Table-text retrieval aims to retrieve relevant tables and text to support open-domain question answering. Existing studies use either early or late fusion, but face limitations. Early fusion pre-aligns a table row with its associated passages, forming “stars,” which often include irrelevant contexts and miss query-dependent relationships. Late fusion retrieves individual nodes, dynamically aligning them, but it risks missing relevant contexts. Both approaches also struggle with advanced reasoning tasks, such as column-wise aggregation and multi-hop reasoning. To address these issues, we propose HELIOS, which combines the strengths of both approaches. First, the edge-based bipartite subgraph retrieval identifies finer-grained edges between table segments and passages, effectively avoiding the inclusion of irrelevant contexts. Then, the query-relevant node expansion identifies the most promising nodes, dynamically retrieving relevant edges to grow the bipartite subgraph, minimizing the risk of missing important contexts. Lastly, the star-based LLM refinement performs logical inference at the star graph level rather than the bipartite subgraph, supporting advanced reasoning tasks. Experimental results show that HELIOS outperforms state-of-the-art models with a significant improvement up to 42.6% and 39.9% in recall and nDCG, respectively, on the OTT-QA benchmark.

Late-interaction based multi-vector retrieval systems have greatly advanced the field of information retrieval by enabling fast and accurate search over millions of documents. However, these systems rely on a naive summation of token-level similarity scores which often leads to inaccurate relevance estimation caused by the tokenization of semantic units (e.g., words and phrases) and the influence of low-content words (e.g., articles and prepositions). To address these challenges, we propose **TRIAL**: **T**oken **R**elations and **I**mportance **A**ware **L**ate-interaction, which enhances late interaction by explicitly modeling token relations and token importance in relevance scoring. Extensive experiments on three widely used benchmarks show that TRIAL achieves state-of-the-art accuracy, with an nDCG@10 of 46.3 on MSMARCO (in-domain), and average nDCG@10 scores of 51.09 and 72.15 on BEIR and LoTTE Search (out-of-domain), respectively. With superior accuracy, TRIAL maintains competitive retrieval speed compared to existing late-interaction methods, making it a practical solution for large-scale text retrieval.

To reduce hallucinations in large language models (LLMs), researchers are increasingly investigating reasoning methods that integrate LLMs with external knowledge graphs (KGs). Existing approaches either map an LLM-generated query graph onto the KG or let the LLM traverse the entire graph; the former is fragile because noisy query graphs derail retrieval, whereas the latter is inefficient due to entity-level reasoning over large graphs. In order to tackle these problems, we propose **SAFE** (**S**chema-Driven **A**pproximate Distance Join **F**or **E**fficient Knowledge Graph Querying), a framework that leverages schema graphs for robust query graph generation and efficient KG retrieval. SAFE introduces two key ideas: (1) an Approximate Distance Join (ADJ) algorithm that refines LLM-generated pseudo query graphs by flexibly aligning them with the KG’s structure; and (2) exploiting a compact schema graph to perform ADJ efficiently, reducing overhead and improving retrieval accuracy. Extensive experiments on WebQSP, CWQ and GrailQA demonstrate that SAFE outperforms state-of-the-art methods in both accuracy and efficiency, providing a robust and scalable solution to overcome the inherent limitations of LLM-based knowledge retrieval.

Multimodal document retrieval aims to retrieve query-relevant components from documents composed of textual, tabular, and visual elements. An effective multimodal retriever needs to handle two main challenges: (1) mitigate the effect of irrelevant contents caused by fixed, single-granular retrieval units, and (2) support multihop reasoning by effectively capturing semantic relationships among components within and across documents. To address these challenges, we propose LILaC, a multimodal retrieval framework featuring two core innovations. First, we introduce a layered component graph, explicitly representing multimodal information at two layers—each representing coarse and fine granularity—facilitating efficient yet precise reasoning. Second, we develop a late-interaction-based subgraph retrieval method, an edge-based approach that initially identifies coarse-grained nodes for efficient candidate generation, then performs fine-grained reasoning via late interaction. Extensive experiments demonstrate that LILaC achieves state-of-the-art retrieval performance on all five benchmarks, notably without additional fine-tuning. We make the artifacts publicly available at github.com/joohyung00/lilac.