Rosetta Memory: Adaptive Memory for Cross-LLM Agents
Rosetta Memory optimizes cross-LLM tasks with adaptive memory, showing significant performance improvements in experiments.
Key Findings
Methodology
The paper introduces Rosetta Memory, an adaptive memory system with two profile-conditioned operators to optimize memory storage and presentation. These operators integrate with LLM profiles to effectively adapt to different LLM models, enhancing task completion efficiency.
Key Results
- On the HotpotQA dataset, Rosetta Memory achieved a 20% increase in F1 score, significantly outperforming baseline models.
- On 2WikiMultihopQA, the model maintained high performance even with unseen model replacement, showing a 15% F1 improvement.
- On MuSiQue, the model excelled in complex multi-hop problems, achieving a 25% F1 increase.
Significance
This research provides a novel solution for cross-LLM memory adaptation, addressing long-standing challenges in memory exchange between different models. It not only improves task performance but also offers important insights for future multi-model system design.
Technical Contribution
Rosetta Memory introduces profile-conditioned operators, overcoming limitations of existing methods, providing new theoretical guarantees and engineering possibilities, especially in multi-model environments.
Novelty
This is the first implementation of memory adaptation in cross-LLM agents, differing from existing LLM-centric designs by offering new perspectives and methods.
Limitations
- Performance may degrade in extreme model replacement scenarios due to insufficient profile detail for some models.
- Requires more training data to ensure broad adaptability.
Future Work
Future research could explore adaptability to more LLM models, optimize profile generation, and extend to other task types.
AI Executive Summary
In the modern AI landscape, cross-large language model (LLM) agent systems face challenges in memory adaptation. Existing solutions often focus on single LLM memory design, but users frequently switch models across tasks. Rosetta Memory addresses this issue with an adaptive memory system.
Rosetta Memory designs two profile-conditioned operators for memory writing and reading. These operators integrate with LLM profiles to effectively adapt to different LLM models, enhancing task completion efficiency. Experimental results show significant performance improvements across multiple datasets.
This research not only provides a novel solution for cross-LLM memory adaptation but also offers important insights for future multi-model system design. While performance may degrade in extreme model replacement scenarios, its overall performance is excellent, and future research can further optimize profile generation and extend to other task types.
Deep Analysis
Background
With the rapid development of large language models, cross-model memory adaptation has become an important research direction. Existing methods focus on single-model memory design, unable to meet the needs of multi-model environments. Representative works include memory streams in generative agents and self-reflective verbal memory, but these methods often assume the consuming model remains unchanged.
Core Problem
The core problem is how memory written by one model can be effectively read and activated by another. Different models may produce inconsistencies in memory writing and reading, leading to task performance degradation. Solving this problem is crucial for improving multi-model system efficiency.
Innovation
Rosetta Memory addresses cross-model memory adaptation with two profile-conditioned operators. These operators adjust memory storage and presentation based on target model profiles, enhancing task completion efficiency.
Methodology
- �� Design two profile-conditioned operators: one for memory writing, one for memory reading.
- �� Use a minimum-gain sampling curriculum to prioritize underserved models during training.
- �� Design a performance-gap reward to measure the operators' actual contribution.
Experiments
Experiments were conducted using HotpotQA, 2WikiMultihopQA, and MuSiQue datasets to evaluate model performance across different tasks. The effectiveness of Rosetta Memory was validated by comparing with baseline models.
Results
Experimental results show that Rosetta Memory significantly outperforms baseline models across all datasets, especially in complex multi-hop problems. The model maintains high performance even with unseen model replacement, demonstrating strong adaptability.
Applications
This system can be directly applied to tasks requiring cross-model memory adaptation, such as complex problem-solving in multi-model collaboration. It offers new ideas for task optimization in multi-model environments.
Limitations & Outlook
Although Rosetta Memory performs excellently, performance may degrade in extreme model replacement scenarios. Additionally, the system requires more training data to ensure broad adaptability.
Plain Language Accessible to non-experts
Imagine a library where each book represents a memory fragment. Rosetta Memory acts like a smart librarian who adjusts the placement and recommendation of books based on the reader's needs. Whether the reader is a scientist or a novelist, the librarian ensures they find the most suitable books.
ELI14 Explained like you're 14
Imagine you're playing a game that requires multiple characters, each with their own skills and tasks. Rosetta Memory is like a super assistant that helps you retain and adjust game progress as you switch between characters, ensuring you complete tasks smoothly under any character. Isn't that cool?
Glossary
LLM (Large Language Model)
A large model capable of processing and generating natural language, typically used for various language tasks.
In this paper, LLM is the core component of the agent system.
Memory Adaptation
Refers to the adjustment of memory according to the needs of different models to improve task efficiency.
Rosetta Memory improves cross-model task performance through memory adaptation.
Profile-Conditioned Operator
An operator used to adjust memory storage and presentation based on model profiles.
These operators are the core innovation of Rosetta Memory.
Minimum-Gain Sampling Curriculum
A curriculum design that prioritizes training underserved models.
Used to enhance the broad adaptability of Rosetta Memory.
Performance-Gap Reward
A reward mechanism used to measure the actual contribution of operators.
Helps evaluate the effectiveness of Rosetta Memory.
Open Questions Unanswered questions from this research
- 1 How to maintain stable performance in extreme model replacement scenarios?
- 2 How to generate more detailed model profiles to enhance adaptability?
Applications
Immediate Applications
Cross-Model Task Optimization
Applicable to complex tasks requiring model switching, such as multi-model collaboration.
Long-term Vision
Intelligent Agent Systems
By optimizing memory adaptation, promote the development of intelligent agent systems for more efficient task processing.
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.