arXiv:2607.01241v1 Announce Type: new Abstract: Existing prompt compression methods treat text as flat token sequences, failing to capture the distributed nature of important information, which is often spread across multiple locations and connected through both local syntactic dependencies and global semantic relations. Such relational structure is naturally represented as a graph, where tokens or sentences become nodes and their dependencies become edges. To this end, we propose RAGP, which formulates prompt compression as Redundancy-Aware Graph Pruning on a multiplex graph that jointly mode
The proliferation of large language models and their increasing computational demands drives the need for more efficient prompt handling, making prompt compression a critical area of research.
This research introduces a novel, graph-based approach to prompt compression that addresses limitations of current methods, potentially leading to significant improvements in AI efficiency and performance.
The shift from treating text as flat sequences to a multiplex graph representation could fundamentally alter how prompt engineering and large language model interactions are optimized, impacting cost and speed.
- · AI developers
- · Cloud computing providers
- · Enterprises using LLMs
- · Inefficient prompt optimization techniques
- · Legacy AI infrastructure
Improved efficiency and reduced computational cost for large language model inference.
Enables more complex and longer-context interactions with AI models, broadening their applications.
Accelerates the development of more sophisticated AI agents by providing a better foundation for understanding and processing information.
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