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This work introduces ACLArena, a framework for comprehensively studying, analyzing, and evaluating Agent Continual Learning, and proposes a new ACL recipe that combines offline replay over high-quality trajectories with a routed network of multiple LoRA experts each specialized via RL, substantially improving the agent...
InteraCtive Memory Learning is proposed, a multi-agent framework that transforms the memory mechanism from a passive archive into a learnable, interactive memory policy that significantly outperforms strong baselines.
ThinkFlow bypasses the text bottleneck by dynamically compressing conversational flows into probabilistic latent memory skills, autonomously consolidating complex user states into disentangled, continuous vectors without semantic interference.
GRAIN models reasoning as a semantic parsing and tool-execution pipeline, guided by a Structure Invariance Reward, which forces the LLM to learn robust text-to-structure mappings rather than memorizing linguistic artifacts.
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