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ContinualSkillBench: Can LLM Agents Truly Evolve Th... | AI Research

Key Takeaways

  • ContinualSkillBench is a dynamic evaluation framework designed to determine if large language model (LLM) agents can autonomously evolve their capabilities b...
  • Modern agent frameworks equip large language models with external skill libraries to solve complex tasks.
  • However, it remains unclear whether these systems can effectively evolve their skills and whether the resulting skills improve task-solving capabilities.
  • To bridge this gap, we introduce ContinualSkillBench, a dynamic evaluation framework for in-context continual skill learning.
  • It covers five representative domains, each containing 100 interconnected subtasks ordered by increasing difficulty and opportunities for cross-task skill reuse.
Paper AbstractExpand

Modern agent frameworks equip large language models with external skill libraries to solve complex tasks. However, it remains unclear whether these systems can effectively evolve their skills and whether the resulting skills improve task-solving capabilities. To bridge this gap, we introduce ContinualSkillBench, a dynamic evaluation framework for in-context continual skill learning. It covers five representative domains, each containing 100 interconnected subtasks ordered by increasing difficulty and opportunities for cross-task skill reuse. Our experiments show that sequential execution generally improves performance, but the gains vary substantially across models and domains. Moreover, in-context learning performs comparably to explicit skill maintenance on average, suggesting that much of the improvement arises from adaptation to prior context and feedback rather than reusable skill abstraction alone. Explicit skills nevertheless provide selective benefits for tasks requiring reusable procedures or precise outputs. We further find that less capable models tend to accumulate larger, more fragmented collections of task-specific skills. These findings show that current in-context skill evolution mechanisms can support continual adaptation, but still struggle to consistently consolidate experience into robust and transferable skills.

ContinualSkillBench is a dynamic evaluation framework designed to determine if large language model (LLM) agents can autonomously evolve their capabilities by synthesizing and refining skills through sequential task interactions. Researchers Tianyi Guan, Yiding Wang, Haotong Yang, Siyuan Cao, Shirui Liu, Yi Hu, Jiaqi Li, and Muhan Zhang developed this benchmark to move beyond static evaluations, testing whether agents can consolidate experience into reusable skills that improve performance on increasingly difficult, interconnected tasks.

Evaluating Skill Evolution

The framework covers five domains—Healthcare, Law, Mathematics, Finance, and Office—with 100 subtasks per domain. These tasks are organized into sequences based on difficulty and skill dependencies, ensuring that later tasks require knowledge or procedures practiced in earlier ones. The researchers compare two primary agent configurations: "Independent Execution," where agents solve tasks from scratch, and "Sequential Execution," where agents retain and update a skill repository after each task using feedback.

Performance Gains and Limitations

Experiments with models including GPT-4o, GPT-5.3-Codex, and Claude 4.7 Opus show that sequential execution generally improves performance, yielding an aggregate relative gain of 16.9% in normalized reward. However, these improvements are not universal and vary significantly across different models and domains.
The researchers found that in-context learning—simply adapting to prior context and feedback—performs comparably to explicit skill maintenance. This suggests that much of the observed improvement stems from immediate adaptation rather than the successful abstraction of robust, reusable skills.

Model Behavior and Skill Management

The study reveals distinct differences in how models manage their skill repositories. For example, GPT-4o accumulated 384 skills across the five domains, while GPT-5.3-Codex accumulated 205. Despite these differences, the researchers observed that less capable models tend to build larger, more fragmented collections of task-specific skills that are reused less frequently.

Franklin Analysis

The evidence suggests that while current agent frameworks successfully support continual adaptation, they struggle to consistently consolidate experience into transferable knowledge. The data indicates that explicit skill maintenance provides selective benefits primarily for tasks requiring precise outputs or reusable procedures, but it does not yet function as a reliable mechanism for long-term capability expansion. The variability in performance across models and domains implies that the ability to evolve skills is not solely tied to a model's baseline intelligence, but rather to how effectively the agent architecture processes and stores sequential feedback.

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