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arxiv: 2605.00702 · v1 · submitted 2026-05-01 · 💻 cs.CL

Recognition: unknown

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory

Derong Xu , Shuochen Liu , Pengfei Luo , Pengyue Jia , Yingyi Zhang , Yi Wen , Yimin Deng , Wenlin Zhang
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Enhong Chen Xiangyu Zhao Tong Xu
Authors on Pith no claims yet

Pith reviewed 2026-05-09 19:07 UTC · model grok-4.3

classification 💻 cs.CL
keywords memory evolutionLLM agentsreinforcement learningpersonalizationtextual gradientsprocess rewardscognition-inspired
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The pith

Cognition-inspired two-stage optimization learns memory guidelines then policies for evolving LLM personalization.

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

The paper targets the challenge of maintaining consistent, evolving user preferences in LLM agents across long interactions where context windows are limited. Existing static rules lack adaptability while direct RL suffers from weak sparse rewards that destabilize learning. MemCoE addresses this by first inducing a global memory guideline from contrastive feedback treated as textual gradients, then applying that guideline to generate structured process rewards for multi-turn RL training of a memory-update policy. A sympathetic reader would care because reliable long-term memory could make personalization more stable and less dependent on hand-engineering or short-horizon signals.

Core claim

We introduce MemCoE, a cognition-inspired two-stage optimization framework that learns how memory should be organized and what information to update. In the first stage, Memory Guideline Induction optimizes a global guideline via contrastive feedback interpreted as textual gradients; in the second stage, Guideline-Aligned Memory Policy Optimization uses the induced guideline to define structured process rewards and performs multi-turn RL to learn a guideline-following memory evolution policy. We evaluate on three personalization memory benchmarks, covering explicit/implicit preference and different sizes and noise, and observe consistent improvements over strong baselines with favorable ro

What carries the argument

MemCoE's two-stage framework: Memory Guideline Induction, which derives a global memory organization rule from contrastive textual feedback, followed by Guideline-Aligned Memory Policy Optimization, which converts the guideline into process rewards to guide RL-based learning of memory update actions.

Load-bearing premise

That contrastive feedback interpreted as textual gradients can reliably induce an optimal global memory guideline, and that this guideline then supplies sufficiently informative process rewards to stabilize multi-turn RL for the memory evolution policy.

What would settle it

An ablation experiment in which the guideline induction stage is removed or replaced with random guidelines, resulting in no performance gain or greater instability during RL training on the same benchmarks, would falsify the claim that the two-stage separation is necessary for the observed improvements.

Figures

Figures reproduced from arXiv: 2605.00702 by Derong Xu, Enhong Chen, Pengfei Luo, Pengyue Jia, Shuochen Liu, Tong Xu, Wenlin Zhang, Xiangyu Zhao, Yimin Deng, Yingyi Zhang, Yi Wen.

Figure 1
Figure 1. Figure 1: Top: With a limited context window, the agent fails to capture preferences. Bottom: Inspired by Prefrontal → Hippocampus, we decouple agent memory into Memory Guideline (for organizing) → Agent Memory (for updating). user’s evolving preferences and habits (Jiang et al., 2025a,b). However, the context window prevents LLMs from retaining and exploiting the full history of dialogue (Yen et al., 2024), and sim… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our proposed MemCoE. It performs two-stage optimization for evolving user memory: (1) Memory Guideline Induction (MGI) iteratively refines a natural-language guideline; (2) Guideline-Aligned Memory Policy Optimization (GMPO) fixes the induced guideline to define guideline-aligned rewards and applies multi-turn GRPO to learn what information to update in evolving memory bank. 4.2 Guideline-Align… view at source ↗
Figure 3
Figure 3. Figure 3: Efficiency analysis on PersonaMem. We re view at source ↗
Figure 4
Figure 4. Figure 4: Retrieval Top-K on PersonaMem (32K). sults show that, across all four LLMs, both MGI vari￾ants consistently outperform the baselines (RAG and A-Mem), indicating that the learned guideline captures model-agnostic memory-update principles rather than overfitting to a specific LLM. Notably, optimizing with gpt-4o-mini generalizes strongly and achieves the best numbers on three backbones, including GPT-5 and g… view at source ↗
Figure 6
Figure 6. Figure 6: Impact of prompt quality on PersonaMem, averaged over three random runs with different seeds; error bars indicate standard deviation. 6 Conclusion Inspired by memory schema theory that highlights prefrontal regions and hippocampus regions, we present MemCoE, a two-stage optimization frame￾work that decouples how to organize memory from what to store. Specifically, MemCoE first induces a transferable, schem… view at source ↗
Figure 7
Figure 7. Figure 7: Preference retention during multi-round memory evolution on PrefEval (Explicit). We insert a user view at source ↗
Figure 8
Figure 8. Figure 8: Error analysis decomposing failures into the view at source ↗
Figure 9
Figure 9. Figure 9: Scaling analysis on PersonaMem (128K). We increase the total dialogue tokens (each evolution round view at source ↗
Figure 10
Figure 10. Figure 10: Hyperparameter analysis of optimization steps. The relative gains ( view at source ↗
Figure 11
Figure 11. Figure 11: Test set Accuracy of RL-based baselines across RL training steps. imental setting: 300 sampled PersonaMem train￾ing examples, batch size 4, and evaluation on PersonaMem-32K, varying only the number of RL update steps up to 200. As shown in view at source ↗
Figure 12
Figure 12. Figure 12: PersonaMem case study (MCQ). Our method matches the ground-truth option (d), while all baselines select different options. CASE STUDY (PersonaMem-2): Suggesting New Ideas for Music Expression (MCQ) User question: How can I find a more fulfilling way to express my love for music? Question type: suggest_new_ideas Topic: musicRecommendation Options: (a) You might consider exploring different avenues like wri… view at source ↗
Figure 13
Figure 13. Figure 13: PersonaMem case study (MCQ). Our method selects the correct option (a), whereas each baseline chooses a different option view at source ↗
Figure 14
Figure 14. Figure 14: PersonaBench case study (open-form QA). Our method outputs the correct age (39); baselines answer with missing/unknown information. CASE STUDY (PersonaBench-2): Work Location (Open-form QA) User question: What is the address of my work location? Question type: Basic information Correct answer: University Predictions: Ours: University; MemAgent: Unknown; Mem-α: Not specified; A-Mem: Not specified; LightMem… view at source ↗
Figure 15
Figure 15. Figure 15: PersonaBench case study (open-form QA). Our method recovers the correct work location (University), while baselines report missing or unknown information. mary, resolving inconsistencies and retaining the most consistent actionable points view at source ↗
Figure 16
Figure 16. Figure 16: Meta prompt for generating contrastive feedback by comparing a correct and an incorrect memory-update view at source ↗
Figure 17
Figure 17. Figure 17: Meta prompt for synthesizing multiple analysis outputs into a single consolidated feedback summary view at source ↗
Figure 18
Figure 18. Figure 18: Meta prompt for updating template_evolve using aggregated feedback while strictly preserving required placeholders and forbidding new ones (TEMPLATE_OPTIM). TEMPLATE_EVOLVE_STEP50: Evidence-Bound Memory Update from a New Chunk TEMPLATE_EVOLVE_STEP50 = """ You are given a question with options, some new memory, and previous user memory. Read the new section and update the user memory by prioritizing recent… view at source ↗
Figure 19
Figure 19. Figure 19: Meta prompt for updating the user memory profile from a newly observed dialogue chunk with view at source ↗
Figure 20
Figure 20. Figure 20: Shared prompt for final answer generation on multiple-choice benchmarks (PersonaMem, and PrefEval). view at source ↗
Figure 21
Figure 21. Figure 21: Shared prompt for final answer generation on PersonaBench, constraining outputs to only the relevant view at source ↗
read the original abstract

Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving preferences over long interactions. Existing memory systems mainly rely on static, hand-crafted update rules; although reinforcement learning (RL)-based agents learn memory updates, sparse outcome rewards provide weak supervision, resulting in unstable long-horizon optimization. Drawing on memory schema theory and the functional division between prefrontal regions and hippocampus regions, we introduce MemCoE, a cognition-inspired two-stage optimization framework that learns how memory should be organized and what information to update. In the first stage, we propose Memory Guideline Induction to optimize a global guideline via contrastive feedback interpreted as textual gradients; in the second stage, Guideline-Aligned Memory Policy Optimization uses the induced guideline to define structured process rewards and performs multi-turn RL to learn a guideline-following memory evolution policy. We evaluate on three personalization memory benchmarks, covering explicit/implicit preference and different sizes and noise, and observe consistent improvements over strong baselines with favorable robustness, transferability, and efficiency.

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

3 major / 1 minor

Summary. The manuscript proposes MemCoE, a cognition-inspired two-stage optimization framework for evolving memory in LLM agents. Stage 1 induces a global memory guideline from contrastive feedback interpreted as textual gradients. Stage 2 uses the guideline to supply structured process rewards for multi-turn RL, training a guideline-following memory evolution policy. The approach is evaluated on three personalization memory benchmarks covering explicit/implicit preferences and varying sizes/noise levels, with claims of consistent improvements over strong baselines along with favorable robustness, transferability, and efficiency.

Significance. If the two-stage separation reliably converts contrastive feedback into stable, dense process rewards that resolve the sparse-outcome instability identified in prior RL memory work, the framework could advance long-horizon memory management for personalized LLM agents by providing both interpretability (via the explicit guideline) and empirical gains. The cognitive analogy and explicit decoupling of guideline induction from policy optimization are conceptually attractive strengths.

major comments (3)
  1. [Abstract] Abstract: the central claims of 'consistent improvements over strong baselines with favorable robustness, transferability, and efficiency' are stated without any quantitative metrics, error bars, baseline descriptions, or ablation results, preventing assessment of effect sizes or whether the two-stage procedure actually outperforms direct RL or static rules.
  2. [Method] Description of the two-stage framework: the guideline produced by contrastive feedback in stage 1 is used directly to define the process rewards in stage 2, creating a circular dependency in which the RL optimization target is shaped by the same learned component without an independent external benchmark or validation that the guideline is optimal or general.
  3. [Experiments] Evaluation section: no ablation, convergence argument, or analysis is supplied demonstrating that the induced guideline measurably reduces reward sparsity or variance in the multi-turn RL stage, despite the abstract explicitly identifying sparse outcome rewards as the source of instability in prior work.
minor comments (1)
  1. The term 'textual gradients' is used without a formal definition or worked example showing how contrastive pairs are converted into an update rule for the guideline.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments and positive recognition of the conceptual contributions. We address each major comment point by point below, clarifying our approach where needed and committing to revisions that strengthen the presentation without altering the core claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claims of 'consistent improvements over strong baselines with favorable robustness, transferability, and efficiency' are stated without any quantitative metrics, error bars, baseline descriptions, or ablation results, preventing assessment of effect sizes or whether the two-stage procedure actually outperforms direct RL or static rules.

    Authors: We agree that the abstract would benefit from greater specificity to convey effect sizes. In the revised version we will insert concise quantitative highlights drawn from the experimental results (e.g., average accuracy gains across the three benchmarks and reference to statistical robustness), while remaining within the abstract length constraint. Baseline families and the two-stage versus direct-RL comparison will be mentioned at a high level. revision: yes

  2. Referee: [Method] Description of the two-stage framework: the guideline produced by contrastive feedback in stage 1 is used directly to define the process rewards in stage 2, creating a circular dependency in which the RL optimization target is shaped by the same learned component without an independent external benchmark or validation that the guideline is optimal or general.

    Authors: The two stages are strictly sequential and non-circular. Stage 1 performs an independent optimization of the guideline using only contrastive textual feedback; once induced, the guideline is frozen and supplied as a fixed reward-shaping function to Stage 2. The RL policy is then optimized against this fixed external signal. We will add explicit wording and a diagram annotation in the method section to emphasize the one-way information flow and note that the guideline itself can be inspected or validated on held-out data independently of the RL stage. revision: partial

  3. Referee: [Experiments] Evaluation section: no ablation, convergence argument, or analysis is supplied demonstrating that the induced guideline measurably reduces reward sparsity or variance in the multi-turn RL stage, despite the abstract explicitly identifying sparse outcome rewards as the source of instability in prior work.

    Authors: We acknowledge that a direct quantitative demonstration of reduced reward sparsity or variance would strengthen the link to the motivating problem. Although overall performance gains are reported, we did not include a dedicated ablation isolating this mechanism. In the revision we will add a short analysis (main text or appendix) comparing reward variance and convergence behavior between the guideline-aligned RL and a direct-outcome-reward baseline, using the same multi-turn setup. revision: yes

Circularity Check

0 steps flagged

No significant circularity: two-stage framework validated on external benchmarks

full rationale

The paper proposes MemCoE as a two-stage process where stage 1 induces a global guideline from contrastive feedback and stage 2 uses it to shape process rewards for RL-based policy learning. No equations, definitions, or self-citations in the provided text reduce the final performance claims to the inputs by construction. The central results rest on empirical improvements over baselines across three independent personalization memory benchmarks with varying preference types, sizes, and noise levels, providing external falsifiability outside the fitted guideline itself.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 1 invented entities

The framework rests on a domain assumption from cognitive science and introduces new learned components whose values are fitted during the two optimization stages.

free parameters (2)
  • guideline induction parameters
    Parameters controlling contrastive feedback and textual gradient updates in stage one.
  • RL process reward scaling
    Scaling factors that turn the induced guideline into structured rewards for the policy optimization.
axioms (1)
  • domain assumption Memory schema theory and functional division between prefrontal regions and hippocampus regions
    Invoked to justify the separation into guideline induction and policy optimization stages.
invented entities (1)
  • MemCoE two-stage framework no independent evidence
    purpose: To jointly learn memory organization and update policy
    New composite system whose components are defined and optimized within the paper.

pith-pipeline@v0.9.0 · 5514 in / 1283 out tokens · 50167 ms · 2026-05-09T19:07:22.493037+00:00 · methodology

discussion (0)

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