Tootfinder

Global Patterns of Knowledge: Language, Genre, and the Geography of Knowledge
Akira Matsui, Fujio Toriumi, Mitsuo Yoshida, Taichi Murayama, Shiori Hironaka
https://arxiv.org/abs/2507.22271

Global Patterns of Knowledge: Language, Genre, and the Geography of Knowledge
Online platforms, particularly Wikipedia, have become critical infrastructures for providing diverse linguistic and cultural contexts. This human-curated knowledge now forms the foundation for modern AI. However, we have not yet fully explored how knowledge production capability vary across languages and domains. Here, we address this gap by applying economic complexity analysis to understand the editing history of Wikipedia platforms. This approach allows us to infer the latent mode of ``knowl…

Knowledge Is More Than Performance: How Knowledge Diversity Drives Human-Human and Human-AI Interaction Synergy and Reveals Pure-AI Interaction Shortfalls
Tom Sheffer, Alon Miron, Yaniv Dover, Ariel Goldstein
https://arxiv.org/abs/2507.22889

Knowledge Is More Than Performance: How Knowledge Diversity Drives Human-Human and Human-AI Interaction Synergy and Reveals Pure-AI Interaction Shortfalls
Conversations transform individual knowledge into collective insight, allowing groups of humans and increasingly groups of artificial intelligence (AI) agents to collaboratively solve complex problems. Whether interactions between AI agents can replicate the synergy observed in human discussions remains an open question. To investigate this, we systematically compared four conversational configurations: pairs of large language models (LLM-LLM), trios of LLMs, trios of humans, and mixed human-LL…

In its white paper ‘Beyond AI & Copyright’, @… emphasises two structural risks of AI-supported access to knowledge:
1. the concentration of control over knowledge in the hands of a few powerful actors
2. the erosion of the institutions and economic foundations of human information production.
To preserve digital knowledge communities, he calls for …

DriveQA: Passing the Driving Knowledge Test
Maolin Wei, Wanzhou Liu, Eshed Ohn-Bar
https://arxiv.org/abs/2508.21824 https://arxiv.org/pdf/2508.21824…

DriveQA: Passing the Driving Knowledge Test
If a Large Language Model (LLM) were to take a driving knowledge test today, would it pass? Beyond standard spatial and visual question-answering (QA) tasks on current autonomous driving benchmarks, driving knowledge tests require a complete understanding of all traffic rules, signage, and right-of-way principles. To pass this test, human drivers must discern various edge cases that rarely appear in real-world datasets. In this work, we present DriveQA, an extensive open-source text and vision-…

Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
Siyuan Li, Ruitong Liu, Yan Wen, Te Sun
https://arxiv.org/abs/2506.23141

Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
Semantic context surrounding a triplet $(h, r, t)$ is crucial for Knowledge Graph Completion (KGC), providing vital cues for prediction. However, traditional node-based message passing mechanisms, when applied to knowledge graphs, often introduce noise and suffer from information dilution or over-smoothing by indiscriminately aggregating information from all neighboring edges. To address this challenge, we propose a semantic-aware relational message passing. A core innovation of this framework …

Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
Nasim Shirvani-Mahdavi, Devin Wingfield, Amin Ghasemi, Chengkai Li
https://arxiv.org/abs/2507.23740 …

Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
Knowledge graphs (KGs) often contain sufficient information to support the inference of new facts. Identifying logical rules not only improves the completeness of a knowledge graph but also enables the detection of potential errors, reveals subtle data patterns, and enhances the overall capacity for reasoning and interpretation. However, the complexity of such rules, combined with the unique labeling conventions of each KG, can make them difficult for humans to understand. In this paper, we exp…

SciMantify -- A Hybrid Approach for the Evolving Semantification of Scientific Knowledge
Lena John, Kheir Eddine Farfar, S\"oren Auer, Oliver Karras
https://arxiv.org/abs/2506.21819

SciMantify -- A Hybrid Approach for the Evolving Semantification of Scientific Knowledge
Scientific publications, primarily digitized as PDFs, remain static and unstructured, limiting the accessibility and reusability of the contained knowledge. At best, scientific knowledge from publications is provided in tabular formats, which lack semantic context. A more flexible, structured, and semantic representation is needed to make scientific knowledge understandable and processable by both humans and machines. We propose an evolution model of knowledge representation, inspired by the 5-…

Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitation
Yufei Chen, Yao Wang, Haibin Zhang, Tao Gu
https://arxiv.org/abs/2507.23229

Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitation
Retrieval-augmented generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge bases, but this advancement introduces significant privacy risks. Existing privacy attacks on RAG systems can trigger data leakage but often fail to accurately isolate knowledge-base-derived sentences within mixed responses. They also lack robustness when applied across multiple domains. This paper addresses these challenges by presenting a novel black-box attack framework that ex…

Bug Fixing with Broader Context: Enhancing LLM-Based Program Repair via Layered Knowledge Injection
Ramtin Ehsani, Esteban Parra, Sonia Haiduc, Preetha Chatterjee
https://arxiv.org/abs/2506.24015

Bug Fixing with Broader Context: Enhancing LLM-Based Program Repair via Layered Knowledge Injection
Prompting LLMs with bug-related context (e.g., error messages, stack traces) improves automated program repair, but many bugs still remain unresolved. In real-world projects, developers often rely on broader repository and project-level context beyond the local code to resolve such bugs. In this paper, we investigate how automatically extracting and providing such knowledge can improve LLM-based program repair. We propose a layered knowledge injection framework that incrementally augments LLMs …

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