Tootfinder

AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents
Md Tahmid Rahman Laskar, Julien Bouvier Tremblay, Xue-Yong Fu, Cheng Chen, Shashi Bhushan TN
https://arxiv.org/abs/2510.08149

AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents
The utilization of conversational AI systems by leveraging Retrieval Augmented Generation (RAG) techniques to solve customer problems has been on the rise with the rapid progress of Large Language Models (LLMs). However, the absence of a company-specific dedicated knowledge base is a major barrier to the integration of conversational AI systems in contact centers. To this end, we introduce AI Knowledge Assist, a system that extracts knowledge in the form of question-answer (QA) pairs from histo…

Un cadre paraconsistant pour l'{\'e}valuation de similarit{\'e} dans les bases de connaissances
Jos\'e-Luis Vilchis Medina (ENSTA Bretagne, Lab-STICC, Lab-STICC_ROBEX)
https://arxiv.org/abs/2509.08433

Un cadre paraconsistant pour l'{é}valuation de similarit{é} dans les bases de connaissances
This article proposes a paraconsistent framework for evaluating similarity in knowledge bases. Unlike classical approaches, this framework explicitly integrates contradictions, enabling a more robust and interpretable similarity measure. A new measure $ S^* $ is introduced, which penalizes inconsistencies while rewarding shared properties. Paraconsistent super-categories $ Ξ_K^* $ are defined to hierarchically organize knowledge entities. The model also includes a contradiction extractor $ E $…

Seeing the Forest Through the Trees: Knowledge Retrieval for Streamlining Particle Physics Analysis
James McGreivy, Blaise Delaney, Anja Beck, Mike Williams
https://arxiv.org/abs/2509.06855

Seeing the Forest Through the Trees: Knowledge Retrieval for Streamlining Particle Physics Analysis
Generative Large Language Models (LLMs) are a promising approach to structuring knowledge contained within the corpora of research literature produced by large-scale and long-running scientific collaborations. Within experimental particle physics, such structured knowledge bases could expedite methodological and editorial review. Complementarily, within the broader scientific community, generative LLM systems grounded in published work could make for reliable companions allowing non-experts to …

Truth-Aware Decoding: A Program-Logic Approach to Factual Language Generation
Faruk Alpay, Hamdi Alakkad
https://arxiv.org/abs/2510.07331 https://arxiv.org…

Truth-Aware Decoding: A Program-Logic Approach to Factual Language Generation
This paper introduces Truth-Aware Decoding (TAD), a verification-oriented decoding scheme that aligns neural language generation with knowledge bases. Situated in the tradition of probabilistic program semantics for sequence models, TAD augments modern instruction-tuned systems with a lattice of semantic guards that operate at decode time. Our contributions are fourfold: (i) a constraint-based semantics that renders oracle filtering as a program-logic judgment, (ii) a proof that greedy selectio…

DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation
Xinyu Gao, Xiangtao Meng, Yingkai Dong, Zheng Li, Shanqing Guo
https://arxiv.org/abs/2509.06026

DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation
While Retrieval-Augmented Generation (RAG) effectively reduces hallucinations by integrating external knowledge bases, it introduces vulnerabilities to membership inference attacks (MIAs), particularly in systems handling sensitive data. Existing MIAs targeting RAG's external databases often rely on model responses but ignore the interference of non-member-retrieved documents on RAG outputs, limiting their effectiveness. To address this, we propose DCMI, a differential calibration MIA that miti…

How People Manage Knowledge in their "Second Brains"- A Case Study with Industry Researchers Using Obsidian
Juliana Jansen Ferreira, Vin\'icius Segura, Joana Gabriela Souza, Joao Henrique Gallas Brasil
https://arxiv.org/abs/2509.20187

How People Manage Knowledge in their "Second Brains"- A Case Study with Industry Researchers Using Obsidian
People face overwhelming information during work activities, necessitating effective organization and management strategies. Even in personal lives, individuals must keep, annotate, organize, and retrieve knowledge from daily routines. The collection of records for future reference is known as a personal knowledge base. Note-taking applications are valuable tools for building and maintaining these bases, often called a ''second brain''. This paper presents a case study on how people build and e…

Mind the Gap: Aligning Knowledge Bases with User Needs to Enhance Mental Health Retrieval
Amanda Chan, James Jiayu Liu, He Kai, Onno P. Kampman
https://arxiv.org/abs/2509.13626 …

Mind the Gap: Aligning Knowledge Bases with User Needs to Enhance Mental Health Retrieval
Access to reliable mental health information is vital for early help-seeking, yet expanding knowledge bases is resource-intensive and often misaligned with user needs. This results in poor performance of retrieval systems when presented concerns are not covered or expressed in informal or contextualized language. We present an AI-based gap-informed framework for corpus augmentation that authentically identifies underrepresented topics (gaps) by overlaying naturalistic user data such as forum po…

Semantic Bridges Between First Order c-Representations and Cost-Based Semantics: An Initial Perspective
Nicholas Leisegang, Giovanni Casini, Thomas Meyer
https://arxiv.org/abs/2510.00817

Semantic Bridges Between First Order c-Representations and Cost-Based Semantics: An Initial Perspective
Weighted-knowledge bases and cost-based semantics represent a recent formalism introduced by Bienvenu et al. for Ontology Mediated Data Querying in the case where a given knowledge base is inconsistent. This is done by adding a weight to each statement in the knowledge base (KB), and then giving each DL interpretation a cost based on how often it breaks rules in the KB. In this paper we compare this approach with c-representations, a form of non-monotonic reasoning originally introduced by Kern…

"The web was supposed to evolve into semantically structured, linked, machine-readable data that would enable amazing opportunities. That never happened. (...) We also never achieved truly personal computing. Computers could've been personal knowledge bases, with structured semantic connections akin to HyperCard, that take advantage of the semantic web and open standards. (...) AI is not a triumph of elegant design, but a brute-force workaround."

Semantic Bridges Between First Order c-Representations and Cost-Based Semantics: An Initial Perspective
Nicholas Leisegang, Giovanni Casini, Thomas Meyer
https://arxiv.org/abs/2510.00817

Semantic Bridges Between First Order c-Representations and Cost-Based Semantics: An Initial Perspective
Weighted-knowledge bases and cost-based semantics represent a recent formalism introduced by Bienvenu et al. for Ontology Mediated Data Querying in the case where a given knowledge base is inconsistent. This is done by adding a weight to each statement in the knowledge base (KB), and then giving each DL interpretation a cost based on how often it breaks rules in the KB. In this paper we compare this approach with c-representations, a form of non-monotonic reasoning originally introduced by Kern…

RadAgents: Multimodal Agentic Reasoning for Chest X-ray Interpretation with Radiologist-like Workflows
Kai Zhang, Corey D Barrett, Jangwon Kim, Lichao Sun, Tara Taghavi, Krishnaram Kenthapadi
https://arxiv.org/abs/2509.20490

RadAgents: Multimodal Agentic Reasoning for Chest X-ray Interpretation with Radiologist-like Workflows
Agentic systems offer a potential path to solve complex clinical tasks through collaboration among specialized agents, augmented by tool use and external knowledge bases. Nevertheless, for chest X-ray (CXR) interpretation, prevailing methods remain limited: (i) reasoning is frequently neither clinically interpretable nor aligned with guidelines, reflecting mere aggregation of tool outputs; (ii) multimodal evidence is insufficiently fused, yielding text-only rationales that are not visually grou…

ConvergeWriter: Data-Driven Bottom-Up Article Construction
Binquan Ji, Jiaqi Wang, Ruiting Li, Xingchen Han, Yiyang Qi, Shichao Wang, Yifei Lu, Yuantao Han, Feiliang Ren
https://arxiv.org/abs/2509.12811

ConvergeWriter: Data-Driven Bottom-Up Article Construction
Large Language Models (LLMs) have shown remarkable prowess in text generation, yet producing long-form, factual documents grounded in extensive external knowledge bases remains a significant challenge. Existing "top-down" methods, which first generate a hypothesis or outline and then retrieve evidence, often suffer from a disconnect between the model's plan and the available knowledge, leading to content fragmentation and factual inaccuracies. To address these limitations, we propose a novel "b…