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@@ -17,18 +17,18 @@ --- ## 🚀 **Highlights** -- ✅ **Relation-free Graph Construction**: Eliminates unstable relation extraction, using only lightweight entity recognition to graph. -- 🔥 **Zero Token Consumption**: Complete graph construction and retrieval without any LLM calls. -- 📊 **Strong Results**: Outperforms previous RAG methods on widely-used benchmarks. - +- ✅ **Context-Preserving**: A relation-free graph construction paradigm, relying on lightweight entity recognition and semantic linking to achieve comprehensive contextual comprehension. +- ✅ **Complex Reasoning**: Enables deep retrieval via semantic bridging, achieving multi-hop reasoning in a single retrieval pass without requiring an explicit relational graph. +- ✅ **High Scalability**: Zero LLM token consumption, faster processing speed, and linear time/space complexity. + <p align="center"> <img src="figure/main_figure.png" width="95%" alt="Framework Overview"> </p> --- ## 🎉 **News** -- [2025-10-26] We have released the code and [dataset](https://huggingface.co/datasets/Zly0523/linear-rag). -- [2025-10-11] We have released the paper on [Arxiv](https://arxiv.org/abs/2510.10114). +- [2025-10-26] We have released the [code](https://github.com/DEEP-PolyU/LinearRAG.git) and [dataset](https://huggingface.co/datasets/Zly0523/linear-rag). +- [2025-10-11] We have released the preprint version of this paper on [arxiv](https://arxiv.org/abs/2510.10114). --- |