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arxiv: 2512.10696 · v2 · submitted 2025-12-11 · 💻 cs.AI · cs.CL

Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution

Zouying Cao , Jiaji Deng , Li Yu , Weikang Zhou , Zhaoyang Liu , Bolin Ding , Hai Zhao This is my paper

Pith reviewed 2026-05-16 23:16 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords procedural memoryLLM agentsdynamic memoryexperience distillationmemory refinementagent evolutionlifelong learningmemory scaling
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The pith

ReMe lets smaller LLM agents outperform larger ones by dynamically distilling, reusing, and refining procedural experiences.

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

The paper aims to replace passive, static memory storage in LLM agents with an active system that extracts lessons from both successes and failures, applies them to new situations, and cleans up the memory store over time. This matters because agents currently waste effort repeating mistakes; a dynamic memory could internalize reliable how-to knowledge and cut down on trial-and-error loops. The core demonstration is a memory-scaling effect: an 8B model using the system beats a 14B model that lacks it on standard agent benchmarks. If the approach holds, memory management becomes a practical route to stronger agents without always needing bigger base models.

Core claim

ReMe implements procedural memory through three linked processes: multi-faceted distillation that pulls out success patterns, failure triggers, and comparative insights; context-adaptive reuse that indexes memories for new scenarios; and utility-based refinement that adds helpful memories while pruning outdated ones to keep the pool compact. On BFCL-V3 and AppWorld, the full system sets a new state-of-the-art, and the memory-scaling result shows Qwen3-8B with ReMe surpassing a memoryless Qwen3-14B.

What carries the argument

The ReMe framework's three mechanisms—multi-faceted distillation, context-adaptive reuse, and utility-based refinement—that together turn raw interaction history into an evolving, high-quality experience pool.

If this is right

  • Agents reduce redundant trial-and-error by internalizing reliable how-to knowledge.
  • Smaller models with ReMe can surpass larger memoryless models on agent benchmarks.
  • Autonomous addition and pruning keeps the experience pool compact and high-quality.
  • Self-evolving memory supplies a computation-efficient route to lifelong agent learning.
  • The same mechanisms deliver state-of-the-art results on BFCL-V3 and AppWorld.

Where Pith is reading between the lines

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

  • Memory scaling could become a cheaper alternative to model scaling for improving agent reliability over long horizons.
  • The distillation and refinement loop might transfer to domains outside the two tested benchmarks without major redesign.
  • Combining ReMe-style memory with other agent training signals could produce even stronger compounding gains.
  • In extended deployments the compact memory pool would lower token costs and latency compared with growing static archives.

Load-bearing premise

The utility-based refinement step can correctly judge which memories remain valid and which have become outdated without task-specific tuning or systematic mistakes.

What would settle it

A controlled run on BFCL-V3 where the refinement step is disabled or replaced with random pruning, showing whether performance falls below the reported ReMe baseline or memory quality degrades over repeated tasks.

Figures

Figures reproduced from arXiv: 2512.10696 by Bolin Ding, Hai Zhao, Jiaji Deng, Li Yu, Weikang Zhou, Zhaoyang Liu, Zouying Cao.

Figure 1
Figure 1. Figure 1: Example of how agents complete one stock trading task with and without past experience. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The ReMe framework comprises three alternating phases. The system first constructs the initial experience pool from the agent’s past trajectories. For new tasks, relevant experiences are recalled and reorganized to guide agent inference. After task execution, ReMe updates the pool, selectively adding new insights and removing outdated ones. rently, LLMsumm conducts failure analysis, scru￾tinizing unsuccess… view at source ↗
Figure 3
Figure 3. Figure 3: Ablation on retrieval keys. The experiments [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Statistics of failed tasks with and without [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Experience example on BFCL-V3. when to use: When a user wants to place an order for a stock but without providing a specific price. experience content: The assistant demonstrated a methodical approach by first retrieving the current stock price using get_stock_info and then using that price in the place_order function. This two-step process ensures compliance with the required parameters of the place_order… view at source ↗
Figure 8
Figure 8. Figure 8: Experience example on AppWorld. code, with slight prompt modifications to extract procedural memories. C Implementation Details C.1 For Experience Acquisition First, we sample trajectories N= 8 times for each task query to obtain a diverse set of potential solu￾tions including both high-reward and low-reward results. Next, within each group corresponding to the same task, all trajectories are sorted by the… view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of trajectory-level and keypoint-level experience granularity. [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
read the original abstract

Procedural memory enables large language model (LLM) agents to internalize "how-to" knowledge, theoretically reducing redundant trial-and-error. However, existing frameworks predominantly suffer from a "passive accumulation" paradigm, treating memory as a static append-only archive. To bridge the gap between static storage and dynamic reasoning, we propose $\textbf{ReMe}$ ($\textit{Remember Me, Refine Me}$), a comprehensive framework for experience-driven agent evolution. ReMe innovates across the memory lifecycle via three mechanisms: 1) $\textit{multi-faceted distillation}$, which extracts fine-grained experiences by recognizing success patterns, analyzing failure triggers and generating comparative insights; 2) $\textit{context-adaptive reuse}$, which tailors historical insights to new contexts via scenario-aware indexing; and 3) $\textit{utility-based refinement}$, which autonomously adds valid memories and prunes outdated ones to maintain a compact, high-quality experience pool. Extensive experiments on BFCL-V3 and AppWorld demonstrate that ReMe establishes a new state-of-the-art in agent memory system. Crucially, we observe a significant memory-scaling effect: Qwen3-8B equipped with ReMe outperforms larger, memoryless Qwen3-14B, suggesting that self-evolving memory provides a computation-efficient pathway for lifelong learning. We release our code and the $\texttt{reme.library}$ dataset to facilitate further research.

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 ReMe, a dynamic procedural memory framework for LLM agents with three mechanisms: multi-faceted distillation (extracting success/failure patterns and comparative insights), context-adaptive reuse (scenario-aware indexing), and utility-based refinement (autonomous addition/pruning of memories). It claims new state-of-the-art results on BFCL-V3 and AppWorld benchmarks, plus a memory-scaling effect in which Qwen3-8B equipped with ReMe outperforms the larger memoryless Qwen3-14B model.

Significance. If the results hold under scrutiny, ReMe would demonstrate that dynamic, self-refining procedural memory can enable smaller models to surpass larger memoryless ones, offering a compute-efficient route to agent evolution and lifelong learning. The public release of code and the reme.library dataset is a clear strength that supports reproducibility and follow-on work.

major comments (2)
  1. [Methods (utility-based refinement)] The utility-based refinement mechanism (described in the methods section) is presented only at a high level as 'autonomously adds valid memories and prunes outdated ones' with no explicit utility scoring function, equation, pseudocode, LLM-as-judge prompt, or reported metric (e.g., success-rate delta or recency-weighted score). This is load-bearing for the headline scaling claim, as the 8B+ReMe > 14B result depends on the refinement step reliably improving memory quality without systematic bias or hidden task-specific tuning.
  2. [Experiments and Results] The experiments section reports SOTA performance and a 'significant memory-scaling effect' but provides no error bars, standard deviations across runs, number of trials, statistical significance tests, or detailed ablations that isolate the contribution of each of the three mechanisms. Without these, it is impossible to determine whether the observed gains are robust or sensitive to post-hoc choices in memory extraction and refinement.
minor comments (1)
  1. [Abstract and Introduction] The abstract and introduction would benefit from a concise table or diagram summarizing the three mechanisms and their inputs/outputs to improve readability before the detailed methods.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We agree that additional technical detail and statistical rigor are needed to support the core claims. We outline our planned revisions below.

read point-by-point responses

  1. Referee: [Methods (utility-based refinement)] The utility-based refinement mechanism (described in the methods section) is presented only at a high level as 'autonomously adds valid memories and prunes outdated ones' with no explicit utility scoring function, equation, pseudocode, LLM-as-judge prompt, or reported metric (e.g., success-rate delta or recency-weighted score). This is load-bearing for the headline scaling claim, as the 8B+ReMe > 14B result depends on the refinement step reliably improving memory quality without systematic bias or hidden task-specific tuning.

    Authors: We agree the current description is high-level and insufficient to substantiate the scaling result. In the revision we will add the explicit utility scoring function (including its equation), the full pseudocode for the add/prune procedure, the complete LLM-as-judge prompt, and quantitative metrics (pre-/post-refinement success-rate deltas and any bias diagnostics). We will also state explicitly that no task-specific tuning beyond the published mechanisms was used. revision: yes

  2. Referee: [Experiments and Results] The experiments section reports SOTA performance and a 'significant memory-scaling effect' but provides no error bars, standard deviations across runs, number of trials, statistical significance tests, or detailed ablations that isolate the contribution of each of the three mechanisms. Without these, it is impossible to determine whether the observed gains are robust or sensitive to post-hoc choices in memory extraction and refinement.

    Authors: We acknowledge the absence of these statistical elements. We will rerun all main experiments with a fixed number of independent trials (reporting the exact count and seeds), add error bars and standard deviations, and include statistical significance tests for the key comparisons. We will also insert comprehensive ablation tables that isolate each of the three mechanisms (multi-faceted distillation, context-adaptive reuse, utility-based refinement) and quantify their individual contributions to both absolute performance and the memory-scaling effect. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical framework with independent benchmark validation

full rationale

The paper describes ReMe as a procedural memory framework with three qualitative mechanisms (multi-faceted distillation, context-adaptive reuse, utility-based refinement) and supports its claims exclusively through experimental results on BFCL-V3 and AppWorld benchmarks. No equations, fitted parameters, or derivations are presented that could reduce outputs to inputs by construction. The reported memory-scaling effect (Qwen3-8B+ReMe outperforming Qwen3-14B) is an observed empirical outcome, not a self-referential prediction. Self-citations, if present, are not load-bearing for any central claim, and the framework remains self-contained against external benchmarks without renaming known results or smuggling ansatzes.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that LLMs can reliably extract and judge experience quality from their own traces; no free parameters or invented physical entities are described in the abstract.

axioms (1)
  • domain assumption LLM agents can extract fine-grained success patterns, failure triggers, and comparative insights from interaction traces
    Invoked in the description of multi-faceted distillation

pith-pipeline@v0.9.0 · 5566 in / 1333 out tokens · 36477 ms · 2026-05-16T23:16:40.665496+00:00 · methodology

discussion (0)

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Forward citations

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