Rui Shi, Xunjian Yin, Jiacheng Sang, Zichuan Liu, Shuyan Zhou · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26847 · open-access PDF
Long-horizon agents must learn heterogeneous capabilities from interleaved trajectories. Updating shared parameters to learn new capabilities can degrade previously acquired ones, resulting in catastrophic forgetting. As a trajectory unfolds, different stages may require different capabilities, yet conventional joint training and static adapter merging maintain a fixed parameter configuration throughout execution. We introduce WeAct (Weight-level Internal Actions), which trains independent capability-specific LoRA adapters over a frozen backbone and adds adapter selection to the agent's action
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