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arxiv: 2605.19952 · v1 · pith:66T6XJTQnew · submitted 2026-05-19 · 💻 cs.CL

Rethinking How to Remember: Beyond Atomic Facts in Lifelong LLM Agent Memory

Jingwei Sun , Jianing Zhu , Jiangchao Yao , Tongliang Liu , Bo Han This is my paper

Pith reviewed 2026-05-20 05:57 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM agent memorylong-term dialoguemulti-granularity representationprompt optimizationfact extractionsynthesized profileslifelong learningTextGrad
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The pith

TriMem keeps raw dialogue segments, atomic facts and synthesized profiles together so LLM agents can store faithfully, retrieve efficiently and reason deeply over long histories.

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

Existing memory methods for LLM agents compress conversations into isolated atomic facts using fixed prompts, which discards details and blocks connecting scattered information for deeper thought. TriMem maintains three memory layers at once: original dialogue chunks tied to their sources, the extracted facts for fast matching, and higher-level profiles that pull facts into coherent views. It adds TextGrad prompt optimization that improves the extraction and profiling instructions over time using performance feedback, allowing the system to adapt without retraining the model. Experiments on LoCoMo and PerLTQA show consistent gains over baselines across several LLM backbones, suggesting agents can sustain longer, more natural interactions.

Core claim

The paper establishes that a memory system with three coexisting representation granularities—raw dialogue segments anchored by source identifiers, extracted atomic facts, and synthesized profiles that aggregate facts into holistic understanding—combined with TextGrad-based iterative prompt refinement enables faithful storage, efficient retrieval and deep reasoning over accumulated dialogue history, outperforming static fact-centric approaches on long-term QA benchmarks.

What carries the argument

TriMem's three-granularity memory (raw segments for fidelity, atomic facts for retrieval, synthesized profiles for reasoning) plus TextGrad prompt optimization that refines extraction and profiling instructions via response-quality feedback without parameter updates.

If this is right

  • Agents can connect dispersed facts into coherent understanding instead of treating them as isolated items.
  • Memory prompts can evolve over the agent's lifetime based on actual task performance.
  • Raw source dialogues remain available for verification or detailed inspection when needed.
  • The same backbone model works better on long-term tasks without changes to its weights.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar multi-level memory could apply to non-dialogue agent tasks such as planning sequences or tool-use histories.
  • The design may lower reliance on ever-larger context windows by offloading detail to structured memory layers.
  • If prompt optimization generalizes, it offers a lightweight way to adapt memory behavior to new domains or users.

Load-bearing premise

Three memory levels plus feedback-driven prompt changes can keep extraction consistent and reasoning deep across varied dialogue styles without creating contradictions or high cost.

What would settle it

A new long-dialogue test set where TriMem produces inconsistent fact extraction or fails to improve reasoning depth over strong baselines would show the approach does not deliver the claimed benefits.

Figures

Figures reproduced from arXiv: 2605.19952 by Bo Han, Jiangchao Yao, Jianing Zhu, Jingwei Sun, Tongliang Liu.

Figure 1
Figure 1. Figure 1: Comparison with previous systems. Our system establishes a three-level architecture, which leverages raw dialogue to guarantee information fidelity in storage, relies on key facts to enable efficient retrieval, and provides in-depth understanding over the facts to ensure the reliability of reasoning. The construction prompts are also continuously optimized based on answer feedback. matching against these f… view at source ↗
Figure 2
Figure 2. Figure 2: Analysis of existing agent memory systems. Although the totally extracted fact based systems enable efficient retrieval, they suffer from lossy storage and shallow reasoning. Additionally, the fixed prompt words cannot be well applied to all contexts, which compromise the performance. critical issue: even if the system retrieves topically relevant entries, it still fails to provide accurate and complete an… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of TriMem. It segments historical dialogue into windows, extracts multi￾dimensional facts with traceable index, and constructs entity profiles. Relevant memories are retrieved according to queries, and prompts are continuously optimized via response feedback. 3 Method In this section, we present TriMem, which constructs a three-level architecture, from raw dialogue to extracted key fact and integr… view at source ↗
Figure 4
Figure 4. Figure 4: Ablation of profile and raw dialogue module. Incorporating entity profile and raw dialogue shows the best results, which demonstrates the rationality of our design [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Impact of varying evolution step. Appropriate update steps lead to overall improvements, while further updates result in excessive refinement, which negatively affects the performance [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Performance in different retrieval numbers. The results show that the performance is optimal when the number is 25, and performance degrades when the number is too low or too high. Generalization on Different Datasets. PerLTQA is also a widely adopted benchmark for long￾term agent QA, consisting of multi-dimensional evaluations covering personal profiles, social rela￾tionship, historical events and dialogu… view at source ↗
Figure 7
Figure 7. Figure 7: Necessity of search query and efficiency analysis. Although the introduction of search query increases the retrieval time, it greatly improve the system performance. The smaller window size increase the memory construction time, therefore we set the size to 40 to maintain the efficiency, which enabling competitive construction time comparing with previous methods [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Ablation of window size. The memory system can also achieve great performance with smaller size. However, considering the efficiency, we finally set the window size to 40. Performance in Different Retrieval Numbers. In [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visualization of prompt evolution. As evolution progresses, the prompts become more refined. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Content of retrieval entries. TriMem realizes precise retrieval when handling different questions. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Example of search query. Each question is divided into detailed required information during analysis process. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Performance of different window size. Larger window size leads to lower extraction quality. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
read the original abstract

To enable reliable long-term interaction, LLM agents require a memory system that can faithfully store, efficiently retrieve, and deeply reason over accumulated dialogue history. Most existing methods adopt an extracted fact based paradigm: handcrafted static prompts compress raw dialogues into atomic facts, which are then stored, matched, and injected into downstream reasoning. Nevertheless, such fact-centric designs inevitably discard fine-grained details in original dialogues and fail to support deep reasoning over scattered isolated facts. Moreover, static prompts cannot maintain consistent extraction granularity across diverse dialogue styles. To address these limitations, we propose TriMem, which maintains three coexisting representation granularities, including raw dialogue segments anchored by source identifiers for storage fidelity, extracted atomic facts for efficient memory retrieval, synthesized profiles that aggregate dispersed facts into holistic semantic understanding for deep reasoning. We further adopt TextGrad-based prompt optimization, which iteratively refines extraction and profiling prompts via response quality feedback, achieving lifelong evolution without any parameter updating. Extensive experiments on LoCoMo and PerLTQA across multiple LLM backbones demonstrate that TriMem consistently outperforms strong memory baselines. The code is available at https://TMLR-TriMem.github.io .

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.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces TriMem, a memory architecture for lifelong LLM agents that maintains three coexisting representation granularities—raw dialogue segments with source identifiers, extracted atomic facts, and synthesized profiles—combined with TextGrad-based iterative prompt optimization to enable faithful storage, efficient retrieval, and deep reasoning without parameter updates. It critiques static-prompt fact extraction for discarding details and inconsistent granularity, and reports consistent outperformance over strong baselines on the LoCoMo and PerLTQA benchmarks across multiple LLM backbones, with code released.

Significance. If the results and consistency claims hold after verification, the work could meaningfully advance memory design for agentic LLMs by balancing fidelity and reasoning depth via multi-granularity representations and prompt evolution. The TextGrad optimization loop and open code are clear strengths that support reproducibility and falsifiability of the adaptation mechanism.

major comments (2)
  1. [Abstract and Experiments] Abstract and Experiments section: the claim of consistent outperformance on LoCoMo and PerLTQA lacks reported details on experimental controls, statistical significance testing, or ablations of the three granularities and TextGrad components. This is load-bearing for the central empirical claim and prevents verification of whether gains stem from the proposed architecture.
  2. [Method] Method section (architecture and optimization loop): no explicit consistency verification step (e.g., entailment checks or source-grounding metric) is described between raw segments, atomic facts, and synthesized profiles after each TextGrad round. This directly undermines the premise that the three granularities maintain faithfulness and support holistic reasoning without drift across dialogue-style shifts.
minor comments (1)
  1. [Abstract] The abstract could more precisely specify the LLM backbones and baseline implementations to aid immediate assessment of generality.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point by point below, providing the strongest honest defense of the work while committing to revisions that strengthen clarity and verifiability without misrepresenting the original contributions.

read point-by-point responses

  1. Referee: [Abstract and Experiments] Abstract and Experiments section: the claim of consistent outperformance on LoCoMo and PerLTQA lacks reported details on experimental controls, statistical significance testing, or ablations of the three granularities and TextGrad components. This is load-bearing for the central empirical claim and prevents verification of whether gains stem from the proposed architecture.

    Authors: We acknowledge that the current presentation of results would benefit from expanded details to support verification. The manuscript reports consistent outperformance across multiple LLM backbones on both benchmarks, with code released for reproducibility. In the revised version, we will expand the Experiments section to include explicit descriptions of experimental controls (e.g., fixed seeds, prompt templates, and dialogue-style variations), statistical significance testing (e.g., paired t-tests with p-values across runs), and dedicated ablation studies isolating the three granularities and the TextGrad optimization loop. These additions will directly address whether gains derive from the proposed architecture. revision: yes

  2. Referee: [Method] Method section (architecture and optimization loop): no explicit consistency verification step (e.g., entailment checks or source-grounding metric) is described between raw segments, atomic facts, and synthesized profiles after each TextGrad round. This directly undermines the premise that the three granularities maintain faithfulness and support holistic reasoning without drift across dialogue-style shifts.

    Authors: The TextGrad optimization loop refines prompts iteratively using downstream response quality as feedback, which is intended to preserve faithfulness across granularities without parameter updates. However, we agree that an explicit consistency verification mechanism is not described in the current Method section. In the revision, we will add a description of how consistency is enforced, including options for entailment checks between raw segments and atomic facts as well as source-grounding metrics for synthesized profiles, to clarify the absence of drift across dialogue shifts. revision: yes

Circularity Check

0 steps flagged

TriMem architecture and TextGrad optimization form an independent design with no reduction to fitted inputs or self-citations

full rationale

The paper introduces TriMem as a new memory system maintaining three explicit representation levels (raw segments with source IDs, atomic facts, synthesized profiles) plus TextGrad prompt optimization driven by response-quality feedback. No equations, fitted parameters, or predictions are defined in the abstract or described derivation; the three granularities are presented as coexisting design choices motivated by critiques of prior fact-centric methods, and the optimization loop is an external technique applied without parameter updates. Experiments on LoCoMo and PerLTQA report empirical outperformance but do not reduce any claimed advantage to quantities defined by the method's own inputs. No self-citation chains, uniqueness theorems, or ansatzes are invoked as load-bearing. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on the domain assumption that multi-granularity memory improves fidelity and reasoning, with no free parameters, fitted constants, or new invented entities introduced beyond the system components themselves.

axioms (1)
  • domain assumption Maintaining raw segments, atomic facts, and synthesized profiles together improves storage fidelity, retrieval efficiency, and deep reasoning compared to fact-only designs.
    This premise directly motivates the proposal and is invoked to address the limitations of static prompt-based fact extraction.

pith-pipeline@v0.9.0 · 5738 in / 1175 out tokens · 39340 ms · 2026-05-20T05:57:59.660827+00:00 · methodology

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Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages

  1. [1]

    Only extract entries that contain novel factual information (events, activities, plans, preferences, relationships, specific details like names/titles/numbers)

    **Extract Facts, Not Social Gestures **: SKIP greetings, thank-yous, compliments, and generic praise. Only extract entries that contain novel factual information (events, activities, plans, preferences, relationships, specific details like names/titles/numbers). ,→ ,→ ,→

    work page

  2. [2]

    source_dialogue_ids

    **Source Dialogue IDs **: Each dialogue line starts with an ID in brackets like [ID:42]. For each memory entry, list the dialogue IDs that the entry was derived from in the "source_dialogue_ids" field. This is CRITICAL for tracing back to original context. ,→ ,→ ,→

    work page

  3. [3]

    Always use the person's actual name

    **Force Disambiguation **: Absolutely PROHIBIT using pronouns (he, she, it, they, this, that). Always use the person's actual name. Every memory MUST explicitly state WHO did/said/experienced the thing. ,→ ,→

    work page

  4. [4]

    yesterday

    **Resolve Temporal References **: Convert ALL relative time expressions to absolute dates based on the dialogue timestamp:,→ - "yesterday" on May 8 -> May 7 - "last year" in 2023 -> 2022 - "last week" -> compute the actual date - "next month" -> compute the actual month The "timestamp" field should be the EVENT time, NOT the conversation time.,→

    work page 2023

  5. [5]

    If one dialogue mentions 3 activities, create 3 entries

    **Atomic Facts **: Extract individual facts as SEPARATE entries. If one dialogue mentions 3 activities, create 3 entries. Do not merge unrelated facts into one summary. ,→ ,→

    work page

  6. [6]

    **Preserve Specific Details **: Always capture exact names (people, pets, books, songs), exact numbers (durations, counts, ages), and specific entities. ,→ ,→

    work page

  7. [7]

    25 minutes on, 5 minutes off

    **Identify Described-But-Unnamed Things Using World Knowledge **: When the dialogue describes something without naming it, IDENTIFY it by name in the memory entry. This is CRITICAL | future queries will search by name, not by description. ,→ ,→ ,→ - A study method like "25 minutes on, 5 minutes off"→identify as "Pomodoro technique",→ - A composer whose mu...

    work page

  8. [8]

    lossless_restatement

    **Precise Extraction **: - keywords: Core keywords (names, places, entities, topic words) - timestamp: Absolute time of the EVENT in ISO 8601 format (resolved from relative expressions),→ - location: Specific location name (if mentioned) - persons: All person names mentioned - entities: Companies, products, organizations, book titles, song names, etc.,→ -...

    work page 2025

  9. [9]

    What type of question is this? (factual, temporal, relational, explanatory, etc.),→

    work page

  10. [10]

    What key entities, events, or concepts need to be identified?

    work page

  11. [11]

    What relationships or connections need to be established?

    work page

  12. [12]

    question_type

    What minimal set of information pieces would be sufficient to answer this question?,→ Return your analysis in JSON format: ```json {{ "question_type": "type of question", "key_entities": ["entity1", "entity2", ...], "required_info": [ {{ "info_type": "what kind of information", "description": "specific information needed", "priority": "high/medium/low" }}...

    work page

  13. [13]

    Always include the original query as one option

    work page

  14. [14]

    Generate only the minimal necessary queries (usually 1-3)

    work page

  15. [15]

    Each query should target a specific information requirement

    work page

  16. [16]

    Avoid redundant or overlapping queries

    work page

  17. [17]

    reasoning

    Focus on efficiency - fewer, more targeted queries are better Return your response in JSON format: ```json {{ "reasoning": "Brief explanation of the query strategy", "queries": [ "targeted query 1", "targeted query 2", ... ] }} ``` Return ONLY the JSON, no other text. 31 Rethinking How to Remember: Beyond Atomic Facts in Lifelong LLM Agent Memory E.4 Prom...

    work page

  18. [18]

    keywords: List of keywords (names, places, topic words, etc.)

    work page

  19. [19]

    persons: Person names mentioned

    work page

  20. [20]

    time_expression: Time expression (if any)

    work page

  21. [21]

    location: Location (if any)

    work page

  22. [22]

    keywords

    entities: Entities (companies, products, etc.) Return in JSON format: ```json {{ "keywords": ["keyword1", "keyword2", ...], "persons": ["name1", "name2", ...], "time_expression": "time expression or null", "location": "location or null", "entities": ["entity1", ...] }} ``` Return ONLY JSON, no other content. 32 Rethinking How to Remember: Beyond Atomic Fa...

    work page

  23. [23]

    Not mentioned

    INFER from the evidence | NEVER say "Not mentioned" or "unknown". You MUST commit to an answer.,→

    work page

  24. [24]

    Use context + profiles to reason about personality, interests, values, behavior.,→

    work page

  25. [25]

    card game about exploding cats

    **USE WORLD KNOWLEDGE **: When the context describes something without naming it, IDENTIFY it.,→ - A game exclusive to a platform→name the platform (e.g. Xenoblade 2→ Nintendo Switch),→ - A described but unnamed product→identify it (e.g. "card game about exploding cats"→Exploding Kittens),→ - A described technique→name it (e.g. "work 25 minutes then break...

    work page

  26. [26]

    Pay attention to WHO said/did things | speaker tags are authoritative

    work page

  27. [27]

    ALWAYS cite specific facts from profiles/context in your reasoning | never guess generically.,→

    work page

  28. [28]

    If the question asks what someone ELSE would say about a person, check [How Others Describe Them] in profiles.,→

    work page

  29. [29]

    If the question asks about religion/spirituality, check [Beliefs/Spirituality] in profiles.,→

    work page

  30. [30]

    adopting children

    Prefer the MOST SPECIFIC reason from profiles (e.g. "adopting children" beats "she is settled").,→ ANSWER FORMAT | BE MAXIMALLY CONCISE: - The "answer" field must be as SHORT as possible. All reasoning goes in the "reasoning" field ONLY.,→ - For yes/no questions ("would", "does", "is", "can", "did", "are", "was"): * If evidence is DIRECT: answer "Yes" or ...

    work page

  31. [31]

    It must contain highly relevant information that is topically aligned with the user's intent

    **Responsiveness to Query **: The predicted answer must directly address the specific question asked. It must contain highly relevant information that is topically aligned with the user's intent. ,→ ,→

    work page

  32. [32]

    Key Signal

    **Core Fact Preservation **: The prediction must capture the "Key Signal" or "Core Entity" from the reference. The primary subject (Who), event (What), or outcome must be factually grounded in the reference text. ,→ ,→

    work page

  33. [33]

    Even if brief, it must convey the essential message required by the question context.,→

    **Informational Utility **: The answer must provide actionable or meaningful value. Even if brief, it must convey the essential message required by the question context.,→

    work page

  34. [34]

    Afternoon

    **Acceptable Representational Variances (Robustness Protocol) **: To ensure fair evaluation of semantic meaning over syntactic rigidity, you must accept the following variations as **Valid Matches **:,→ - **Temporal & Numerical Margins **: Accept timestamps within a reasonable proximity (e.g., +/- 1-2 days due to timezone/reporting differences) and rounde...

    work page