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arxiv: 2606.07711 · v1 · pith:7VS7JEC4new · submitted 2026-06-05 · 💻 cs.LG · cs.AI

Rosetta Memory: Adaptive Memory for Cross-LLM Agents

Hao Yang , Shiqi Shen , Haoxuan Li , Zhipeng Wang , Zhi Gong , Xu Chen This is my paper

Pith reviewed 2026-06-27 22:40 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords memory adaptationcross-LLM agentsprofile-conditioned operatorsminimum-gain samplingmulti-hop QAagent memory systemsLLM switching
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The pith

Profile-conditioned operators enable memory written by one LLM to activate a different LLM effectively.

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

The paper establishes that existing memory systems tie operations to a single LLM backbone, but users often switch models mid-task or across tasks, requiring memory to transfer upstream to downstream models. By reframing the problem as memory-centric adaptation rather than LLM-centric design, two operators are introduced that condition memory writing and reading on LLM profiles. These operators are trained jointly with a minimum-gain sampling curriculum that prioritizes least-served models and a performance-gap reward that isolates the operators' contribution. The result is memory that stores and presents information in ways that improve task completion when consumed by other models. Experiments on HotpotQA, 2WikiMultihopQA, and MuSiQue confirm outperformance over baselines and robustness when models are replaced with unseen ones.

Core claim

We approach the upstream-downstream memory adaptation problem from both write and read sides by designing two profile-conditioned operators that are jointly trained to optimize how memory is stored and presented for better task completion; to ensure generalization across LLMs we use a minimum-gain sampling curriculum that prioritizes the least-served models, and we measure contribution with a performance-gap reward that compares against a naive memory baseline.

What carries the argument

Profile-conditioned operators that adapt memory write and read operations based on LLM profiles.

Load-bearing premise

The profile-conditioned operators trained with minimum-gain sampling will generalize to a broad set of LLMs including unseen ones rather than overfitting to the models seen during the curriculum.

What would settle it

If transferring memory written by one LLM to an unseen different LLM on HotpotQA or MuSiQue produces no gain over the naive memory baseline, the claim of effective cross-LLM adaptation would be falsified.

Figures

Figures reproduced from arXiv: 2606.07711 by Haoxuan Li, Hao Yang, Shiqi Shen, Xu Chen, Zhi Gong, Zhipeng Wang.

Figure 1
Figure 1. Figure 1: Pilot study of memory mismatch. Each dataset is plotted in a separate panel because [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Model replacement on 2WikiMultihopQA and MuSiQue. Bars are grouped by the number [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Sensitivity to the minimum-gain sampling coefficient [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Representative read/write inspection from a held-out 2Wiki validation sample. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Writer–reader compatibility heatmaps on HotpotQA and 2WikiMultihopQA. Each cell [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Worst-case target-reader performance on HotpotQA and 2WikiMultihopQA. For each target [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Noisy memory robustness on HotpotQA and 2WikiMultihopQA across three target readers. [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
read the original abstract

Memory is the key component for transforming a stateless LLM into a persistent, evolving agent through experience accumulation, long-horizon planning, and continual self-improvement. Existing memory systems typically take the LLM as the center and design memory operations tailored to a specific backbone. In practice, however, users frequently switch between LLMs, for example using Claude for coding and GPT for writing across tasks, or routing different steps to different backbones within a single task for cost-effective trade-offs. As a result, memory written by one model often needs to be consumed by another. Making upstream memory effectively adapt to and activate downstream LLMs remains a critical yet underexplored problem. To bridge this gap, we shift the perspective from LLM-centric memory design to \emph{memory-centric LLM adaptation}. Specifically, we approach the above upstream-downstream memory adaptation problem from both the write and read sides, and design two profile-conditioned operators that are jointly trained to optimize how memory is stored and presented for better task completion. To ensure the learned operators generalize across a broad set of LLMs, we propose a minimum-gain sampling curriculum that prioritizes the least-served LLMs during training. To better measure the operators' actual contribution rather than the LLM's own capability, we design a performance-gap reward that compares against a naive memory baseline. Experiments on HotpotQA, 2WikiMultihopQA, and MuSiQue demonstrate that our model consistently outperforms baselines and remains robust under unseen-model replacement.

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 Rosetta Memory, a memory-centric approach to cross-LLM agent adaptation. It introduces two jointly trained profile-conditioned operators (write and read) that adapt memory storage and retrieval for better task performance across different LLMs. Training uses a minimum-gain sampling curriculum to prioritize least-served models for generalization, paired with a performance-gap reward that measures operator contribution against a naive baseline. Experiments on HotpotQA, 2WikiMultihopQA, and MuSiQue report consistent outperformance over baselines and robustness when LLMs are replaced with unseen models.

Significance. If the claimed generalization of the profile-conditioned operators holds, the work addresses a practical gap in multi-LLM workflows where memory written by one model must be consumed by another. The minimum-gain curriculum and performance-gap reward are constructive elements that could support broader applicability of memory systems beyond single-LLM designs.

major comments (2)
  1. [Experiments (unseen-model replacement)] Experiments section (unseen-model replacement results): The central robustness claim requires that the jointly trained operators generalize beyond the training LLM distribution. The minimum-gain sampling is intended to achieve this, yet no details are provided on the exact set of LLMs used in the curriculum, the specific unseen replacement models, or quantitative metrics (e.g., performance delta with vs. without the curriculum) showing that the operators capture transferable rules rather than patterns specific to the seen models. This directly affects whether the unseen-replacement experiments support the generalization conclusion.
  2. [Method (operators and training)] Method section (profile-conditioned operators and joint training): The write and read operators are described as profile-conditioned, but the manuscript does not specify the content or representation of the LLM profile used for conditioning, nor how the joint training objective combines the two sides with the performance-gap reward. Without these details, it is difficult to verify that the operators implement memory-centric adaptation rather than model-specific heuristics.
minor comments (2)
  1. [Abstract] The abstract states that the model 'remains robust under unseen-model replacement' but provides no numerical results or baseline comparisons for that setting; adding a brief quantitative summary would improve clarity.
  2. [Method] Notation for the performance-gap reward and minimum-gain sampling could be formalized with equations to make the training procedure reproducible from the text alone.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive feedback and the recommendation for major revision. We address each major comment point by point below, providing clarifications and committing to revisions where details were insufficiently specified in the manuscript.

read point-by-point responses

  1. Referee: Experiments section (unseen-model replacement results): The central robustness claim requires that the jointly trained operators generalize beyond the training LLM distribution. The minimum-gain sampling is intended to achieve this, yet no details are provided on the exact set of LLMs used in the curriculum, the specific unseen replacement models, or quantitative metrics (e.g., performance delta with vs. without the curriculum) showing that the operators capture transferable rules rather than patterns specific to the seen models. This directly affects whether the unseen-replacement experiments support the generalization conclusion.

    Authors: We agree that the Experiments section would benefit from explicit enumeration of these elements to better substantiate the generalization claim. In the revised manuscript, we will add: (i) the full set of LLMs employed in the minimum-gain sampling curriculum, (ii) the precise identities of the unseen replacement models, and (iii) an ablation table reporting performance deltas with versus without the curriculum on the unseen-model replacement tasks. These additions will directly address how the operators learn transferable rules. revision: yes

  2. Referee: Method section (profile-conditioned operators and joint training): The write and read operators are described as profile-conditioned, but the manuscript does not specify the content or representation of the LLM profile used for conditioning, nor how the joint training objective combines the two sides with the performance-gap reward. Without these details, it is difficult to verify that the operators implement memory-centric adaptation rather than model-specific heuristics.

    Authors: We acknowledge that the manuscript would be strengthened by more precise specifications here. The LLM profile is represented as a fixed-dimensional embedding constructed from model metadata and benchmark scores; the joint training objective combines the write and read operators through a shared performance-gap reward in a single optimization loop. In the revision, we will insert the exact profile construction procedure, dimensionality, and the mathematical form of the joint objective (including how the reward is applied across both operators) into the Method section. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical claims rest on external benchmarks and generalization tests

full rationale

The paper describes an empirical system of profile-conditioned operators trained via a minimum-gain curriculum and evaluated with a performance-gap reward against a naive baseline. No equations, fitted parameters, or self-citations are presented that reduce any claimed prediction or result to the inputs by construction. The robustness claim under unseen-model replacement is an empirical generalization test rather than a definitional or self-referential derivation. The derivation chain is therefore self-contained against external data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities can be extracted beyond the high-level design choices stated in the abstract.

pith-pipeline@v0.9.1-grok · 5807 in / 1083 out tokens · 22463 ms · 2026-06-27T22:40:44.289826+00:00 · methodology

discussion (0)

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Reference graph

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