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As Large Language Models (LLMs) evolve, parallel reasoning has emerged as a vital inference paradigm that enhances robustness by concurrently exploring multiple thought trajectories. Unlike fragile sequential methods, parallel reasoning expands inference breadth to significantly improve problem-solving performance. T...
This article proposes a novel Memory-ENhanced Dependency Network, dubbed as MENDNet, for the multistock movement prediction task, and develops a well-elaborated memory structure to store selective news history for each stock, so that history embeddings can be dynamically estimated by attentively aggregating exclusive h...
Progressive Tree Drafting (PTD) is proposed, which employs a structured, guided parallel drafting strategy to harness the model's parallel potential by coupling a progressive tree structure with a stepwise pruning mechanism and actively guides the LLM to explore multiple semantic paths in a single forward pass.
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