LoopWeaver: Weaving Feedback Loops into Hierarchical Generation under Constraints

Large Language Models face a challenging multi-objective coupled optimization problem in constrained long-text generation, requiring the simultaneous balancing of global structure, local coherence, and constraint satisfaction. However, existing methods often rely on static pipelines or loosely coupled designs, making it difficult to achieve cross-stage collaborative optimization. To address this, we propose LoopWeaver, which models the generation process as a hierarchical, feedback-driven closed-loop optimization problem; by introducing iterative feedback between the planning and generation stages, it achieves the joint optimization of both global and local aspects. Specifically, LoopWeaver integrates constraint-aware hierarchical planning, feasibility filtering, and reward-based preference optimization, thereby allowing feedback signals to permeate both the planning and generation layers. Unlike methods that utilize feedback solely during post-processing or within a single module, LoopWeaver introduces a unified paradigm for feedback-coupled optimization, demonstrating significant improvements in text quality and constraint satisfaction capabilities across various models.