arxiv: 2603.16496 · v2 · submitted 2026-03-17 · 💻 cs.CL

Recognition: 2 theorem links

· Lean Theorem

AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents

Shannan Yan , Jingchen Ni , Leqi Zheng , Jiajun Zhang , Peixi Wu , Dacheng Yin , Jing Lyu , Chun Yuan

show 1 more author

Fengyun Rao

Authors on Pith no claims yet

Pith reviewed 2026-05-15 09:43 UTC · model grok-4.3

classification 💻 cs.CL

keywords long-horizon dialogueuser-centric memoryadaptive retrievalLLM agentsmemory managementdialogue systemsgraph expansionpersonalization

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The pith

AdaMem improves long-horizon dialogue performance by organizing memory into four adaptive types and using question-conditioned retrieval with selective graph expansion.

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

The paper introduces AdaMem as a memory framework for LLM agents that must track extended conversations and user details over many turns. It stores information across working memory for recent context, episodic memory for structured experiences, persona memory for stable traits, and graph memory for relations. At inference the system identifies the relevant user, runs semantic retrieval, and adds graph links only when they are needed before synthesizing the answer through specialized steps. This design targets three problems in prior systems: over-reliance on similarity that misses key user facts, fragmented storage that breaks coherence, and fixed memory sizes that do not match question demands. Experiments on two benchmarks show the approach reaches state-of-the-art results for long-horizon reasoning and user modeling.

Core claim

AdaMem organizes dialogue history into working, episodic, persona, and graph memories within a single framework. At inference it first resolves the target participant, constructs a question-conditioned retrieval route that starts with semantic retrieval and adds relation-aware graph expansion only when needed, then produces the final answer through a role-specialized pipeline for evidence synthesis and response generation. The method achieves state-of-the-art performance on the LoCoMo and PERSONAMEM benchmarks for long-horizon reasoning and user modeling.

What carries the argument

The question-conditioned retrieval route that combines semantic similarity search with selective relation-aware graph expansion, backed by four distinct memory stores and a role-specialized synthesis pipeline.

If this is right

Related experiences remain linked through graph structure, preserving temporal and causal coherence that isolated fragments lose.
Retrieval adapts to each question by expanding the graph only when semantic matches alone are insufficient.
Different memory types supply evidence at the right granularity for recent context versus long-term traits.
Role-specialized synthesis steps reduce the chance that mixed evidence produces off-target responses.
State-of-the-art results appear on both long-horizon reasoning and user-modeling benchmarks.

Where Pith is reading between the lines

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

The same conditional retrieval pattern could be applied to agent planning tasks that require consistent memory across multiple steps.
Selective graph expansion might reduce noise in domains where user relationships form dense but sparse subgraphs.
Modular role-specialized pipelines could be reused in multi-agent systems to keep synthesis logic separate from retrieval.
The four-type memory split suggests a general template for balancing recency, episodicity, stability, and connectivity in other long-context applications.

Load-bearing premise

The assumption that adding selective graph expansion and role-specialized synthesis will surface user-centric evidence that pure similarity misses without introducing new coherence or relevance errors.

What would settle it

A controlled test set of long-horizon questions where the full AdaMem pipeline produces lower accuracy or more inconsistent user modeling than a version that uses only semantic retrieval without graph expansion.

Figures

Figures reproduced from arXiv: 2603.16496 by Chun Yuan, Dacheng Yin, Fengyun Rao, Jiajun Zhang, Jingchen Ni, Jing Lyu, Leqi Zheng, Peixi Wu, Shannan Yan.

**Figure 1.** Figure 1: Comparison of previous methods and AdaMem. Conventional approaches rely on fixedlength chunks or coarse summaries with semantic retrieval, while AdaMem emphasizes user-centric adaptive structured memories and multi-agent collaborative retrieval. are inherently long-horizon: an agent must continually accumulate information across many turns, preserve salient details as user goals evolve, and recover the… view at source ↗

**Figure 2.** Figure 2: Model overview. Dialogue history is organized into working, episodic, persona, and graph memories, and question answering is performed through target-aware, question-conditioned retrieval and role-specialized evidence synthesis. oriented persona descriptors, while clustered attributes are merged into aspect-based persona summaries. In parallel, both message-level and consolidated records are indexed int… view at source ↗

**Figure 3.** Figure 3: Ablation on key hyperparameters. porting evidence has already been retrieved. We therefore use K = 10 as the default setting, as it achieves nearly the best performance while avoiding the additional latency and noise introduced by a larger evidence set. Fixing K = 10, we vary Li ∈ {1, 2, 3}. Both F1 and BLEU-1 peak at Li = 2. Using only 1 iteration is often insufficient for questions that require decompo… view at source ↗

**Figure 4.** Figure 4: Case One (Success). associated with Caroline, her childhood, and the father-related activity. During inference, target resolution narrows retrieval to Caroline’s memory bundle, while topic-to-message recovery and relationaware evidence aggregation restore the exact supporting utterance instead of only a vague semantic neighbor. The case therefore illustrates the central advantage of AdaMem: it succeeds… view at source ↗

**Figure 5.** Figure 5: Case Two (Failure). such as Melanie having read an inspiring book “last year,” yet fail to store a stable symbolic binding between the book title and the absolute year required by the question. Once that canonical link is absent from memory, the Research Agent cannot recover it through additional search alone, and the Working Agent correctly defaults to abstention. This example suggests that the next bot… view at source ↗

read the original abstract

Large language model (LLM) agents increasingly rely on external memory to support long-horizon interaction, personalized assistance, and multi-step reasoning. However, existing memory systems still face three core challenges: they often rely too heavily on semantic similarity, which can miss evidence crucial for user-centric understanding; they frequently store related experiences as isolated fragments, weakening temporal and causal coherence; and they typically use static memory granularities that do not adapt well to the requirements of different questions. We propose AdaMem, an adaptive user-centric memory framework for long-horizon dialogue agents. AdaMem organizes dialogue history into working, episodic, persona, and graph memories, enabling the system to preserve recent context, structured long-term experiences, stable user traits, and relation-aware connections within a unified framework. At inference time, AdaMem first resolves the target participant, then builds a question-conditioned retrieval route that combines semantic retrieval with relation-aware graph expansion only when needed, and finally produces the answer through a role-specialized pipeline for evidence synthesis and response generation. We evaluate AdaMem on the LoCoMo and PERSONAMEM benchmarks for long-horizon reasoning and user modeling. Experimental results show that AdaMem achieves state-of-the-art performance on both benchmarks. The code will be released upon acceptance.

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

AdaMem gives a clean four-way memory split plus conditional graph expansion for dialogue agents, but the SOTA claims sit on missing ablations and implementation details.

read the letter

The paper's core idea is to split memory into working, episodic, persona, and graph stores so recent context, long experiences, stable user traits, and relations stay separate but accessible. At inference it resolves the participant first, then runs question-conditioned semantic retrieval and adds graph expansion only when needed, before handing off to role-specialized synthesis. That conditional route is the part that feels new compared with earlier memory systems that either stay flat or always expand everything.

Referee Report

3 major / 2 minor

Summary. The paper proposes AdaMem, an adaptive user-centric memory framework for LLM-based long-horizon dialogue agents. It organizes dialogue history into four memory types (working, episodic, persona, and graph) to address limitations of pure semantic similarity retrieval, fragmented storage that breaks temporal/causal links, and static memory granularities. At inference, the system resolves the target participant, constructs a question-conditioned retrieval route that combines semantic retrieval with selective relation-aware graph expansion, and generates responses via a role-specialized synthesis pipeline. The central claim is that this yields state-of-the-art performance on the LoCoMo and PERSONAMEM benchmarks.

Significance. If the results hold under rigorous validation, AdaMem would represent a meaningful step toward more reliable long-term user modeling in dialogue agents by selectively augmenting similarity-based retrieval with structured relational expansion while preserving coherence. This could influence downstream work on personalized agents and memory-augmented reasoning.

major comments (3)

[Experimental Results] Experimental Results section: The SOTA claim on LoCoMo and PERSONAMEM is presented without ablations that isolate the selective graph expansion component from pure semantic retrieval or from the role-specialized synthesis step. This is load-bearing for the central claim that the combined route captures user-centric evidence missed by similarity alone without introducing coherence or relevance errors.
[Method / Inference-time Route] Inference-time retrieval route description: No explicit decision rule, threshold, or condition is given for when relation-aware graph expansion is triggered versus skipped. This omission directly affects the weakest assumption that the adaptive mechanism reliably improves over baseline similarity retrieval on long-horizon dialogues.
[Abstract / Experiments] Abstract and Experiments: The manuscript reports SOTA results but supplies no quantitative metrics, error analysis, or comparison tables in the provided summary; without these, the robustness of the performance advantage cannot be assessed.

minor comments (2)

[Memory Organization] A summary table comparing the four memory types (working, episodic, persona, graph) on dimensions such as update frequency, retrieval method, and intended use case would improve clarity.
[Experiments] Ensure that all benchmark-specific metrics and baseline implementations are described with sufficient detail for reproducibility, including any prompt templates used in the role-specialized synthesis.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, providing clarifications from the full paper and committing to revisions that strengthen the experimental validation and method transparency without misrepresenting our contributions.

read point-by-point responses

Referee: [Experimental Results] Experimental Results section: The SOTA claim on LoCoMo and PERSONAMEM is presented without ablations that isolate the selective graph expansion component from pure semantic retrieval or from the role-specialized synthesis step. This is load-bearing for the central claim that the combined route captures user-centric evidence missed by similarity alone without introducing coherence or relevance errors.

Authors: We agree that explicit ablations isolating the selective graph expansion and role-specialized synthesis are necessary to substantiate the central claim. The current experiments compare AdaMem against strong baselines that use pure semantic retrieval, but we will add new ablation variants in the revised manuscript: one disabling graph expansion (relying only on semantic retrieval) and one disabling the role-specialized synthesis pipeline. These will quantify incremental gains and confirm no coherence degradation occurs. revision: yes
Referee: [Method / Inference-time Route] Inference-time retrieval route description: No explicit decision rule, threshold, or condition is given for when relation-aware graph expansion is triggered versus skipped. This omission directly affects the weakest assumption that the adaptive mechanism reliably improves over baseline similarity retrieval on long-horizon dialogues.

Authors: The decision rule is implemented via the participant resolver combined with a lightweight query classifier that triggers graph expansion for queries involving relational, causal, or cross-event reasoning (e.g., detected via keywords and intent patterns). We will revise the Inference-time Route subsection to include an explicit algorithmic description and pseudocode detailing the exact conditions under which expansion is applied versus skipped. revision: yes
Referee: [Abstract / Experiments] Abstract and Experiments: The manuscript reports SOTA results but supplies no quantitative metrics, error analysis, or comparison tables in the provided summary; without these, the robustness of the performance advantage cannot be assessed.

Authors: The full manuscript (Section 4) includes quantitative metrics, comparison tables against baselines on both LoCoMo and PERSONAMEM, and initial error breakdowns. To improve accessibility, we will update the abstract with key numerical results and expand the Experiments section with a dedicated error analysis subsection in the revision. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical framework with benchmark results

full rationale

The paper introduces AdaMem as a new memory organization (working/episodic/persona/graph) and inference pipeline (participant resolution + conditional semantic+graph retrieval + role synthesis). No equations, fitted parameters, or derivations appear that reduce performance claims to self-defined quantities or author prior work. SOTA results are stated as direct outcomes of evaluation on the external LoCoMo and PERSONAMEM benchmarks, with no self-citation chains or ansatz smuggling invoked to justify the core mechanism. This is a standard empirical systems paper whose central claims rest on reported benchmark numbers rather than any closed derivation loop.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the premise that dialogue history can be cleanly partitioned into working, episodic, persona, and graph memories and that a question-conditioned selector can decide when graph expansion adds value without introducing noise.

axioms (1)

domain assumption LLM agents benefit from explicit separation of recent context, long-term events, stable traits, and relational structure
Invoked in the description of the four memory types in the abstract.

invented entities (1)

question-conditioned retrieval route no independent evidence
purpose: Decides when to add relation-aware graph expansion to semantic retrieval
New control mechanism introduced to address static granularity problems.

pith-pipeline@v0.9.0 · 5546 in / 1177 out tokens · 19047 ms · 2026-05-15T09:43:15.956846+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

AdaMem organizes dialogue history into working, episodic, persona, and graph memories... combines semantic retrieval with relation-aware graph expansion only when needed
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We propose AdaMem, an adaptive user-centric memory framework... achieving state-of-the-art performance on both benchmarks

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems
cs.AI 2026-05 unverdicted novelty 7.0

Goal-Mem improves RAG memory retrieval in agentic LLMs by explicit goal decomposition and backward chaining via Natural Language Logic, outperforming nine baselines on multi-hop and implicit inference tasks.

Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages · cited by 1 Pith paper · 2 internal anchors

[1]

InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13851– 13870

Evaluating very long-term conversational memory of llm agents. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13851– 13870. Kai Mei, Xi Zhu, Wujiang Xu, Mingyu Jin, Wenyue Hua, Zelong Li, Shuyuan Xu, Ruosong Ye, Yingqiang Ge, and Yongfeng Zhang. 2025. Aios: Llm agent operating syste...

work page 2025
[2]

MemGPT: Towards LLMs as Operating Systems

Memgpt: Towards llms as operating systems. arXiv preprint arXiv:2310.08560. Preston Rasmussen, Pavlo Paliychuk, Travis Beauvais, Jack Ryan, and Daniel Chalef. 2025. Zep: a tempo- ral knowledge graph architecture for agent memory. arXiv preprint arXiv:2501.13956. Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-n...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[3]

Qwen3 Technical Report

Qwen3 technical report.arXiv preprint arXiv:2505.09388. Yanwei Yue, Guibin Zhang, Boyang Liu, Guancheng Wan, Kun Wang, Dawei Cheng, and Yiyan Qi. 2025. Masrouter: Learning to route llms for multi-agent systems. InProceedings of the 63rd Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers). Shaokun Zhang, Ming Yin, Jiey...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[4]

Identify one primary topic/event in the message.,→

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[5]

Infer the author's attitude toward the event

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[6]

Infer the reason behind the attitude

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[7]

Extract facts or events revealed by the message.,→

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Extract user attributes revealed by the message.,→

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[9]

text": "original message

Produce a one-sentence summary and a brief rationale.,→ Return JSON: { "text": "original message", "tags": { "topic": ["..."], "attitude": ["Positive|Negative|Mixed"], "reason": ["..."], "facts": ["..."], "attributes": ["..."] }, "summary": "...", "rationale": "..." } Message: "{message}" E.2 Episodic Memory Router Prompts Role.After message understanding...

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[10]

Only merge topics that talk about the same underlying event/theme.,→

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Preserve the original meaning and context of each topic.,→

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[12]

Extract as many common details as possible when naming the merged topic.,→

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Do not reveal the user's attitude in the merged topic name.,→

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topic1":

Keep distinct concepts separate. Input: { "topic1": "summary sentence 1", "topic2": "summary sentence 2", ... } Return JSON: { "Grouped Topics": { "NewTopicName1": ["original_topic1", "original_topic2"],,→ "NewTopicName2": ["original_topic3"] }, "Grouping Rationale": "Explanation of the grouping",→ } E.4 Route Refinement Prompt Role.This prompt is used by...

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[15]

Keep useful, on-topic information from CURRENT RESULT.,→

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[16]

Add new, relevant, well-supported facts from EVIDENCE.,→

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[17]

Remove off-topic content

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[18]

Prefer concrete details such as entities, dates, numbers, and events.,→

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[19]

Resolve contradictions by preferring more specific or more recent evidence.,→

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[20]

content":

Use timestamps when answering temporal questions.,→ Return JSON: { "content": "merged factual summary", "sources": ["source-1", "source-2", ...] } [Info Check Prompt] You are the InfoCheckAgent. Judge whether the collected information is,→ sufficient to answer the QUESTION. QUESTION: {question} RESULT: {result} Return JSON: { "enough": true | false } [Fol...

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[21]

Identify what is still missing

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[22]

Generate 1-3 targeted retrieval queries

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new_requests

Mention concrete entities or events whenever possible.,→ Return JSON: { "new_requests": ["query-1", "query-2", ...] } E.6 Working Agent Answer Prompt Role.This prompt is used by the Working Agent to convert the research summary into the final answer. It explicitly encourages concise, entity- centric responses and injects additional persona attributes or f...

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