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OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases
Yash Tiwari, Owais Ahmad Lone, Mayukha Pal
https://arxiv.org/abs/2506.00664

OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases
Ontologies are pivotal for structuring knowledge bases to enhance question answering (QA) systems powered by Large Language Models (LLMs). However, traditional ontology creation relies on manual efforts by domain experts, a process that is time intensive, error prone, and impractical for large, dynamic knowledge domains. This paper introduces OntoRAG, an automated pipeline designed to derive ontologies from unstructured knowledge bases, with a focus on electrical relay documents. OntoRAG integr…

Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering
Linhao Ye, Lang Yu, Zhikai Lei, Qin Chen, Jie Zhou, Liang He
https://arxiv.org/abs/2506.00491

Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering
Retrieval-augmented generation (RAG) is usually integrated into large language models (LLMs) to mitigate hallucinations and knowledge obsolescence. Whereas,conventional one-step retrieve-and-read methods are insufficient for multi-hop question answering, facing challenges of retrieval semantic mismatching and the high cost in handling interdependent subquestions. In this paper, we propose Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering (Q-DREAM). …

Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability
Genta Indra Winata, David Anugraha, Emmy Liu, Alham Fikri Aji, Shou-Yi Hung, Aditya Parashar, Patrick Amadeus Irawan, Ruochen Zhang, Zheng-Xin Yong, Jan Christian Blaise Cruz, Niklas Muennighoff, Seungone Kim, Hanyang Zhao, Sudipta Kar, Kezia Erina Suryoraharjo, M. Farid Adilazuarda, En-Shiun Annie Lee, Ayu Purwarianti, Derry Tanti Wijaya, Monojit Choudhury

Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability
High-quality datasets are fundamental to training and evaluating machine learning models, yet their creation-especially with accurate human annotations-remains a significant challenge. Many dataset paper submissions lack originality, diversity, or rigorous quality control, and these shortcomings are often overlooked during peer review. Submissions also frequently omit essential details about dataset construction and properties. While existing tools such as datasheets aim to promote transparency…

Finsler and sub-Finsler geodesics with chattering
L. V. Lokutsievskiy, M. I. Zelikin
https://arxiv.org/abs/2505.24474 https://arxiv.o…

Finsler and sub-Finsler geodesics with chattering
In this paper, we provide examples of Finsler and sub-Finsler manifolds whose geodesics exhibit chattering, that is, a countable number of switches over an arbitrarily small time interval. We also present an explicit left-invariant structure on a Carnot group whose geodesics exhibit chattering. This provides a negative answer to Le Donne's question. Furthermore, the paper presents a sufficient condition for normal Pontryagin maximum principle extremals in (sub-)Finsler problems to exhibit chatt…

Judgment as Coordination: A Joint Systems View of Visualization Design Practice
Paul C. Parsons, Arran Ridley
https://arxiv.org/abs/2507.01209 https://

Judgment as Coordination: A Joint Systems View of Visualization Design Practice
Professional visualization design has become an increasingly important area of inquiry, yet much of the field's discourse remains anchored in researcher-centered contexts. Studies of design practice often focus on individual designers' decisions and reflections, offering limited insight into the collaborative and systemic dimensions of professional work. In this paper, we propose a systems-level reframing of design judgment grounded in the coordination and adaptation that sustain progress amid …

This https://arxiv.org/abs/2503.08720 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

AI for Just Work: Constructing Diverse Imaginations of AI beyond "Replacing Humans"
"why" we develop AI. Lacking critical reflections on the general visions and purposes of AI may make the community vulnerable to manipulation. In this position paper, we explore the "why" question of AI. We denote answers to the "why" question the imaginations of AI, which depict our general visions, frames, and mindsets for the prospects of AI. We identify that the prevailing vision in the AI community is largely a monoculture that emphasizes objectives such as replacing humans and improving p…

This https://arxiv.org/abs/2405.17286 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Asymptotics of extensions of simple $\mathbb Q$-algebras
We answer various questions concerning the distribution of extensions of a given central simple algebra $K$ over a number field. Specifically, we give asymptotics for the count of inner Galois extensions $L/K$ of fixed degree and center with bounded discriminant. We also relate the distribution of outer extensions of $K$ to the distribution of field extensions of its center $Z(K)$. This paper generalizes the study of asymptotics of field extensions to the noncommutative case in an analogous man…

Even-degeneracy of a random graph
Ting-Wei Chao, Dingding Dong, Zixuan Xu
https://arxiv.org/abs/2506.01021 https://arxiv.org/pdf/2506…

Even-degeneracy of a random graph
A graph is even-degenerate if one can iteratively remove a vertex of even degree at each step until at most one edge remains. Recently, Janzer and Yip showed that the Erdős--Renyi random graph $G(n,1/2)$ is even-degenerate with high probability, and asked whether an analogous result holds for any general $G(n,p)$. In this paper, we answer this question for any constant $p\in (0,1)$ in affirmation by proving that $G(n,p)$ is even-degenerate with high probability.

Sensitivity and Query Complexity under Uncertainty
Deepu Benson, Balagopal Komarath, Nikhil Mande, Sai Soumya Nalli, Jayalal Sarma, Karteek Sreenivasaiah
https://arxiv.org/abs/2507.00148

Sensitivity and Query Complexity under Uncertainty
In this paper, we study the query complexity of Boolean functions in the presence of uncertainty, motivated by parallel computation with an unlimited number of processors where inputs are allowed to be unknown. We allow each query to produce three results: zero, one, or unknown. The output could also be: zero, one, or unknown, with the constraint that we should output ''unknown'' only when we cannot determine the answer from the revealed input bits. Such an extension of a Boolean function is ca…

The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets
Shenzhe Zhu, Jiao Sun, Yi Nian, Tobin South, Alex Pentland, Jiaxin Pei
https://arxiv.org/abs/2506.00073

The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets
AI agents are increasingly used in consumer-facing applications to assist with tasks such as product search, negotiation, and transaction execution. In this paper, we explore a future scenario where both consumers and merchants authorize AI agents to fully automate negotiations and transactions. We aim to answer two key questions: (1) Do different LLM agents vary in their ability to secure favorable deals for users? (2) What risks arise from fully automating deal-making with AI agents in consum…