Tree-of-Experience: A Structured Experience-Management Solution for Self-Evolving Agents under Low-Repetition and Implicit-Reward Environments
arXiv:2606.06960v1 Announce Type: new Abstract: Experience-based self-evolution is crucial for LLM agents, but existing benchmarks often assume explicit goals, stable task patterns, and clear feedback. We study a more challenging setting: low-repetition tasks with implicit rewards, where past experience is difficult to reuse and feedback is delayed, noisy, and outcome-level. We introduce \textsc{FinEvolveBench}, a temporally controlled benchmark for financial sentiment prediction that links daily news-driven predictions to future excess returns. We further propose Tree-of-Experience (ToE), a s
The increasing sophistication and autonomy of LLM agents demand better experience management systems to handle real-world complexities, which current benchmarks fail to address adequately.
This development is crucial for advancing AI agents beyond controlled environments, enabling them to operate effectively in dynamic, implicit-reward settings like financial markets, collapsing complex workflows.
The introduction of Tree-of-Experience and FinEvolveBench changes how LLM agents learn and adapt, moving from explicit, stable feedback to more robust, real-world-like implicit reward structures.
- · AI agent developers
- · Financial services leveraging AI
- · LLM research and development
- · SaaS companies adopting agentic systems
- · Companies with static, non-adaptive AI systems
- · Traditional human-driven workflow providers
More capable and autonomous AI agents will emerge, reducing the need for human oversight in complex decision-making tasks.
The improved adaptability of AI agents could lead to their wider deployment in sectors with high uncertainty and implicit rewards, such as trading, strategic planning, and scientific discovery.
Enhanced AI agent autonomy and effectiveness could accelerate the 'white-collar workflow collapse' and reshape labor markets across various industries, creating demand for entirely new human roles in agent supervision and ethical AI development.
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