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When a large language model handles a multi-turn task and a user proposes a change but ultimately rejects it, the model should continue as if nothing changed. We find a surprising failure: merely mentioning a rejected change can derail task execution, even when the user's final intent remains unchanged. To systematical...
This second part of the special issue extends the conversation beyond the foundations established in Part I, which highlighted the transformative potential of generative AI and large language models for knowledge reuse, design exploration, data fusion, and smart manufacturing operations.
This work proposes K-GAT (Knowledge-Guided Agent Topology Generator), a neuro-symbolic framework that formulates collaboration topology design as a knowledge-conditioned structure learning problem, integrating external evidence directly into autoregressive graph generation.
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