EvoBelief: Falsifiable World Models for Self-Improving LLM Agents
We introduce EvoBelief, a self-evolving world model that represents environment knowledge as falsifiable hypotheses.
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Key points
- Self-evolving LLM agents typically improve how they act through accumulated experience, reflection, or skills.
- Yet even procedurally capable agents can fail when their beliefs about environment dynamics are incomplete, incorrect, or outdated.
- We study a complementary axis of self-evolution: evolving those beliefs during deployment.
- Across four benchmarks spanning static dynamics, hidden drift, and scientific discovery, EvoBelief improves task performance and reuses learned rules across tasks.
Sources (1)
- [1]EvoBelief: Falsifiable World Models for Self-Improving LLM AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 10:46 PM
We introduce EvoBelief, a self-evolving world model that represents environment knowledge as falsifiable hypotheses.
Self-evolving LLM agents typically improve how they act through accumulated experience, reflection, or skills.
Extractive summary: sentences quoted from the sources.
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