Tootfinder

Beyond Interactions: Node-Level Graph Generation for Knowledge-Free Augmentation in Recommender Systems
Zhaoyan Wang, Hyunjun Ahn, In-Young Ko
https://arxiv.org/abs/2507.20578 h…

Beyond Interactions: Node-Level Graph Generation for Knowledge-Free Augmentation in Recommender Systems
Recent advances in recommender systems rely on external resources such as knowledge graphs or large language models to enhance recommendations, which limit applicability in real-world settings due to data dependency and computational overhead. Although knowledge-free models are able to bolster recommendations by direct edge operations as well, the absence of augmentation primitives drives them to fall short in bridging semantic and structural gaps as high-quality paradigm substitutes. Unlike ex…

Prometheus: Unified Knowledge Graphs for Issue Resolution in Multilingual Codebases
Zimin Chen, Yue Pan, Siyu Lu, Jiayi Xu, Claire Le Goues, Martin Monperrus, He Ye
https://arxiv.org/abs/2507.19942

Prometheus: Unified Knowledge Graphs for Issue Resolution in Multilingual Codebases
Language model (LM) agents, such as SWE-agent and OpenHands, have made progress toward automated issue resolution. However, existing approaches are often limited to Python-only issues and rely on pre-constructed containers in SWE-bench with reproduced issues, restricting their applicability to real-world and work for multi-language repositories. We present Prometheus, designed to resolve real-world issues beyond benchmark settings. Prometheus is a multi-agent system that transforms an entire co…

Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning
Aleksandr Perevalov, Andreas Both
https://arxiv.org/abs/2507.16971

Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning
Accessing knowledge via multilingual natural-language interfaces is one of the emerging challenges in the field of information retrieval and related ones. Structured knowledge stored in knowledge graphs can be queried via a specific query language (e.g., SPARQL). Therefore, one needs to transform natural-language input into a query to fulfill an information need. Prior approaches mostly focused on combining components (e.g., rule-based or neural-based) that solve downstream tasks and come up wi…

Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs
Vadim Zadykian, Bruno Andrade, Haithem Afli
https://arxiv.org/abs/2509.09522

Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs
Semantic Textual Relatedness (STR) captures nuanced relationships between texts that extend beyond superficial lexical similarity. In this study, we investigate STR in the context of job title matching - a key challenge in resume recommendation systems, where overlapping terms are often limited or misleading. We introduce a self-supervised hybrid architecture that combines dense sentence embeddings with domain-specific Knowledge Graphs (KGs) to improve both semantic alignment and explainability…

Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking
Shengbo Gong, Xianfeng Tang, Carl Yang, Wei jin
https://arxiv.org/abs/2508.02435 https:/…

Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking
Retrieval-augmented generation (RAG) is critical for reducing hallucinations and incorporating external knowledge into Large Language Models (LLMs). However, advanced RAG systems face a trade-off between performance and efficiency. Multi-round RAG approaches achieve strong reasoning but incur excessive LLM calls and token costs, while Graph RAG methods suffer from computationally expensive, error-prone graph construction and retrieval redundancy. To address these challenges, we propose T$^2$RAG…