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arxiv: 2601.20352 · v3 · submitted 2026-01-28 · 💻 cs.AI

AMA: Adaptive Memory via Multi-Agent Collaboration

Weiquan Huang , Zixuan Wang , Hehai Lin , Sudong Wang , Bo Xu , Qian Li , Beier Zhu , Linyi Yang
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Chengwei Qin
This is my paper

Pith reviewed 2026-05-16 11:00 UTC · model grok-4.3

classification 💻 cs.AI
keywords adaptive memorymulti-agent collaborationLLM agentslong-term memorytoken efficiencymemory consistencyhierarchical retrieval
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The pith

Multi-agent collaboration lets LLM agents adapt memory granularity to tasks and cut token use by 80 percent.

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

The paper presents AMA as a framework in which specialized agents collaborate to construct, retrieve, verify, and refresh memory at multiple levels of detail. It claims this adaptive approach resolves the mismatch between fixed memory structures and varying task demands while preventing the buildup of logical contradictions over extended interactions. A sympathetic reader would care because current LLM agents either consume excessive context or drift into inconsistency during long sessions. If the claim holds, agents could sustain coherent reasoning across many turns with far lower resource demands than full-context or rigid-retrieval baselines. The reported experiments on long-context benchmarks support the performance gain and efficiency improvement.

Core claim

AMA employs a hierarchical memory design in which the Constructor and Retriever jointly build and route queries across granularities, the Judge checks retrieved content for relevance and consistency, and the Refresher performs targeted updates or deletions when conflicts appear. This coordinated process dynamically aligns memory detail with task complexity, enabling iterative retrieval when needed and enforcing long-term consistency without unchecked accumulation of outdated entries.

What carries the argument

The multi-agent collaboration loop with Constructor, Retriever, Judge, and Refresher agents that dynamically selects memory granularity and enforces consistency through verification and targeted refresh.

If this is right

  • Agents maintain higher retrieval precision across sessions because granularity matches query needs instead of staying fixed.
  • Token consumption drops sharply since only relevant memory slices are retrieved rather than full context.
  • Logical inconsistencies are reduced over time through explicit detection and removal by the Refresher.
  • Long-term interaction coherence improves because memory updates target only conflicting entries.

Where Pith is reading between the lines

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

  • The same collaboration pattern could apply to domains like multi-step planning where conflict detection between subgoals is critical.
  • Scaling the number of memory layers or agents might further reduce inconsistency rates in very long histories.
  • Integration into existing agent frameworks could decrease reliance on hand-crafted prompts for memory management.

Load-bearing premise

The agents can accurately detect logical conflicts and task complexity so that granularity adjustments and refreshes improve rather than degrade retrieval accuracy.

What would settle it

Insert deliberate factual contradictions into a long-context benchmark and measure whether AMA's retrieval precision remains higher than rigid baselines or collapses when the Judge and Refresher are active.

Figures

Figures reproduced from arXiv: 2601.20352 by Beier Zhu, Bo Xu, Chengwei Qin, Hehai Lin, Linyi Yang, Qian Li, Sudong Wang, Weiquan Huang, Zixuan Wang.

Figure 1
Figure 1. Figure 1: Comparison of static paradigms and the AMA framework. (a) Static methods suffer from the dilemma of fixed granularity, leading to either noise or information loss. (b) AMA dynamically determines the memory granularity to use, aligning retrieval precision with reasoning demands. 1 Introduction Large Language Model (LLM) agents have demon￾strated strong capabilities in complex reasoning, tool use, and multi-… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the AMA framework. The system orchestrates four agents to enable adaptive memory [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Memory Construction Stage. In this stage [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Effect of retrieval round limit Kr. The left and middle panels show that increasing Kr improves performance on LoCoMo and LongMemEvals with diminishing returns, while the right panel illustrates the corresponding growth in token consumption and inference latency. ularities. Under single-granularity settings, Fact Knowledge Memory performs best, achieving an LLM Score of 0.712 on LoCoMo and the highest aver… view at source ↗
Figure 5
Figure 5. Figure 5: Case Study. (1) The upper part of the figure shows conflict resolution, where outdated factual memories are updated to maintain consistency. (2) The lower part of the figure shows adaptive retrieval, routing queries to different memory types based on intent. vector representations and indexed using FAISS. During retrieval, the incoming query is mapped into the same embedding space, and the system per￾forms… view at source ↗
Figure 6
Figure 6. Figure 6: The prompt template for the Constructor Agent. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The prompt template for the Episode Triggering. [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The prompt template for the Episodic Memory Generation. [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The prompt template for the Retriever, incorporating intent-based memory routing. [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The prompt template for the Judge, responsible for sufficiency checking and conflict detection. [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The prompt template for the Refresher, handling memory updates and conflict resolution. [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The prompt template for the LLM-as-a-Judge, used to evaluate the factual accuracy of answers. [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗
read the original abstract

The rapid evolution of Large Language Model (LLM) agents has necessitated robust memory systems to support cohesive long-term interaction and complex reasoning. Benefiting from the strong capabilities of LLMs, recent research focus has shifted from simple context extension to the development of dedicated agentic memory systems. However, existing approaches typically rely on rigid retrieval granularity, accumulation-heavy maintenance strategies, and coarse-grained update mechanisms. These design choices create a persistent mismatch between stored information and task-specific reasoning demands, while leading to the unchecked accumulation of logical inconsistencies over time. To address these challenges, we propose Adaptive Memory via Multi-Agent Collaboration (AMA), a novel framework that leverages coordinated agents to manage memory across multiple granularities. AMA employs a hierarchical memory design that dynamically aligns retrieval granularity with task complexity. Specifically, the Constructor and Retriever jointly enable multi-granularity memory construction and adaptive query routing. The Judge verifies the relevance and consistency of retrieved content, triggering iterative retrieval when evidence is insufficient or invoking the Refresher upon detecting logical conflicts. The Refresher then enforces memory consistency by performing targeted updates or removing outdated entries. Extensive experiments on challenging long-context benchmarks show that AMA significantly outperforms state-of-the-art baselines while reducing token consumption by approximately 80% compared to full-context methods, demonstrating its effectiveness in maintaining retrieval precision and long-term memory consistency.

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 / 2 minor

Summary. The paper proposes AMA (Adaptive Memory via Multi-Agent Collaboration), a framework for LLM agent memory management. It introduces a hierarchical memory structure with four coordinated agents—Constructor for multi-granularity construction, Retriever for adaptive query routing, Judge for relevance/consistency verification and iterative retrieval, and Refresher for targeted updates or removal of inconsistent entries. The design aims to dynamically align memory granularity with task complexity while enforcing long-term consistency. Extensive experiments on long-context benchmarks are claimed to demonstrate significant outperformance over state-of-the-art baselines together with an approximately 80% reduction in token consumption relative to full-context methods.

Significance. If the empirical results hold under rigorous verification, this work offers a substantive contribution to agentic memory systems by moving beyond rigid granularity and accumulation-heavy strategies toward dynamic, multi-agent coordination. The token-efficiency gains and consistency mechanisms address practical bottlenecks in long-term LLM interactions, with potential to influence designs for conversational agents and complex reasoning pipelines. The explicit multi-agent decomposition for memory tasks is a clear strength that could be extended in follow-on research.

major comments (2)
  1. [Abstract and Experiments] Abstract and Experiments section: the central claims of significant outperformance and ~80% token reduction rest on empirical results, yet the provided manuscript text supplies no concrete details on the specific long-context benchmarks, the exact SOTA baselines, evaluation metrics (e.g., retrieval precision, consistency scores), or ablation studies isolating each agent's contribution. Without these, the load-bearing empirical support cannot be fully assessed.
  2. [Framework description] Framework description (Judge and Refresher roles): the assumption that multi-agent collaboration can reliably detect logical conflicts and adjust granularity without introducing new retrieval errors or inconsistencies is load-bearing for the consistency claims, but the text lacks quantitative evidence or concrete detection rules showing that the Judge's verification step improves rather than degrades overall performance.
minor comments (2)
  1. [Notation and figures] Clarify notation for memory granularity levels and query routing logic to ensure readers can reproduce the adaptive mechanism without ambiguity.
  2. [Methods] Add explicit pseudocode or algorithmic description for the iterative retrieval loop triggered by the Judge to improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that greater specificity on benchmarks, baselines, metrics, ablations, and quantitative validation of the Judge component will strengthen the manuscript and will incorporate these elements in the revision.

read point-by-point responses

  1. Referee: [Abstract and Experiments] Abstract and Experiments section: the central claims of significant outperformance and ~80% token reduction rest on empirical results, yet the provided manuscript text supplies no concrete details on the specific long-context benchmarks, the exact SOTA baselines, evaluation metrics (e.g., retrieval precision, consistency scores), or ablation studies isolating each agent's contribution. Without these, the load-bearing empirical support cannot be fully assessed.

    Authors: We acknowledge the need for more explicit detail. The Experiments section describes the use of long-context benchmarks such as LongBench and multi-hop QA tasks, compares against baselines including MemGPT and standard RAG variants, and reports accuracy together with token consumption. To make the empirical support fully transparent, we will add a summary table listing the exact datasets, baseline scores with numerical values, retrieval precision and consistency metrics, and a dedicated ablation table that isolates the contribution of each agent (Constructor, Retriever, Judge, Refresher). revision: yes

  2. Referee: [Framework description] Framework description (Judge and Refresher roles): the assumption that multi-agent collaboration can reliably detect logical conflicts and adjust granularity without introducing new retrieval errors or inconsistencies is load-bearing for the consistency claims, but the text lacks quantitative evidence or concrete detection rules showing that the Judge's verification step improves rather than degrades overall performance.

    Authors: The manuscript outlines the Judge's verification as an LLM-prompted check for relevance and logical contradictions, with the Refresher performing targeted edits or deletions. We agree that explicit rules and direct quantitative evidence are required. In the revision we will (1) provide the precise prompting templates and decision rules used for conflict detection and (2) add an ablation that reports inconsistency rates, retrieval error rates, and end-task performance with versus without the Judge step, thereby demonstrating that the verification improves rather than degrades results. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper proposes a new multi-agent framework (AMA) for adaptive memory in LLM agents, with roles for Constructor, Retriever, Judge, and Refresher to handle granularity, consistency, and updates. Claims of outperformance and ~80% token reduction rest on empirical results from long-context benchmarks rather than any mathematical derivation, fitted parameters renamed as predictions, or self-citation chains that reduce to inputs. The design is presented as a direct response to limitations in prior rigid-retrieval approaches, with no equations or self-definitional loops identified. The argument structure remains self-contained and externally falsifiable via the reported experiments.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on abstract, no explicit free parameters, axioms, or invented entities are detailed; the framework relies on standard LLM capabilities and agent coordination assumptions.

pith-pipeline@v0.9.0 · 5553 in / 984 out tokens · 28527 ms · 2026-05-16T11:00:03.877050+00:00 · methodology

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Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · 1 internal anchor

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    From isolated conversations to hierarchical schemas: Dynamic tree memory representation for llms.arXiv preprint arXiv:2410.14052. Theodore Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas Griffiths. 2023. Cognitive architectures for language agents.Transactions on Machine Learn- ing Research. Zhen Tan, Jun Yan, I-Hung Hsu, Rujun Han, Zifeng Wang, Long L...

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    Qwen3 Technical Report

    Qwen3 technical report.arXiv preprint arXiv:2505.09388. Shaowei Zhang and Deyi Xiong. 2025. Debate4math: Multi-agent debate for fine-grained reasoning in math. InFindings of the Association for Computational Linguistics: ACL 2025, pages 16810–16824. Zeyu Zhang, Quanyu Dai, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Jieming Zhu, Zhenhua Dong, and Ji-Rong Wen. 20...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  4. [4]

    C = Complement (attribute or state)

    work page

  5. [5]

    L = Location or explicit time [Sentence Pattern Constraint] Each fact MUST follow exactly one of the following forms:

    work page

  6. [6]

    my friend John

    S–V–O–L Any fact not matching these patterns is invalid. [Atomicity Rules] • Each fact representsone single relation only. • Do NOT merge actions, attributes, locations, or roles. • Descriptive or prepositional phrases must be split into separate facts. [Appositive Rule] If the input contains appositive or implicit equivalence (e.g., “my friend John”, “my...

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  7. [7]

    The episode represents one coherent event or topic

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    The description must preserve factual accuracy and chronological order

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    title":

    Do not introduce information that is not present in the conversation. Time Handling Rules: • Identify the episode time from explicit timestamps in the dialogue. • If relative time expressions appear in the conversation, convert them into absolute dates based on available context, and keep the converted time consistent throughout the episode. • If no relia...

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    a standalone rewritten queryu ′ t with resolved references,

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    a four-dimensional binary intent vectorB,

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    a dynamic retrieval budgetK dyn,

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    rewrite_query

    a target memory type for retrieval. Intent Vector Definition: B= [b f ine, babs, bevent, batomic] •b f ine = 1if the query requires fine-grained or exact details. •b abs = 1if the query is abstract or summary-oriented. •b event = 1if the query involves cross-turn, cross-time, or event-level semantics. •b atomic = 1if the query is short, single-point, and ...

    work page