GRAVITY: Architecture-Agnostic Structured Anchoring for Long-Horizon Conversational Memory

• URL: http://arxiv.org/abs/2605.01688v1
• Date: Sun, 03 May 2026 02:55:46 GMT
• Title: GRAVITY: Architecture-Agnostic Structured Anchoring for Long-Horizon Conversational Memory
• Abstract summary: We introduce GRAVITY, a plug-and-play structured memory module.<n>It extracts three knowledge representations from raw conversational utterances.<n>It injects these representations into the host system's prompt as structured anchoring contexts.
• Score: 38.10762218232756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-horizon conversational agents rely on memory systems with increasingly sophisticated retrieval mechanisms. However, retrieved fragments are typically fed to the language model as unstructured text, lacking the relational, temporal, and thematic structures essential for complex reasoning. To bridge this reasoning gap, we introduce GRAVITY (\textbf{G}eneration-time \textbf{R}elational \textbf{A}nchoring \textbf{V}ia \textbf{I}njected \textbf{T}opological Memor\textbf{Y}), a plug-and-play structured memory module. GRAVITY extracts three complementary knowledge representations from raw conversational utterances: entity profiles grounded in relational graphs, temporal event tuples linked into causal traces, and cross-session topic summaries. At generation time, it injects these representations into the host system's prompt as structured anchoring contexts. This approach effectively synthesizes scattered evidence into a coherent, query-relevant context without requiring any architectural modifications to the host model. Extensive evaluations across five diverse memory systems on the LongMemEval and LoCoMo benchmarks demonstrate the efficacy of our approach. On average, GRAVITY improves LLM-judge accuracy by 7.5--10.1%. Gains are inversely correlated with baseline strength: the weakest host improves by 12.2% while the strongest still gains 3.8--5.7%. These findings establish structured context anchoring as a broadly effective, architecture-agnostic augmentation paradigm for long-horizon conversational memory.

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)
• Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs [55.78708003681562]
  Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation.<n>Existing RAG approaches typically retrieve and unstructured text fragments as context.<n>We propose Tri-RAG, a structured triplet-based retrieval framework that improves retrieval efficiency through reasoning-aligned context construction.
  arXiv  Detail & Related papers  (2026-04-14T11:36:29Z)
• FlexStructRAG: Flexible Structure-Aware Multi-Granular Relational Retrieval for RAG [25.45341852479756]
  RAG systems depend on how external knowledge is segmented, structured, and retrieved.<n>We propose FlexStructRAG, a structure-aware RAG framework that supports multi-granular, query-adaptive retrieval.<n>Experiments show that FlexStructRAG improves semantic evaluation over strong RAG baselines.
  arXiv  Detail & Related papers  (2026-02-01T09:19:03Z)
• E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory [4.8183840404266185]
  E-mem is a framework shifting from Memory Preprocessing to Episodic Context Reconstruction.<n>E-mem achieves over 54% F1, surpassing the state-of-the-art GAM by 7.75%, while reducing token cost by over 70%.
  arXiv  Detail & Related papers  (2026-01-29T13:42:42Z)
• Structured Episodic Event Memory [37.643537420763344]
  We propose Structured Episodic Event Memory (SEEM), a hierarchical framework that synergizes a graph memory layer for relational facts with a dynamic episodic memory layer for narrative progression.<n> Experimental results on the LoCoMo and LongMemEval benchmarks demonstrate that SEEM significantly outperforms baselines.
  arXiv  Detail & Related papers  (2026-01-10T03:17:25Z)
• Disco-RAG: Discourse-Aware Retrieval-Augmented Generation [81.53888908988756]
  We propose Disco-RAG, a discourse-aware framework that injects discourse signals into the generation process.<n>Our method constructs intra-chunk discourse trees to capture local hierarchies and builds inter-chunk rhetorical graphs to model cross-passage coherence.<n>Experiments on question answering and long-document summarization benchmarks show the efficacy of our approach.
  arXiv  Detail & Related papers  (2026-01-07T20:32:50Z)
• Beyond Fact Retrieval: Episodic Memory for RAG with Generative Semantic Workspaces [5.110309385104824]
  Large Language Models (LLMs) face fundamental challenges in long-context reasoning.<n>Current solutions fail to build the space-time-anchored narrative representations required for tracking entities through episodic events.<n>We propose the textbfGenerative Semantic Workspace (GSW), a neuro-inspired generative memory framework.
  arXiv  Detail & Related papers  (2025-11-10T19:59:34Z)
• 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)
• Semantic Anchoring in Agentic Memory: Leveraging Linguistic Structures for Persistent Conversational Context [0.0]
  We propose a hybrid agentic memory architecture that enriches vector-based storage with explicit linguistic cues to improve recall of nuanced, context-rich exchanges.<n>Experiments on adapted long-term dialogue datasets show that semantic anchoring improves factual recall and discourse coherence by up to 18% over strong RAG baselines.
  arXiv  Detail & Related papers  (2025-08-18T05:14:48Z)
• Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation [69.45495166424642]
  We develop a robust and discriminative QA benchmark to measure temporal, causal, and character consistency understanding in narrative documents.<n>We then introduce Entity-Event RAG (E2RAG), a dual-graph framework that keeps separate entity and event subgraphs linked by a bipartite mapping.<n>Across ChronoQA, our approach outperforms state-of-the-art unstructured and KG-based RAG baselines, with notable gains on causal and character consistency queries.
  arXiv  Detail & Related papers  (2025-06-06T10:07:21Z)
• Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering [51.7493726399073]
  We present a discourse-aware hierarchical framework to enhance long document question answering.<n>The framework involves three key innovations: specialized discourse parsing for lengthy documents, LLM-based enhancement of discourse relation nodes, and structure-guided hierarchical retrieval.
  arXiv  Detail & Related papers  (2025-05-26T14:45:12Z)

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.