Efficient Long-Context Reasoning in Large Language Models: Hierarchical Token Compression with Sparse Attention and Dynamic Memory Management
The paper introduces CLEAR, a unified framework that combines hierarchical token compression, sparse attention orchestration, and dynamic memory management to enable efficient long-context reasoning in large language models, achieving significant reductions in computational complexity and memory usage while maintaining high accuracy on long-context benchmarks.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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