The paper reports the ABAI submission to COLIEE 2026 Task 1, a case law retrieval challenge that suppresses cited passages, and details a four‑stage retrieval pipeline: multi‑view BM25 with reciprocal rank fusion, neural reranking, graph‑based features via a graph attention network, and a LightGBM meta‑learner over 34 features. The best run achieved an F1 score of 0.177 on the official test set, compared to a cross‑validated 0.311, and the authors attribute the gap to a recall ceiling, temporal distribution shift, and threshold miscalibration. A controlled post‑hoc study examined the impact of threshold transfer, decision quality across time, and query similarity, and identified specific remedies—such as BM25 length‑normalisation tuning, event‑triple views, and dense fusion—that improved recall, while other interventions had no effect.
By Minhan Cho, Soyoung Park, Daejin Choi, Jinyoung Han
As retrieval systems scale, high-quality reranking becomes increasingly important. However, most existing rerankers, whether encoder-based or decoder-based, jointly encode the query and passage, tightly coupling their computation and limiting deployment efficiency as well as flexibility.
arXiv:2606.22807v3 Announce Type: replace
Abstract: As retrieval systems scale, effective and efficient reranking becomes increasingly important. However, most existing encoder- and decoder-based rer...
By Xinping Zhao, Jiaxin Xu, Ziqi Dai, Xin Zhang, Huiyao Chen, Shouzheng Huang, Xianhao Xiong, Danyu Tang, Xinshuo Hu, Guohong Fu, Meishan Zhang, Baotian Hu
The paper introduces RicciPool, a graph pooling method that incorporates higher‑order connectivity via Ollivier‑Ricci curvature to reweight edges before spectral clustering. Unlike traditional pooling approaches that focus only on rough topology, RicciPool leverages local connection information to improve cluster assignment. Experiments on protein and social network datasets demonstrate its effectiveness.
By Chaoqun Fei, Guoxuan Li, Tinglve Zhou, Chuanqing Wang, Yangyang Li
arXiv:2603. 29875v3 Announce Type: replace-cross Abstract: One of the key problems in Retrieval-augmented generation (RAG) systems is that chunk-based retrieval pipelines represent the source chunks as atomic objects, mixing the information contained within such a chunk into a single vector.
By Ryszard Tuora, Mateusz Gali\'nski, Micha{\l} Godziszewski, Micha{\l} Karpowicz, Mateusz Czy\.znikiewicz, Adam Kozakiewicz, Tomasz Zi\k{e}tkiewicz
arXiv:2607. 05469v1 Announce Type: cross Abstract: Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks.
By Jingyun Zhang, Hao Peng, Jianxin Li, Angsheng Li, Philip S. Yu
Detecting communities in heterophilic graphs -- where connected nodes often belong to different classes -- is hard for unsupervised methods: classical modularity and spectral methods are feature agnostic, while deep graph-clustering methods rely on contrastive or generative machinery that is opaque. We propose Curvature-Guided Sheaf Diffusion (CGSD), a fully unsupervised community-detection algorithm that uses the discrete Forman--Ricci curvature of each edge as its single topological signal, propagated through every stage of an end-to-end pipeline.
arXiv:2607. 27530v1 Announce Type: new Abstract: Graph-text retrieval typically maps a graph and its description to a single embedding, even when a query concerns only one semantic aspect, such as a class label or molecular property.
By Xiao Yue, Guangzhi Qu
arXiv:2608. 14841v1 Announce Type: new Abstract: Long-document visual question answering (VQA) over documents of tens to hundreds of pages mixing text, tables, charts, and figures typically follows retrieve-then-read pipelines.
By Guanchen Wu, Jiayuan Ding, Subhabrata Mukherjee, Carl Yang
arXiv:2606. 30249v1 Announce Type: cross Abstract: Detecting communities in heterophilic graphs -- where connected nodes often belong to different classes -- is hard for unsupervised methods: classical modularity and spectral methods are feature agnostic, while deep graph-clustering methods rely on contrastive or generative machinery that is opaque.
By Feifan Wang
arXiv:2608.22381v1 Announce Type: cross
Abstract: Scientific papers may relate by problem, method, result, or contribution, but document-level retrievers collapse these into a single similarity score...
By Italo Luis da Silva, Hanqi Yan, Yujing Wang, Jiangnan Ye, Lin Gui, Yulan He
arXiv:2609.36359v1 Announce Type: new
Abstract: Graph-based approximate nearest neighbor search (ANNS) is widely used for large-scale semantic search. Its indices are constructed primarily based on g...
By Fangzhou Wu, Haike Xu, Sandeep Silwal