation by constructing multi-granularity association, adaptive selection, and retrieval.<n>MemGAS is based on multi-granularity memory units and employs Gaussian Mixture Models to cluster and associate new memories with historical ones.<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)
Quantifying Memory Utilization with Effective State-Size [73.52115209375343]
We develop a measure of textitmemory utilization' This metric is tailored to the fundamental class of systems with textitinput-invariant and textitinput-varying linear operators
arXiv Detail & Related papers (2025-04-28T08:12:30Z)
Cognitive Memory in Large Language Models [8.059261857307881]
This paper examines memory mechanisms in Large Language Models (LLMs), emphasizing their importance for context-rich responses, reduced hallucinations, and improved efficiency. It categorizes memory into sensory, short-term, and long-term, with sensory memory corresponding to input prompts, short-term memory processing immediate context, and long-term memory implemented via external databases or structures.
arXiv Detail & Related papers (2025-04-03T09:58:19Z)
Toward Multi-Session Personalized Conversation: A Large-Scale Dataset and Hierarchical Tree Framework for Implicit Reasoning [30.54506564763053]
We introduce ImplexConv, a large-scale long-term dataset with 2,500 examples, each containing approximately 100 conversation sessions. We also propose TaciTree, a novel hierarchical tree framework that structures conversation history into multiple levels of summarization.
arXiv Detail & Related papers (2025-03-10T07:59:41Z)
ReTreever: Tree-based Coarse-to-Fine Representations for Retrieval [64.44265315244579]
We propose a tree-based method for organizing and representing reference documents at various granular levels. Our method, called ReTreever, jointly learns a routing function per internal node of a binary tree such that query and reference documents are assigned to similar tree branches. Our evaluations show that ReTreever generally preserves full representation accuracy.
arXiv Detail & Related papers (2025-02-11T21:35:13Z)
On Memory Construction and Retrieval for Personalized Conversational Agents [69.46887405020186]
We propose SeCom, a method that constructs the memory bank at segment level by introducing a conversation segmentation model. Experimental results show that SeCom exhibits a significant performance advantage over baselines on long-term conversation benchmarks LOCOMO and Long-MT-Bench+.
arXiv Detail & Related papers (2025-02-08T14:28:36Z)
Integrating Hierarchical Semantic into Iterative Generation Model for Entailment Tree Explanation [7.5496857647335585]
We propose an architecture of integrating the Hierarchical Semantics of sentences under the framework of Controller-Generator (HiSCG) to explain answers. The proposed method achieves comparable performance on all three settings of the EntailmentBank dataset.
arXiv Detail & Related papers (2024-09-26T11:46:58Z)
Enhancing Long-Term Memory using Hierarchical Aggregate Tree for Retrieval Augmented Generation [1.4665304971699265]
HAT encapsulates information from children nodes, enabling broad coverage with depth control. experiments show HAT improves dialog coherence and summary quality over baseline contexts.
arXiv Detail & Related papers (2024-06-10T09:29:08Z)
Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations [39.05338079159942]
This study introduces a novel framework, COmpressive Memory-Enhanced Dialogue sYstems (COMEDY), which eschews traditional retrieval modules and memory databases. Central to COMEDY is the concept of compressive memory, which intergrates session-specific summaries, user-bot dynamics, and past events into a concise memory format.
arXiv Detail & Related papers (2024-02-19T09:19:50Z)
Topology-aware Embedding Memory for Continual Learning on Expanding Networks [63.35819388164267]
We present a framework to tackle the memory explosion problem using memory replay techniques. PDGNNs with Topology-aware Embedding Memory (TEM) significantly outperform state-of-the-art techniques.
arXiv Detail & Related papers (2024-01-24T03:03:17Z)
Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading [63.93888816206071]
We introduce MemWalker, a method that processes the long context into a tree of summary nodes. Upon receiving a query, the model navigates this tree in search of relevant information, and responds once it gathers sufficient information. We show that, beyond effective reading, MemWalker enhances explainability by highlighting the reasoning steps as it interactively reads the text; pinpointing the relevant text segments related to the query.
arXiv Detail & Related papers (2023-10-08T06:18:14Z)
A Framework for Inference Inspired by Human Memory Mechanisms [9.408704431898279]
We propose a PMI framework that consists of perception, memory and inference components. The memory module comprises working and long-term memory, with the latter endowed with a higher-order structure to retain extensive and complex relational knowledge and experience. We apply our PMI to improve prevailing Transformers and CNN models on question-answering tasks like bAbI-20k and Sort-of-CLEVR datasets.
arXiv Detail & Related papers (2023-10-01T08:12:55Z)
Conversational Semantic Parsing using Dynamic Context Graphs [68.72121830563906]
We consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types. We focus on models which are capable of interactively mapping user utterances into executable logical forms.
arXiv Detail & Related papers (2023-05-04T16:04:41Z)
Enhancing Large Language Model with Self-Controlled Memory Framework [56.38025154501917]
Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information. We propose the Self-Controlled Memory (SCM) framework to enhance the ability of LLMs to maintain long-term memory and recall relevant information.
arXiv Detail & Related papers (2023-04-26T07:25:31Z)
Eigen Memory Tree [27.33148786536804]
This work introduces the Eigen Memory Tree (EMT), a novel online memory model for sequential learning scenarios. We demonstrate that EMT outperforms existing online memory approaches, and provide a hybridized EMT-parametric algorithm that enjoys drastically improved performance. Our findings are validated using 206 datasets from the OpenML repository in both bounded and infinite memory budget situations.
arXiv Detail & Related papers (2022-10-25T14:57:41Z)
Pin the Memory: Learning to Generalize Semantic Segmentation [68.367763672095]
We present a novel memory-guided domain generalization method for semantic segmentation based on meta-learning framework. Our method abstracts the conceptual knowledge of semantic classes into categorical memory which is constant beyond the domains.
arXiv Detail & Related papers (2022-04-07T17:34:01Z)
Memory-Based Semantic Parsing [79.48882899104997]
We present a memory-based model for context-dependent semantic parsing. We learn a context memory controller that manages the memory by maintaining the cumulative meaning of sequential user utterances.
arXiv Detail & Related papers (2021-09-07T16:15:13Z)

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