arXiv:2609.05955v1 Announce Type: new
Abstract: Tabular foundation models have become powerful graph learners. Systems such as G2T-FM and GraphPFN encode each node as a feature row and make predictio...
By Mingqi Yang, Zidong Guo, Jihui Yang, Wenming Zuo
VisKG‑LM proposes compiling retrieved knowledge graph subgraphs into static visual memories rather than re‑encoding them during each inference step. The method serializes each subgraph as Relation‑Labeled Paths, renders them as images that preserve the graph’s branching structure, and caches these images for reuse. At inference, a language model processes the question and candidate text first, then consults the cached visual memory only at its final layer, yielding improved performance on CommonsenseQA, OpenBookQA, and MedQA‑USMLE compared to both text‑only baselines and a large vision‑language model.
By Yixin Peng, Er Jin, Shiwei Luo, Diego Collarana, Stefan Decker
arXiv:2608. 12391v1 Announce Type: cross Abstract: Graph reasoning provides a promising testbed for evaluating the reasoning ability of large language models (LLMs), as graph instances can be programmatically generated, structurally controlled, and naturally scaled to long-input settings.
By Fali Wang, Ali Al-Lawati, Iliyas Bektas, Jinxuan Fang, Alek Melenski, Tianxiang Zhao, Yao Ma, Suhang Wang
The paper introduces EffiRAG, a graph-based retrieval‑augmented generation system that reduces the cost of building and querying a graph by using it only to locate relevant passages and generating answers from the original text. On the UltraDomain benchmark, EffiRAG outperforms LightRAG‑hybrid in 93 of 120 questions while cutting total system cost by 57 % (from USD 0.952 to USD 0.408). The study shows that graph‑based RAG can be both more accurate and cheaper, especially as the corpus grows, and recommends evaluating such systems on both answer quality and cost.
By Yuzhong Zhang, Haoyang Ma, Chao Peng, Lionel Briand, Boxi Yu, Jialun Cao
arXiv:2608. 07458v1 Announce Type: cross Abstract: Recent optimization studies on Retrieval-Augmented Generation (RAG) have exploited chunk-level KV cache reuse to avoid processing long retrieved contexts for higher efficiency, while significant information redundancy and noise still remain in the coarse-grained chunks.
By Gyuwan Kim, Cheoneum Park, Tao Yang
LADDER is a new framework that combines diffusion language modeling with Graph Retrieval-Augmented Generation (GraphRAG) to enable efficient multi‑hop reasoning. It introduces an event‑driven self‑clocking retrieval mechanism that triggers graph queries only when new entities appear, and an incomplete‑query graph propagation module that aggregates multi‑hop evidence during parallel decoding. Experiments on three multi‑hop QA benchmarks show that LADDER improves exact match from 39.6% to 45.2% while reducing latency by 4.1×.
By Senlei Zhang, Linhao Luo, Qian-Wen Zhang, Siyu An, Junnan Dong, Shuhao Zhang, Xing Sun