arxiv: 2604.17265 · v2 · submitted 2026-04-19 · 💻 cs.IR

Recognition: unknown

MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search

Sheng Zhang , Junyi Li , Yingyi Zhang , Pengyue Jia , Yichao Wang , Xiaowei Qian , Wenlin Zhang , Maolin Wang

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Yong Liu Xiangyu Zhao

Authors on Pith no claims yet

Pith reviewed 2026-05-13 07:22 UTC · model grok-4.3

classification 💻 cs.IR

keywords agentic searchmemory managementmemory dilutionlarge language modelsreasoningmemory growthretrievaltoken-level memory

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The pith

MemSearch-o1 grows fine-grained memory fragments from query seed tokens and retraces them into connected paths to reduce dilution in agentic search.

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

The paper presents MemSearch-o1 as a way to stop memory dilution that builds up when large language models run repeated think-search loops on complex queries. Instead of appending new information in a simple stream, the method starts from seed tokens taken from the query, grows small structured memory pieces, scores their relevance with a contribution function, and then links everything into one coherent path. This change aims to keep fine details between queries and retrieved documents intact while letting the model reason more effectively over longer sessions. Readers would care because current agentic systems often lose critical context after many steps, limiting how far they can plan or retrieve. Tests on eight benchmark datasets are reported to show clearer gains in reasoning activation across different LLMs.

Core claim

MemSearch-o1 shifts memory management from stream-like concatenation to structured, token-level growth with path-based reasoning: it dynamically grows fine-grained memory fragments from memory seed tokens drawn from the queries, retraces and refines those fragments via a contribution function, and finally reorganizes them into a globally connected memory path.

What carries the argument

Reasoning-aligned memory growth that builds and retraces fine-grained fragments from query seed tokens using a contribution function to form connected paths.

If this is right

Agentic search loops can run for more iterations while preserving query-document relations.
Diverse LLMs gain better access to their own reasoning capacity once memory stays structured rather than diluted.
Memory handling moves from linear appending to path reorganization that supports global coherence.
The framework supplies a concrete base for building memory-aware agentic systems.
Fine-grained token-level growth reduces the information loss that occurs in long iterative retrieval sequences.

Where Pith is reading between the lines

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

The same growth-and-retrace pattern could be applied to multi-turn conversational agents to keep context coherent without repeated full-context resets.
Integration with graph databases might let the reorganized paths serve as explicit knowledge graphs for downstream verification.
Longer-horizon tasks such as multi-step planning or tool use would become more reliable if the contribution function can be tuned to prioritize causal links over simple relevance.
The approach suggests that memory dilution is partly an artifact of concatenation order rather than an inevitable limit of context windows.

Load-bearing premise

Dynamically growing and retracing fine-grained memory fragments from query seed tokens will capture semantic relations and lose less information than prior stream-like concatenation methods.

What would settle it

If side-by-side runs on the same eight benchmarks show no measurable drop in memory dilution or no rise in reasoning accuracy compared with standard stream-based memory, the central claim would be falsified.

Figures

Figures reproduced from arXiv: 2604.17265 by Junyi Li, Maolin Wang, Pengyue Jia, Sheng Zhang, Wenlin Zhang, Xiangyu Zhao, Xiaowei Qian, Yichao Wang, Yingyi Zhang, Yong Liu.

**Figure 2.** Figure 2: Model Size Scaling on MemSearch-o1 approach with reasoning tasks. In contrast, Amber shows delayed activation, with noticeable improvements only beyond the 7B scale, and still lags behind MemSearch-o1 in overall performance. Search-o1 (Refined), meanwhile, exhibits unstable behavior: after an initial boost, its performance fluctuates on larger models and fails to scale consistently. This instability re… view at source ↗

**Figure 3.** Figure 3: Top-k Scalability for MemSearch-o1. useful context and ultimately reducing accuracy while increasing the number of search rounds. By contrast, in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Memory Paths for Different Retrieval Cases. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: Average Tokens for Generation. A.5 Average Tokens for Generation In this subsection, we show the average tokens for generating answers across all baseline methods. The experiments are conducted on HotpotQA and 2WikiMQA, using the DeepSeek-V3.1 backbone. We limit the maximum number of search iterations to three. The results are displayed in [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

read the original abstract

Recent advances in large language models (LLMs) have scaled the potential for reasoning and agentic search, wherein models autonomously plan, retrieve, and reason over external knowledge to answer complex queries. However, the iterative think-search loop accumulates long system memories, leading to memory dilution problem. In addition, existing memory management methods struggle to capture fine-grained semantic relations between queries and documents and often lose substantial information. Therefore, we propose MemSearch-o1, an agentic search framework built on reasoning-aligned memory growth and retracing. MemSearch-o1 dynamically grows fine-grained memory fragments from memory seed tokens from the queries, then retraces and deeply refines the memory via a contribution function, and finally reorganizes a globally connected memory path. This shifts memory management from stream-like concatenation to structured, token-level growth with path-based reasoning. Experiments on eight benchmark datasets show that MemSearch-o1 substantially mitigates memory dilution, and more effectively activates the reasoning potential of diverse LLMs, establishing a solid foundation for memory-aware agentic intelligence.

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

MemSearch-o1 describes a token-level memory growth plus retracing scheme for agentic search but rests its claims on experiments that are not shown in the abstract.

read the letter

The paper's main contribution is a concrete mechanism that starts memory fragments from query seed tokens, applies a contribution function to retrace and refine them, and then reorganizes everything into a single global path. This is distinct from standard concatenation or retrieval-augmented memory and directly targets the dilution that builds up in repeated think-search cycles. The framing of the problem is clear and the design choices line up with the stated goal of keeping fine-grained semantic links intact.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes MemSearch-o1, an agentic search framework for LLMs that addresses memory dilution in iterative think-search processes. It introduces reasoning-aligned memory growth, where fine-grained memory fragments are dynamically grown from query seed tokens, followed by retracing using a contribution function and reorganization into a globally connected memory path. This approach is positioned as superior to stream-like concatenation for capturing fine-grained semantic relations. The paper claims that experiments on eight benchmark datasets demonstrate substantial mitigation of memory dilution and more effective activation of reasoning potential in diverse LLMs.

Significance. If the results are substantiated, this work could have notable significance in the field of agentic AI and LLM-based search systems. By shifting memory management to a structured, token-level growth and path-based reasoning paradigm, it offers a potential solution to a key limitation in long-context agentic interactions. The framework establishes a foundation for memory-aware agentic intelligence, which may influence future designs of autonomous reasoning agents.

major comments (2)

[Abstract] Abstract: The central empirical claim that MemSearch-o1 'substantially mitigates memory dilution' and 'more effectively activates the reasoning potential' on eight benchmark datasets is presented without any quantitative results, specific metrics, baselines, error bars, or ablation details. This absence is load-bearing for the paper's contribution, as the soundness of the method rests entirely on these unverified assertions.
[Method] Method: The 'contribution function' for retracing memory fragments and the 'path reorganization' step are described only at a conceptual level with no formal definition, pseudocode, or mathematical formulation. This makes it impossible to verify how the approach captures fine-grained semantic relations better than stream-like methods or to assess its parameter-free status.

minor comments (1)

[Abstract] Abstract: The invented term 'reasoning-aligned memory growth' is used without an immediate definition or citation to related memory management literature.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below and agree that revisions are needed to strengthen the presentation of our empirical claims and methodological details.

read point-by-point responses

Referee: [Abstract] Abstract: The central empirical claim that MemSearch-o1 'substantially mitigates memory dilution' and 'more effectively activates the reasoning potential' on eight benchmark datasets is presented without any quantitative results, specific metrics, baselines, error bars, or ablation details. This absence is load-bearing for the paper's contribution, as the soundness of the method rests entirely on these unverified assertions.

Authors: We agree that the abstract would benefit from concrete quantitative highlights to support the claims. In the revised version, we will incorporate key results such as average performance gains across the eight benchmarks (e.g., accuracy improvements over baselines like standard RAG and stream-concatenation methods), specific metrics used, and a brief mention of ablations demonstrating the contribution of each component. This will make the abstract self-contained while remaining within length limits. revision: yes
Referee: [Method] Method: The 'contribution function' for retracing memory fragments and the 'path reorganization' step are described only at a conceptual level with no formal definition, pseudocode, or mathematical formulation. This makes it impossible to verify how the approach captures fine-grained semantic relations better than stream-like methods or to assess its parameter-free status.

Authors: We acknowledge that the current description of the contribution function and path reorganization remains at a high level. We will revise the Method section to include a precise mathematical formulation of the contribution function (including how it scores token-level fragments based on reasoning alignment), pseudocode for the full retracing and reorganization pipeline, and an explicit statement confirming the parameter-free nature with justification. These additions will clarify the advantages over stream-like concatenation in capturing semantic relations. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework is an independent design proposal

full rationale

The paper introduces MemSearch-o1 as a new agentic search framework that grows fine-grained memory fragments from query seed tokens, applies a contribution function for retracing, and reorganizes paths. No equations, fitted parameters, or self-referential derivations appear in the provided abstract or implied full text. The central claims rest on experimental results across eight benchmarks rather than any mathematical chain that reduces to its own inputs by construction. This matches the reader's assessment of an independent design without reduction to self-defined quantities, yielding a score of 0 with no load-bearing self-citations or ansatzes identified.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central claim depends on the unproven effectiveness of the newly introduced memory growth and contribution mechanisms; no independent evidence or prior validation is referenced in the abstract.

axioms (2)

domain assumption Iterative think-search loops in agentic systems accumulate long memories that suffer dilution.
Stated directly as the core problem motivating the work.
domain assumption Existing memory management methods fail to capture fine-grained semantic relations between queries and documents.
Presented as the limitation that the new approach overcomes.

invented entities (2)

reasoning-aligned memory growth no independent evidence
purpose: Dynamically grow fine-grained memory fragments from query seed tokens
Core new mechanism introduced to replace stream-like concatenation.
contribution function no independent evidence
purpose: Retrace and deeply refine memory fragments
Invented component for deciding refinement during retracing.

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Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages · 2 internal anchors

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