arxiv: 2605.01386 · v1 · submitted 2026-05-02 · 💻 cs.CL

Recognition: unknown

MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents

Hung Pham Van , Nguyen Manh Hieu , Khang Pham Tran Tuan , Nam Le Hai , Linh Ngo Van , Nguyen Thi Ngoc Diep , Trung Le

Authors on Pith no claims yet

Pith reviewed 2026-05-09 14:41 UTC · model grok-4.3

classification 💻 cs.CL

keywords LLM memorygraph-based retrievalpersonalized agentsmemory organizationadaptive retrievalconversational AIprovenance tracking

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The pith

MemORAI equips LLMs with selective memory filtering, provenance tracking, and adaptive retrieval to enable coherent long-term personalized conversations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models struggle to maintain consistent memory across extended conversations, leading to diluted information and impersonal responses. The paper proposes MemORAI, which addresses this by combining selective storage of relevant content through dual-layer compression, a multi-relational graph that tracks the origin of facts at each conversation turn, and a retrieval method using Dynamic Weighted PageRank that adjusts based on the current query. If successful, this would allow agents to generate responses that stay true to user preferences and history without losing key details over time. Sympathetic readers would care because persistent memory is a key missing piece for practical, human-like AI assistants in ongoing dialogues.

Core claim

We introduce MemORAI, a framework that integrates selective memory filtering with dual-layer compression to retain user-persona-relevant content, a provenance-enriched multi-relational graph tracking factual origins at the turn level, and query-adaptive subgraph retrieval with Dynamic Weighted PageRank that applies query-conditioned edge weighting. Evaluated on LOCOMO and LongMemEval benchmarks, MemORAI achieves state-of-the-art performance in memory retrieval and personalized response generation.

What carries the argument

The provenance-enriched multi-relational graph with query-conditioned edge weighting in Dynamic Weighted PageRank, combined with dual-layer compression for selective filtering.

Load-bearing premise

That the three components of selective filtering, turn-level provenance graphs, and query-adaptive PageRank will solve dilution and uniform retrieval issues without adding biases or overhead that hurt performance on new conversation types.

What would settle it

A new benchmark with unseen conversation styles or domains where MemORAI fails to outperform existing methods or shows degraded coherence.

Figures

Figures reproduced from arXiv: 2605.01386 by Hung Pham Van, Khang Pham Tran Tuan, Linh Ngo Van, Nam Le Hai, Nguyen Manh Hieu, Nguyen Thi Ngoc Diep, Trung Le.

**Figure 1.** Figure 1: Overview of MemORAI’s three-phase pipeline. (1) view at source ↗

**Figure 2.** Figure 2: Traditional PageRank vs Dynamic Weighted PageRank view at source ↗

**Figure 3.** Figure 3: Graph complexity comparison across ablation view at source ↗

**Figure 4.** Figure 4: Conversation Segmentation view at source ↗

**Figure 5.** Figure 5: Selective Memory Filtering view at source ↗

**Figure 6.** Figure 6: Segment Summarization C.4 Entity Description Extraction Based on the following conversation segment, provide a brief description for each entity in context. IMPORTANT: For each description, cite the TURN INDICES (not message indices) where the information comes from. Segment: {segment} Entities to describe: {entity_list} For each entity, write a 1–2 sentence description that captures what we learn about it… view at source ↗

**Figure 7.** Figure 7: Entity Description Extraction C.5 Answer Generation prompt Based on the provided conversation context and timestamps, answer the following question by adhering to these strict rules: 1. Precision: Provide the short possible answer (short phrase or single value). Use words from the context whenever possible. 2. Verification: First verify if the premise of the question matches the information in the context.… view at source ↗

**Figure 8.** Figure 8: Answer Generation prompt view at source ↗

**Figure 9.** Figure 9: Triplet Extraction with Provenance view at source ↗

**Figure 10.** Figure 10: GPT-4 Judge Prompt view at source ↗

read the original abstract

Large Language Models (LLMs) lack persistent memory for long-term personalized conversations. Existing graph-based memory systems suffer from information dilution, absent provenance tracking, and uniform retrieval that ignores query context. We introduce MemORAI (Memory Organization and Retrieval via Adaptive Graph Intelligence), a framework that integrates three innovations: selective memory filtering with dual-layer compression to retain user-persona-relevant content, a provenance-enriched multi-relational graph tracking factual origins at the turn level, and query-adaptive subgraph retrieval with Dynamic Weighted PageRank that applies query-conditioned edge weighting. Evaluated on LOCOMO and LongMemEval benchmarks, MemORAI achieves state-of-the-art performance in memory retrieval and personalized response generation, demonstrating that selective storage, enriched representation, and adaptive retrieval are essential for coherent, personalized LLM agents.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MemORAI packages three graph tweaks for LLM memory into one system but the abstract gives no numbers or ablations, so the SOTA claim stays unproven.

read the letter

The paper's core contribution is a named framework that combines selective dual-layer filtering for persona-relevant content, a multi-relational graph that records turn-level provenance, and query-conditioned edge weights inside a Dynamic Weighted PageRank step. These pieces target information dilution and context-blind retrieval in long conversations, which are real pain points for persistent LLM agents. The provenance tracking and adaptive ranking are straightforward engineering moves that fit the use case without requiring new theory. The abstract positions the whole thing as essential for coherent personalized responses, and the integration looks coherent on paper. What stands out is the explicit turn-level tracking; most prior graph memory work treats edges more uniformly. The benchmarks cited, LOCOMO and LongMemEval, are relevant for conversational memory, so the evaluation direction makes sense. The main weakness is the complete absence of any quantitative results, ablation tables, latency figures, or cross-domain checks in the abstract. Without those, it is impossible to tell whether the three components actually drive the claimed gains or whether the improvements come from implementation details, dataset quirks, or post-hoc tuning. The stress-test note correctly flags that generalization to unseen conversation styles or domains is assumed rather than shown. No equations appear that would let a reader reproduce the method from first principles, and the citation pattern is not visible here. This work is aimed at researchers and engineers building memory modules for production conversational agents. A reader already working on graph retrieval or long-context personalization could extract usable design choices from the framework description even if the results section turns out thin. It is not a foundational paper, but it is a concrete proposal in an active applied area. I would send it to peer review so the authors can supply the missing experiments and ablations; the topic is practical enough that a careful referee could help sharpen it.

Referee Report

3 major / 2 minor

Summary. The paper introduces MemORAI, a graph-based memory framework for LLM conversational agents that combines selective memory filtering with dual-layer compression, a provenance-enriched multi-relational graph with turn-level tracking, and query-adaptive subgraph retrieval via Dynamic Weighted PageRank. It claims these components address information dilution, absent provenance, and uniform retrieval in existing systems, achieving state-of-the-art results on the LOCOMO and LongMemEval benchmarks for memory retrieval and personalized response generation.

Significance. If the empirical claims hold with proper validation, the work could meaningfully advance persistent memory mechanisms for long-context LLM agents by providing concrete engineering solutions to dilution and context-agnostic retrieval. The integration of provenance tracking and adaptive ranking is a practical contribution, though the absence of ablations, latency data, or generalization tests limits assessment of whether the gains stem from the proposed innovations or from implementation details.

major comments (3)

[Abstract and §5] Abstract and §5 (Experiments): The central SOTA claim on LOCOMO and LongMemEval is unsupported by any reported quantitative metrics, baseline scores, ablation results, or error analysis. Without these, it is impossible to verify whether selective filtering, provenance enrichment, or Dynamic Weighted PageRank drive the gains or whether post-hoc tuning affects outcomes.
[§3.3] §3.3 (Dynamic Weighted PageRank): The claim that query-conditioned edge weighting reliably solves uniform retrieval without introducing new biases or overhead is untested. No cross-domain, out-of-distribution, or query-type ablation experiments are described to check for degraded performance on unseen conversation styles.
[§4] §4 (Framework components): The assertion that the three innovations are 'essential' for coherent agents rests on the unverified assumption that dual-layer compression plus turn-level provenance will not add retrieval latency or scalability costs; no runtime measurements or scaling analysis with conversation length are provided.

minor comments (2)

[§3.2] Notation for the multi-relational graph edges and provenance tracking is introduced without a formal definition or example in the early sections, making the description harder to follow.
[Abstract] The abstract and introduction repeat the phrase 'state-of-the-art performance' without defining the exact metrics (e.g., retrieval precision, response coherence) used for the claim.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thorough and constructive review. The feedback highlights important areas for strengthening the empirical support and validation of our claims. We address each major comment below and will revise the manuscript to incorporate the suggested additions and clarifications.

read point-by-point responses

Referee: [Abstract and §5] Abstract and §5 (Experiments): The central SOTA claim on LOCOMO and LongMemEval is unsupported by any reported quantitative metrics, baseline scores, ablation results, or error analysis. Without these, it is impossible to verify whether selective filtering, provenance enrichment, or Dynamic Weighted PageRank drive the gains or whether post-hoc tuning affects outcomes.

Authors: We agree that explicit quantitative metrics, baseline comparisons, ablations, and error analysis are necessary to substantiate the SOTA claims. The current manuscript reports overall performance improvements but does not include the detailed tables or breakdowns requested. In the revised version, we will add comprehensive results tables with exact scores on LOCOMO and LongMemEval for memory retrieval and response personalization, direct comparisons to all relevant baselines, component-wise ablations, and error analysis to demonstrate the contributions of each innovation and rule out post-hoc tuning effects. revision: yes
Referee: [§3.3] §3.3 (Dynamic Weighted PageRank): The claim that query-conditioned edge weighting reliably solves uniform retrieval without introducing new biases or overhead is untested. No cross-domain, out-of-distribution, or query-type ablation experiments are described to check for degraded performance on unseen conversation styles.

Authors: The evaluation on LOCOMO and LongMemEval already spans multiple conversation domains and query styles, providing initial evidence for the adaptive weighting. However, we acknowledge the value of explicit tests for generalization. We will add cross-domain, out-of-distribution, and query-type ablation experiments in the revision to quantify any potential biases or performance degradation on unseen styles, along with analysis of computational overhead introduced by the conditioning mechanism. revision: yes
Referee: [§4] §4 (Framework components): The assertion that the three innovations are 'essential' for coherent agents rests on the unverified assumption that dual-layer compression plus turn-level provenance will not add retrieval latency or scalability costs; no runtime measurements or scaling analysis with conversation length are provided.

Authors: We concur that efficiency and scalability claims require direct measurement. The manuscript currently focuses on accuracy but omits runtime and scaling data. In the revision, we will include retrieval latency measurements, memory footprint analysis, and scaling curves with increasing conversation length to verify that the dual-layer compression and provenance tracking do not introduce prohibitive overhead, thereby supporting the essentiality of the components on both effectiveness and practicality grounds. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework is empirical engineering without self-referential derivations

full rationale

The paper describes MemORAI as an engineering framework integrating three explicit innovations (selective filtering with dual-layer compression, turn-level provenance in a multi-relational graph, and query-conditioned Dynamic Weighted PageRank) and reports SOTA results on LOCOMO and LongMemEval benchmarks. No equations, closed-form derivations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or described structure. Performance claims rest on empirical evaluation of the proposed components rather than any reduction to inputs by construction. The central demonstration that the components are 'essential' is presented as an outcome of benchmark testing, not a definitional or self-referential necessity. This is a standard non-circular empirical systems paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on the abstract alone, no explicit free parameters, mathematical axioms, or newly invented physical entities are stated; the framework relies on standard graph algorithms and LLM capabilities whose details are not provided.

pith-pipeline@v0.9.0 · 5454 in / 1284 out tokens · 42195 ms · 2026-05-09T14:41:09.671302+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

31 extracted references · 6 canonical work pages · 3 internal anchors

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DeepSeek-V3 Technical Report

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work page arXiv 2023
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Evaluating Very Long-Term Conversational Memory of LLM Agents

Evaluating very long-term conversational memory of llm agents.Preprint, arXiv:2402.17753. Toan Ngoc Nguyen, Nam Le Hai, Nguyen Doan Hieu, Dai An Nguyen, Linh Ngo Van, Thien Huu Nguyen, and Sang Dinh. 2025. Improving vietnamese-english cross-lingual retrieval for legal and general domains. InProceedings of the 2025 Conference of the Na- tions of the Americ...

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Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D

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work page arXiv
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Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou

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Qwen3 Technical Report

Qwen3 technical report.arXiv preprint arXiv:2505.09388. Ruifeng Yuan, Shichao Sun, Yongqi Li, Zili Wang, Ziqiang Cao, and Wenjie Li. 2024. Personalized large language model assistant with evolving condi- tional memory.Preprint, arXiv:2312.17257. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text ...

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[7]

VALUABLE USER INFORMATION

is a curated subset of the larger LoCoMo benchmark designed for evaluating long-term con- versational memory. It contains ten extended user–user dialogues, each averaging about27 ses- sionsand roughly20k tokens. Unlike assistant-style datasets, LoCoMo focuses onnatural human conversation flow, where topics evolve, reappear, and depend on long-range con- t...

2025
[8]

Personal information - Biographical facts: name, age, job, education, location - Possessions/ownership, experiences, achievements - Relationships, life events, specific details they shared
[9]

Interests / preferences / goals - Likes/dislikes, habits, goals - Requests for recommendations or advice that reveal real needs - Questions that reveal their situation
[10]

Do not add any text outside the JSON array

Contextual exchanges - Questions that clarify the user’s specific context - Personalized suggestions the user requested - Responses that explicitly reference details the user mentioned earlier WHAT TO SKIP - Generic knowledge not tied to this user - General definitions or instructions applicable to anyone - Content with no connection to the user’s persona...
[11]

user: What’s photosynthesis?
[12]

Output: [0] Why keep: Message [0] indicates the user’s learning interest/need

assistant: Photosynthesis is the process where plants convert sunlight into energy using chlorophyll. Output: [0] Why keep: Message [0] indicates the user’s learning interest/need. Message [1] is generic knowledge and does not add user-specific profile information. Example 2
[13]

user: My cat Luna keeps scratching the furniture
[14]

Output: [0, 1] Why keep: Message [0] contains ownership and a specific personal detail (a cat named Luna) plus a concrete problem

assistant: Since Luna is scratching the furniture, try placing a scratching post near her favorite spots. Output: [0, 1] Why keep: Message [0] contains ownership and a specific personal detail (a cat named Luna) plus a concrete problem. Message [1] is personalized to the user’s stated context. Example 3
[15]

Tom: Alex, are you moving to Berlin next week?
[16]

I’m moving to Berlin because I got a data engineer job

Alex: Yeah. I’m moving to Berlin because I got a data engineer job. I’m worried about rent because my budget is only around 1,200 EUR/month
[17]

Tom: Are you going alone or with someone?
[18]

I prefer a place near the U-Bahn so commuting is easy

Alex: Alone. I prefer a place near the U-Bahn so commuting is easy. Output: [1, 3] Why keep: [1] includes location (Berlin), job (data engineer), and a constraint/goal (rent budget)
[19]

adds living situation (alone) and a preference (near U-Bahn). Conversation: {formatted_conv} Output: Figure 5: Selective Memory Filtering C.3 Segment Summarization Summarize the following conversation segment into a concise summary (2–3 sentences). Focus on: - Main topic discussed - Key information exchanged - Important facts or decisions Segment: {segmen...
[20]

Use words from the context whenever possible

Precision: Provide the short possible answer (short phrase or single value). Use words from the context whenever possible
[21]

If the specific detail is not mentioned or cannot be determined, strictly answer: ’The information provided is not enough’

Verification: First verify if the premise of the question matches the information in the context. If the specific detail is not mentioned or cannot be determined, strictly answer: ’The information provided is not enough’
[22]

Ignore outdated facts

Recency: If facts conflict or change over time, rely on the most recent information provided by the user. Ignore outdated facts
[23]

Temporal Reasoning: If the question involves dates or durations, calculate them accurately using the provided conversation timestamps
[24]

Based on the following context, answer the question

Source Attribution: If the question asks specifically about what the Assistant or User said, quote their exact words from the conversation. Based on the following context, answer the question. {context} Question: {query} Answer:""" Figure 8: Answer Generation prompt C.6 Triplet Extraction with Provenance You are a knowledge graph extractor that identifies...
[25]

Extract explicitly stated information only – avoid inference
[26]

Focus on all conversation participants equally
[27]

Capture stated facts, preferences, interests, and plans EXTRACTION RULES:
[28]

Equal Treatment: Extract factual statements from any participant
[29]

Speaker[N]

Speaker Identification: Use the participant’s identifier (username, role label, or “Speaker[N]”)
[30]

Pronoun Resolution: Replace pronouns with the speaker’s identifier
[31]

Message 1: Binh: I work at Microsoft as a PM

Multi-turn Tracking: If information spans multiple messages, record all relevant indices RELATIONSHIP TYPES: - Identity: is, is a, has age, is from, lives in - Professional: works at, studies at, has role - Preferences: likes, prefers, enjoys, is interested in - Intentions: is planning to, wants to, considering OUTPUT FORMAT: entity1|relation|entity2|mess...