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MemInsight: Autonomous Memory Augmentation for LLM Agents

Rana Salama, Jason Cai, Michelle Yuan, Anna Currey, Monica Sunkara, Yi Zhang, Yassine Benajiba

arXiv:2503.21760v2cs.CL

TL;DR

LLM agents struggle to retrieve relevant information as long-term memory grows and becomes unstructured. MemInsight autonomously augments historical interactions with semantic attributes and uses them for retrieval, improving contextual performance across recommendation, question answering, and event summarization. The approach shows stronger retrieval and recommendation outcomes across the evaluated tasks, while remaining sensitive to annotation quality and the underlying attribute-generation model.

  • Problem

    Growing, noisy, and unstructured long-term memory makes relevant retrieval and cross-context knowledge integration difficult for LLM agents.

  • Method

    MemInsight autonomously generates semantic and contextual attributes for historical interactions and uses them to structure and retrieve memory.

  • Results

    MemInsight demonstrates effectiveness across conversational recommendation, question answering, and event summarization, including 10–11% higher partial persuasiveness with comprehensive and attribute-based retrieval.

  • Takeaways & Limitations

    Autonomous memory augmentation can support more contextually relevant retrieval and responses across multiple LLM-agent tasks.

  • Takeaways & Limitations

    MemInsight may generate abstract or generic annotations in ambiguous dialogues, and its performance depends on the underlying LLM used for attribute generation.

Abstract

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Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. A key capability is the integration of long-term memory capabilities, enabling these agents to draw upon historical interactions and knowledge. However, the growing memory size and need for semantic structuring pose significant challenges. In this work, we propose an autonomous memory augmentation approach, MemInsight, to enhance semantic data representation and retrieval mechanisms. By leveraging autonomous augmentation to historical interactions, LLM agents are shown to deliver more accurate and contextualized responses. We empirically validate the efficacy of our proposed approach in three task scenarios; conversational recommendation, question answering and event summarization. On the LLM-REDIAL dataset, MemInsight boosts persuasiveness of recommendations by up to 14%. Moreover, it outperforms a RAG baseline by 34% in recall for LoCoMo retrieval. Our empirical results show the potential of MemInsight to enhance the contextual performance of LLM agents across multiple tasks.

1 Introduction

MemInsight addresses the difficulty of retrieving useful information from growing, unstructured long-term memory by autonomously augmenting historical interactions. It uses structured representations and retrieval methods to support contextual performance across recommendation, question answering, and event summarization.

  • Growing and unstructured historical memory makes relevant retrieval increasingly difficult and can degrade contextual agent performance.Structured knowledge representation is presented as important for efficient retrieval and scalable long-term memory.
  • MemInsight autonomously identifies critical information and proposes attributes that enhance memory representations.The approach is described as analogous to attentional control and cognitive updating.
  • Generated augmentations encode relevant semantic and contextual information to help identify task-relevant memory components.These attributes support retrieval and agent adaptability.
  • The framework adapts memory representations while preserving context across extended conversations and tasks.
  • Its retrieval methods filter irrelevant memory while retaining key historical insights.
  • MemInsight is evaluated on conversational recommendation, question answering, and event summarization.

2 Related Work

Prior work structures historical interactions and develops retrieval methods to improve LLM-agent memory efficiency, adaptability, and generalization. Common approaches use summaries, temporal events, reasoning chains, semantic annotations, and similarity-based retrieval.

  • LLM-agent memory research stores and retrieves prior interactions to improve adaptability and generalization.
  • Memory is commonly structured as summaries, temporal events, or reasoning chains to reduce redundancy and highlight key information.
  • Some methods enrich raw dialogues with semantic annotations such as event sequences or reusable workflows.
  • Retrieval research targets efficiency when agents handle large-scale historical context.
  • Generative retrieval commonly encodes memory entries as dense vectors and retrieves top-k relevant documents through similarity search.Cosine similarity, dual-tower retrievers, and indexing tools such as FAISS are cited as common components.

3 Autonomous Memory Augmentation

MemInsight augments each interaction with structured attributes and retrieves contextually relevant memory through comprehensive or refined strategies. Its design combines attribute mining, annotation, prioritization, and retrieval.

  • MemInsight autonomously augments new interactions into an enriched memory set and comprises attribute mining, annotation, and memory retrieval modules.
  • Attribute Mining: Attribute mining extracts structured attributes guided by perspective, granularity, and annotation alignment.
  • Attribute Perspective: Entity-centric attributes describe referenced items, whereas conversation-centric attributes capture intent, preferences, sentiment, emotions, motivations, and choices.
  • Attribute Granularity: Turn-level augmentation captures nuanced content from individual turns, while session-level augmentation captures broader interaction patterns and user intent.
  • Annotation: The annotation module applies an LLM-based extraction function to produce attribute–value pairs and aggregate them into relevant memory.
  • Attribute Prioritization: Basic augmentation orders attributes arbitrarily, whereas priority augmentation sorts attribute–value pairs by relevance so the most relevant attributes are processed first.
  • Memory Retrieval: Comprehensive retrieval returns related memories with augmentations, while refined retrieval uses task-specific attributes to guide selection.Refined retrieval includes attribute-based filtering and embedding-based similarity search.
  • Memory Retrieval: Retrieved memories are integrated into the current context to inform the ongoing interaction.

4 Evaluation

The evaluation uses LLM-REDIAL for conversational recommendation and LoCoMo for question answering and event summarization. It combines standard retrieval and answer metrics with LLM-based and subjective response-quality measures.

  • Datasets: MemInsight is evaluated on two benchmarks: LLM-REDIAL and LoCoMo.LLM-REDIAL contains conversational movie-recommendation dialogues, while LoCoMo contains multi-session dialogues for question answering and event summarization.
  • Datasets: LoCoMo includes single-hop, multi-hop, temporal-reasoning, open-domain, and adversarial question types with annotated answer turns.It also provides event labels for evaluating event summarization.
  • Experimental Setup: Dataset augmentation uses zero-shot prompting to extract relevant attributes and values, with multiple LLMs supporting attribute generation.Embedding-based retrieval uses Titan Text Embedding and FAISS.
  • Metrics: Question answering is evaluated with F1-score and recall, while conversational recommendation uses Recall@K, NDCG@K, genre matching, Persuasiveness, and Relatedness.
  • Metrics: Event summarization is evaluated with G-Eval for relevance, consistency, and coherence against reference labels.

5 Experiments

Experiments evaluate MemInsight across question answering, conversational recommendation, and event summarization using attribute-based, embedding-based, and comprehensive retrieval settings. Across tasks, augmentation generally improves retrieval, contextual reasoning, recommendation quality, and summary generation, while performance depends on augmentation granularity and backbone model.

  • Question Answering: MemInsight achieves higher overall question-answering accuracy than baselines with both attribute-based and embedding-based retrieval.Attribute-based retrieval improves performance on single-hop, temporal, and adversarial questions, while embedding-based retrieval maintains the highest overall accuracy despite DPR gains on temporal and adversarial questions.
  • Question Answering: 35% overall recall improvement follows from priority augmentation over basic augmentation across LoCoMo retrieval categories.Priority augmentation also improves multi-hop retrieval by integrating dispersed evidence from historical dialogue.
  • Conversational Recommendation: Attribute-based filtering outperforms the LLM-REDIAL model while matching the baseline with almost 90% less retrieved memory.Comprehensive augmentation tends to outperform the baseline and LLM-REDIAL model, while genre matching remains comparable when all attributes are considered.
  • Conversational Recommendation: 10–11% higher partial persuasiveness results from memory augmentation, while embedding-based retrieval increases highly persuasive recommendations by 12%.Attribute-based retrieval also increases highly persuasive recommendations by 4%, and embedding-based retrieval improves all relatedness metrics, although recall and NDCG do not reflect these gains.
  • Event Summarization: Turn-level augmentations produce more precise and detailed event information than session-level annotations, improving event summarization over both the baseline and session-level setting.MemInsight remains comparable to the baseline while using only dialogue turns or sessions containing the event label; Claude-3-Sonnet augmentations improve all reported metrics when Llama generates summaries.
  • Qualitative Analysis: 99.14% of Claude-3-Sonnet-generated annotations are grounded in dialogue, with the remaining 0.86% mainly abstract or generic attributes.The qualitative analysis uses the DeepEval hallucination metric to assess factual consistency.
  • Overall Findings: MemInsight combines structured attribute-based augmentation with attribute-based filtering and embedding-based retrieval across the evaluated tasks.The framework uses generated augmentations to filter irrelevant memory, improve similarity search, and integrate relevant historical context into agent reasoning.

7 Limitations

MemInsight performs strongly across tasks and datasets, but its augmentation quality, dependence on the attribute-generation model, and text-only implementation constrain its scope.

  • Abstract or overly generic annotations may reduce retrieval specificity in ambiguous dialogues requiring fine-grained memory access.These annotations are described as not factually incorrect, but less useful for precise retrieval.
  • Performance depends on the capabilities of the underlying LLM used for attribute generation.Less capable or unaligned models may produce less consistent augmentations.
  • The current implementation is limited to text-based interactions.Future extensions could support multimodal inputs such as images or audio.
  • The paper reports review of artifact licenses and de-identification of all datasets used.These statements concern research-use permissions and dataset anonymity rather than model performance.

C Embedding-based Retrieval

The embedding-based retrieval appendix compares attribute-level averaging with encoding all augmentations into one vector, using the resulting representations for similarity search.

  • Each attribute and value is embedded independently, then averaged to form the final memory representation for similarity search.This is one of the two embedding methods considered.
  • All generated attributes and values can instead be encoded into a single embedding vector stored for retrieval.The method is presented as an alternative representation strategy.
  • Averaging over all augmentations produces a more consistent and reliable similarity measure.The passage attributes this to comprehensively capturing attributes and differentiating between items.

E Conversational Recommendation

The conversational recommendation evaluation uses prompts that incorporate both basic and priority augmentations for movie recommendations.

  • Prompts for conversational recommendation generate movie recommendations using basic and priority augmentations.
  • The appendix documents the prompting setup used to evaluate conversational recommendation.The cited passage specifically identifies Table 9 as presenting these prompts.

E.2 Evaluation Framework

The evaluation framework augments movies and dialogues before generating recommendations, while separate augmentation prompts focus event summarization on event-related attributes.

  • Evaluation Framework: Movies are augmented with entity-centric attributes to enhance retrieval effectiveness.
  • Evaluation Framework: Dialogues are prepared by masking ground-truth labels and prompting the LLM to recover them from augmentations based on previous user interactions.
  • Evaluation Framework: Figure 4 contrasts entity-centric augmentation for a book with conversation-centric augmentation for a sample dialogue.
  • Evaluation Framework: Figure 5 summarizes the most frequent generated attributes across movies, sports items, electronics, and books.The figure describes the top 10 attributes by frequency across these domains.
  • Evaluation Framework: Event summarization uses only event-related attributes to focus on structured summaries of key dialogue events.

E.4 Additional Experiments

MemInsight is evaluated against raw zero-shot LLM summaries to isolate the contribution of memory augmentation. It consistently improves event summarization quality, especially when augmentations are integrated with dialogue context.

  • MemInsight consistently improves event summarization quality across evaluated models.Raw zero-shot summaries without memory augmentation provide the comparison baseline.
  • The best performance occurs when augmentations are integrated with dialogue context.This result highlights the value of fine-grained annotations and contextual grounding.
  • The findings indicate improved factual and semantic quality in generated summaries.

F Qualitative Analysis

The qualitative analysis examines augmentation generation, embedding strategies, evaluation workflows, and annotation quality across recommendation, question answering, and event summarization. It highlights model-specific hallucination and stability patterns alongside the organization of augmentation-based evaluations.

  • Augmentation quality: Claude-Sonnet maintains consistency across adjacent dialogue turns, whereas Llama and Mistral produce hallucinated or unsupported attributes.Llama-generated augmentations include information absent from the dialogue, while Mistral is described as less stable.
  • Retrieval and embedding analysis: Embedding-based retrieval compares averaging independent embeddings with embedding all augmentations together.
  • Retrieval and embedding analysis: The illustrative embedding example contrasts similar attributes between The Departed and Shutter Island with differences between The Departed and The Hobbit.The illustration selects the top five attributes for each movie.
  • Augmentation quality: A DeepEval hallucination evaluation reports a 99.14% score, with lower-scoring annotations remaining semantically aligned despite being more generic or abstract.
  • Task evaluations: The evaluation materials cover conversation recommendation, question answering, and event summarization through task-specific prompts and framework configurations.Event summarization compares relevance, coherence, and consistency across baseline, turn-level, and session-level augmentation settings.
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