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Why Self-Evolution

What Can Evolve

When Evolution Happens

How Evolution Is Guided

Where Agents Evolve

Evaluation & Outlook

CONTENTS

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Why Self-Evolution

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Static Nature of LLMs

Large Language Models (LLMs) are fundamentally static after training. They cannot adapt to new tasks, evolving knowledge domains, or dynamic interaction contexts. This limitation becomes a critical bottleneck in open-ended, interactive environments where continual adaptation is essential for robust performance.

Static LLMs Hit a Wall

Need for Adaptive Agents

The static nature of LLMs necessitates the development of adaptive agents capable of real-time learning and evolution. These agents can dynamically adjust their parameters, reasoning, and actions based on new data and experiences, making them more versatile and effective in complex, dynamic real-world scenarios.

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Paradigm Shift

The field is shifting from scaling static models to developing self-evolving agents. These agents continuously learn from data, interactions, and experiences, enabling them to adapt and improve over time. This shift is crucial for advancing toward Artificial Super Intelligence (ASI).

Continuous Learning

Self-evolving agents are designed to learn continuously, allowing them to handle complex, dynamic real-world problems. They can autonomously generate data, refine their models, and adapt their strategies based on feedback, making them more robust and versatile.

Path to ASI

The development of self-evolving agents is a critical step toward ASI, where agents can autonomously improve and perform at or beyond human-level intelligence across a wide array of tasks. This evolution is essential for achieving advanced, adaptive AI systems.

The Shift to Adaptive Agents

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What Can Evolve

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Memory Evolution

Agents can evolve their memory by adding, merging, or deleting facts based on forgetting curves and reflection mechanisms. This ensures that their knowledge bases remain coherent and up-to-date, enhancing their ability to recall and utilize past experiences.

Model Evolution

The underlying model parameters of agents can evolve through self-generated tasks and feedback, allowing for continuous refinement and improvement. This enables agents to adapt their reasoning and decision-making processes.

Tool Evolution

Agents can autonomously create new tools, master their usage, and select optimal subsets. This expansion of capabilities allows them to tackle a wider range of complex problems and adapt to new tasks more effectively.

Architecture Evolution

The overall architecture of agents, including single-agent and multi-agent systems, can evolve through self-optimization. This involves discovering better workflows, team compositions, and coordination patterns to enhance overall performance.

Four Pillars of Evolution

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Model and Memory Updates

Agents refine their model weights using self-generated tasks and feedback, while their memory evolves by adding, merging, or deleting facts based on forgetting curves and reflection mechanisms. This dual evolution ensures continuous improvement and coherent knowledge retention.

Model & Memory Updates

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Tool Creation and Mastery

Agents autonomously create new tools, master their usage, and select optimal subsets. This capability allows them to expand their skill sets and tackle a wider range of complex problems more effectively.

Architecture Self-Optimization

The architecture of agents, including single-agent and multi-agent systems, evolves through self-optimization. This involves discovering better workflows, team compositions, and coordination patterns to enhance overall performance and efficiency.

Tools & Architecture Growth

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When Evolution Happens

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Real-Time Adaptation

During task execution, agents adapt in real time using in-context learning, self-reflection, and rapid plan revision. This immediate feedback loop allows on-the-fly improvement for the current problem instance, enhancing performance on the fly.

In-Context Learning

Agents leverage their context window to adapt behavior without modifying parameters. They analyze their own performance, generate critiques, and maintain reflections to guide subsequent decisions within the same task context.

Supervised Fine-Tuning

Agents perform immediate self-modification through learned meta-adaptation strategies. They generate self-edits that restructure information representations, specify optimization hyperparameters, and invoke tools for data augmentation and gradient computation.

Intra-Test-Time Learning

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Consolidation of Experiences

Between tasks, agents consolidate experiences through supervised fine-tuning and reinforcement learning on accumulated trajectories. This leads to generalized capability gains across future tasks, improving overall performance.

Offline and Online Learning

Inter-test-time learning involves both offline learning, where agents learn from pre-collected datasets, and online learning, where they continuously adapt based on streaming interaction data. This dual approach ensures robust and efficient evolution.

Inter-Test-Time Learning

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How Evolution Is Guided

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Textual Critiques

Agents receive textual critiques and suggestions as feedback, which they use to refine their behavior. This type of feedback is detailed and interpretable, enabling nuanced self-improvement.

Scalar Rewards

Scalar rewards, such as numerical scores or feedback signals, guide agents in making decisions and improving their performance. These rewards can come from various sources, including environments and rule verifiers.

Confidence Scores

Agents use internal confidence metrics to evaluate their own performance and make adjustments. This self-assessment mechanism allows them to improve without relying on external supervision.

Reward-Based Signals

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Demonstration and Population Methods

Agents learn from self-generated or cross-agent demonstrations and employ evolutionary algorithms that maintain populations of variants. These methods involve mutation, crossover, and competition to select the fittest agents and drive continuous improvement.

Demonstration & Population Methods

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Where Agents Evolve

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General-Purpose Agents

General-purpose agents enhance broad capabilities across various tasks through memory optimization, curriculum-driven training, and model-agent co-evolution. They aim to transfer learned experiences to a wider set of tasks.

Specialized Agents

Specialized agents focus on deepening expertise within specific domains such as coding, GUI, finance, medical, and education. They evolve through targeted training and adaptation to excel in their respective fields.

General vs Specialized Domains

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Evaluation & Outlook

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Static Assessment

Static assessment evaluates the instantaneous performance of agents at a specific point in time. It provides a baseline measure of capabilities but does not capture long-term learning or adaptation.

Short-Horizon Adaptation

Short-horizon assessment focuses on immediate learning and incremental improvement within consistent or slightly varying tasks. It measures how quickly agents can adapt and improve over a limited period.

Long-Horizon Lifelong Learning

Long-horizon assessment evaluates agents' ability to continuously acquire, retain, and reuse knowledge across diverse environments and over extended periods. It addresses challenges like catastrophic forgetting and robust knowledge transfer.

Key Metrics

Metrics include adaptivity speed, retention without forgetting, cross-domain generalization, efficiency, and safety. These metrics provide a comprehensive view of an agent's evolving capabilities and performance.

Measuring Lifelong Growth

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Safe and Controllable Agents

Ensuring the safety and controllability of self-evolving agents is crucial. Future research will address issues like alignment with human values, avoiding harmful behaviors, and maintaining ethical standards during autonomous evolution.

Personalized Agents

Future work focuses on developing personalized agents that can adapt to individual user preferences and behaviors. This involves creating agents that can learn from limited initial data and refine their understanding over time.

Future Directions

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