S-Path-RAG: Semantic-Aware Shortest-Path Retrieval Augmented Generation for Multi-Hop Knowledge Graph Question Answering
arXiv:2603.23512v1 Announce Type: new Abstract: We present S-Path-RAG, a semantic-aware shortest-path Retrieval-Augmented Generation framework designed to improve multi-hop question answering over large knowledge graphs. S-Path-RAG departs from one-shot, text-heavy retrieval by enumerating bounded-length, semantically weighted candidate paths using a hybrid weighted $k$-shortest, beam, and constrained random-walk strategy, learning a differentiable path scorer together with a contrastive path encoder and lightweight verifier, and injecting a compact soft mixture of selected path latents into a language model via cross-attention. The system runs inside an iterative Neural-Socratic Graph Dialogue loop in which concise diagnostic messages produced by the language model are mapped to targeted graph edits or seed expansions, enabling adaptive retrieval when the model expresses uncertainty. This combination yields a retrieval mechanism that is both token-efficient and topology-aware while p
arXiv:2603.23512v1 Announce Type: new Abstract: We present S-Path-RAG, a semantic-aware shortest-path Retrieval-Augmented Generation framework designed to improve multi-hop question answering over large knowledge graphs. S-Path-RAG departs from one-shot, text-heavy retrieval by enumerating bounded-length, semantically weighted candidate paths using a hybrid weighted $k$-shortest, beam, and constrained random-walk strategy, learning a differentiable path scorer together with a contrastive path encoder and lightweight verifier, and injecting a compact soft mixture of selected path latents into a language model via cross-attention. The system runs inside an iterative Neural-Socratic Graph Dialogue loop in which concise diagnostic messages produced by the language model are mapped to targeted graph edits or seed expansions, enabling adaptive retrieval when the model expresses uncertainty. This combination yields a retrieval mechanism that is both token-efficient and topology-aware while preserving interpretable path-level traces for diagnostics and intervention. We validate S-Path-RAG on standard multi-hop KGQA benchmarks and through ablations and diagnostic analyses. The results demonstrate consistent improvements in answer accuracy, evidence coverage, and end-to-end efficiency compared to strong graph- and LLM-based baselines. We further analyze trade-offs between semantic weighting, verifier filtering, and iterative updates, and report practical recommendations for deployment under constrained compute and token budgets.
Executive Summary
This article presents S-Path-RAG, a novel framework for multi-hop question answering over large knowledge graphs. By employing a hybrid weighted k-shortest, beam, and constrained random-walk strategy, S-Path-RAG enumerates semantically weighted candidate paths and injects path latents into a language model via cross-attention. This approach yields a retrieval mechanism that is both token-efficient and topology-aware. The system is validated on standard multi-hop KGQA benchmarks, demonstrating consistent improvements in answer accuracy, evidence coverage, and end-to-end efficiency compared to strong graph- and LLM-based baselines. The authors also analyze trade-offs between semantic weighting, verifier filtering, and iterative updates, providing practical recommendations for deployment under constrained compute and token budgets.
Key Points
- ▸ S-Path-RAG employs a hybrid weighted k-shortest, beam, and constrained random-walk strategy to enumerate semantically weighted candidate paths.
- ▸ The framework injects path latents into a language model via cross-attention, enabling token-efficient and topology-aware retrieval.
- ▸ S-Path-RAG is validated on standard multi-hop KGQA benchmarks, demonstrating consistent improvements in answer accuracy and efficiency compared to strong graph- and LLM-based baselines.
Merits
Strength
The use of hybrid strategy to enumerate semantically weighted candidate paths allows for a more comprehensive search space, improving answer accuracy and efficiency.
Demerits
Limitation
The framework's reliance on language models and knowledge graphs may limit its applicability to domains with limited or noisy data.
Expert Commentary
The presented framework, S-Path-RAG, represents a significant advancement in multi-hop question answering over large knowledge graphs. By leveraging a hybrid strategy to enumerate semantically weighted candidate paths and injecting path latents into a language model, S-Path-RAG achieves a balance between token efficiency and topology awareness. The framework's validation on standard multi-hop KGQA benchmarks demonstrates its potential to improve answer accuracy and efficiency in a variety of applications. However, the framework's reliance on language models and knowledge graphs may limit its applicability to domains with limited or noisy data. As such, further research should focus on addressing these limitations and exploring the framework's potential in real-world applications.
Recommendations
- ✓ Future research should focus on addressing the limitations of the framework, including its reliance on language models and knowledge graphs.
- ✓ The framework should be further evaluated on a wider range of datasets and applications to better understand its potential and limitations.
Sources
Original: arXiv - cs.CL
