Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual Learning

ArXi:2601.03641v3 Announce Type: replace Large Language Model (LLM)-based agents significantly extend the utility of LLMs by interacting with dynamic environments. However, enabling agents to continually learn new tasks without catastrophic forgetting remains a critical challenge, known as the stability-plasticity dilemma. In this work, we argue that this dilemma fundamentally arises from the failure to explicitly distinguish between common knowledge shared across tasks and conflicting knowledge