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arxiv: 2606.13177 · v1 · pith:273CIQBHnew · submitted 2026-06-11 · 💻 cs.CL · cs.AI· cs.LG

MemRefine: LLM-Guided Compression for Long-Term Agent Memory

Minjae Kim , Jinheon Baek , Soyeong Jeong , Sung Ju Hwang This is my paper

Pith reviewed 2026-06-27 06:51 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords long-term memoryagent memorymemory compressionLLM agentsbudgeted memory managementconversation benchmarksmemory management
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The pith

LLM-guided compression trims long-term agent memory stores to any fixed budget while keeping downstream performance intact.

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

The paper targets the unbounded growth of memory in LLM agents during extended interactions, where redundant entries raise storage costs and impair retrieval. It frames storage-budgeted memory management as the task of shrinking an existing memory store to a preset limit without discarding information needed for future tasks. MemRefine solves this by letting surface similarity only nominate candidate pairs and then using an LLM to judge factual content for delete, merge, or preserve actions, repeating until the budget is reached. Experiments across memory frameworks and long-term conversation benchmarks show the method meets budgets, holds task performance steady, and beats rule-based alternatives when space is scarce. Readers would care because practical agents on constrained hardware need reliable ways to retain useful history without endless expansion.

Core claim

MemRefine is an LLM-guided framework for storage-budgeted memory management that, because surface similarity poorly reflects factual value, uses similarity only to propose candidate pairs and defers delete, merge, and preserve decisions to an LLM judge based on factual content, iterating until the budget is met. Across multiple memory frameworks and long-term conversation benchmarks, this approach consistently meets target budgets while preserving downstream performance and outperforming rule-based baselines under tight budgets.

What carries the argument

MemRefine, an iterative process that proposes candidate pairs by similarity then delegates factual delete/merge/preserve decisions to an LLM judge.

If this is right

  • Agent memory stores can be kept inside hard resource limits without measurable loss on future tasks.
  • The same compression method applies across different existing memory frameworks.
  • Rule-based compression is outperformed when budgets are especially tight.
  • Long-term agent operation becomes feasible on platforms with fixed memory ceilings.

Where Pith is reading between the lines

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

  • If the LLM judge remains consistent across many iterations, the same candidate-plus-judge pattern could compress other expanding stores such as retrieval-augmented knowledge bases.
  • Cloud deployments of agents could lower per-user memory costs by enforcing budgets without retraining the underlying models.
  • A verification layer that re-checks merged facts after compression might reduce any accumulated judgment drift the paper does not test.

Load-bearing premise

LLM judgments of factual content and value are reliable enough to avoid introducing errors or biases that degrade downstream task performance.

What would settle it

On a standard long-term conversation benchmark, compare downstream task accuracy using MemRefine-compressed memory versus the original uncompressed memory at the same budget size; lower accuracy with MemRefine would falsify the preservation claim.

Figures

Figures reproduced from arXiv: 2606.13177 by Jinheon Baek, Minjae Kim, Soyeong Jeong, Sung Ju Hwang.

Figure 1
Figure 1. Figure 1: Conceptual overview of storage-budgeted memory management. As long-term interactions ac￾cumulate, agent memory grows without bound and fills with redundant entries, inflating storage cost. MEMRE￾FINE refines the already constructed store under a fixed storage budget, keeping it compact while preserving information useful for future retrieval. the agent continues to interact, the memory store grows without … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the MEMREFINE pipeline. Given an already constructed memory store, MEMREFINE proposes semantically similar memory pairs and lets an LLM judge delete redundant, merge complementary, or preserve distinct memories, iterating until the store meets the storage budget. since an entry may appear marginal in isolation yet become indispensable when combined with an￾other; conversely, an entry may look h… view at source ↗
Figure 3
Figure 3. Figure 3: Category-wise F1 change under MEMREFINE compression on LoCoMo, measured against the uncom￾pressed Base Memory. Positive values indicate improvement. 100 70 60 50 40 30 Storage Budget (%) 0.32 0.34 0.36 0.38 0.40 F1 Graph-Memory Baselines MemRefine RuleSim RulePR [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of MEMREFINE with rule-based baselines (RULESIM and RULEPR) on A-MEM-style graph memory across storage budgets. flat even as a large share of entries is removed, suggesting that constructed memory stores carry re￾dundant or fragmented entries that can be removed or consolidated without losing answer-bearing con￾tent. The scaled LoCoMo-style settings provide a harder test, since longer conversati… view at source ↗
Figure 5
Figure 5. Figure 5: Effect of the compression model: MEMREFINE with GPT-5-mini versus the open Qwen3-8B as judge and merge model across storage budgets. the budget is loose, where most decisions involve obvious near-duplicates, but they fall off far more sharply as the budget tightens. This is expected: un￾der aggressive compression the difficult cases are no longer near-duplicate nodes but semantically re￾lated memories whos… view at source ↗
Figure 6
Figure 6. Figure 6: Storage-utility trade-off of MEMREFINE on the scaled LoCoMo-style datasets, with 3x and 10x longer conversations than standard LoCoMo. 6 Conclusion In this paper, we proposed MEMREFINE, a post￾construction memory compression framework de￾signed to keep an already constructed long-term agent memory store within a fixed storage bud￾get while preserving information useful for future interactions. Through a pa… view at source ↗
read the original abstract

Large language model (LLM) agents are increasingly expected to operate over long-term interactions, where information from past dialogues must be preserved and recalled to support future tasks. However, as interactions accumulate, the memory store grows without bound and fills with redundant entries that inflate storage cost and degrade retrieval by crowding out the most useful evidence. Furthermore, this is especially limiting on resource-constrained platforms with hard memory budgets, motivating us to formulate storage-budgeted memory management, the task of keeping an already constructed memory store within a fixed budget while preserving information useful for future interactions. To this end, we then propose MemRefine, an LLM-guided framework that, since surface similarity poorly reflects factual value, uses similarity only to propose candidate pairs and defers delete, merge, and preserve decisions to an LLM judge based on factual content, iterating until the budget is met. Across multiple memory frameworks and long-term conversation benchmarks, MemRefine consistently meets target budgets while preserving downstream performance and outperforming rule-based baselines under tight budgets.

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 paper proposes MemRefine, an LLM-guided compression framework for storage-budgeted memory management in long-term LLM agents. It uses surface similarity only to propose candidate pairs for compression and defers all delete/merge/preserve decisions to an LLM judge based on factual content and value, iterating until a target memory budget is met. The central claim is that this approach consistently meets budgets across multiple memory frameworks and long-term conversation benchmarks while preserving downstream task performance and outperforming rule-based baselines under tight budgets.

Significance. If the LLM judge decisions prove reliable, the work addresses a practical bottleneck in scaling LLM agents to extended interactions by providing a method to control memory growth without uniform degradation. The use of LLM reasoning over factual value rather than heuristics is a plausible direction, though its impact depends on empirical validation of judge accuracy.

major comments (2)
  1. [Abstract] Abstract: The central performance-preservation claim depends on the reliability of LLM judgments for factual delete/merge/preserve decisions, yet the provided text contains no mention of human ground-truth validation, inter-annotator agreement, or error analysis on those judgments. This is load-bearing because systematic over-merging or deletion of useful facts could leave downstream metrics unchanged only if the chosen benchmarks are insensitive to the introduced noise.
  2. [Abstract] Abstract: No error bars, dataset sizes, ablation results, or statistical significance tests are reported for the claimed consistent outperformance and budget adherence. Without these, it is impossible to assess whether the gains over rule-based baselines are robust or sensitive to particular memory frameworks and benchmarks.
minor comments (1)
  1. [Abstract] Abstract: The phrasing 'since surface similarity poorly reflects factual value' is presented as a premise without supporting citation or preliminary evidence in the visible text; a brief justification or reference would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our work. We address each major comment below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central performance-preservation claim depends on the reliability of LLM judgments for factual delete/merge/preserve decisions, yet the provided text contains no mention of human ground-truth validation, inter-annotator agreement, or error analysis on those judgments. This is load-bearing because systematic over-merging or deletion of useful facts could leave downstream metrics unchanged only if the chosen benchmarks are insensitive to the introduced noise.

    Authors: We agree that direct validation of the LLM judge is important for substantiating the central claim. The current manuscript relies on downstream task performance as an indirect indicator of judgment quality across multiple benchmarks and memory frameworks, but does not include human ground-truth evaluation or error analysis of the individual delete/merge/preserve decisions. In the revised version we will add a human evaluation study on a representative sample of LLM judgments (including inter-annotator agreement and categorization of error types) to directly assess judge reliability. revision: yes

  2. Referee: [Abstract] Abstract: No error bars, dataset sizes, ablation results, or statistical significance tests are reported for the claimed consistent outperformance and budget adherence. Without these, it is impossible to assess whether the gains over rule-based baselines are robust or sensitive to particular memory frameworks and benchmarks.

    Authors: We acknowledge that the current presentation lacks explicit error bars, dataset sizes, additional ablations, and statistical significance tests. The manuscript already evaluates across multiple memory frameworks and long-term conversation benchmarks, but we will revise the experimental section and abstract to report dataset sizes, include error bars on all performance figures, add further ablation studies on key design choices, and apply appropriate statistical tests to support the robustness claims. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper describes an LLM-guided memory compression method that proposes candidate pairs via similarity and defers all delete/merge/preserve decisions to external LLM judgments, iterating until a budget is met. No equations, fitted parameters, self-definitional constructs, or load-bearing self-citations appear in the provided text. The central procedure relies on independent external LLM calls rather than any reduction to the paper's own inputs by construction, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated or derivable from the provided text.

pith-pipeline@v0.9.1-grok · 5715 in / 1038 out tokens · 20145 ms · 2026-06-27T06:51:44.969713+00:00 · methodology

discussion (0)

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