TOPIC

knowledge

Aggregated news, research, and updates specifically regarding knowledge. Auto-curated by our AI Engine.

product #voice 📝 BlogAnalyzed: Jan 18, 2026 08:45

Real-Time AI Voicebot Answers Company Knowledge with OpenAI and RAG!

Published:Jan 18, 2026 08:37

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1 min read

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Zenn AI

Analysis

This is fantastic! The article showcases a cutting-edge voicebot built using OpenAI's Realtime API and Retrieval-Augmented Generation (RAG) to access and answer questions based on a company's internal knowledge base. The integration of these technologies opens exciting possibilities for improved internal communication and knowledge sharing.

Key Takeaways

•Leverages OpenAI's Realtime API for a responsive voicebot experience.
•Employs RAG to provide answers grounded in the company's knowledge base.
•Demonstrates a practical application of AI for improved internal workflows.

Reference

“The bot uses RAG (Retrieval-Augmented Generation) to answer based on search results.”

Permalink Zenn AI

product #voice 📝 BlogAnalyzed: Jan 18, 2026 08:45

Building a Conversational AI Knowledge Base with OpenAI Realtime API!

Published:Jan 18, 2026 08:35

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1 min read

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Qiita AI

Analysis

This project showcases an exciting application of OpenAI's Realtime API! The development of a voice bot for internal knowledge bases using cutting-edge technology like RAG is a fantastic way to streamline information access and improve employee efficiency. This innovation promises to revolutionize how teams interact with and utilize internal data.

Key Takeaways

•Leverages OpenAI's Realtime API for real-time interaction.
•Employs RAG (Retrieval-Augmented Generation) for improved knowledge access.
•Focuses on creating a voice bot for internal company knowledge bases.

Reference

“The article's focus on OpenAI's Realtime API highlights its potential for creating responsive, engaging conversational AI.”

Permalink Qiita AI

research #llm 📝 BlogAnalyzed: Jan 16, 2026 16:02

Groundbreaking RAG System: Ensuring Truth and Transparency in LLM Interactions

Published:Jan 16, 2026 15:57

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1 min read

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r/mlops

Analysis

This innovative RAG system tackles the pervasive issue of LLM hallucinations by prioritizing evidence. By implementing a pipeline that meticulously sources every claim, this system promises to revolutionize how we build reliable and trustworthy AI applications. The clickable citations are a particularly exciting feature, allowing users to easily verify the information.

Key Takeaways

•The system guarantees no hallucinations by grounding all claims in a curated knowledge base.
•It uses a hybrid retrieval method with LLM reranking and confidence scoring for enhanced accuracy.
•Clickable citations provide users with direct access to the source material, promoting transparency.

Reference

“I built an evidence-first pipeline where: Content is generated only from a curated KB; Retrieval is chunk-level with reranking; Every important sentence has a clickable citation → click opens the source”

Permalink r/mlops

business #wikipedia 📝 BlogAnalyzed: Jan 16, 2026 06:47

Wikipedia: A Quarter-Century of Knowledge and Innovation

Published:Jan 16, 2026 06:40

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1 min read

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Techmeme

Analysis

As Wikipedia celebrates its 25th anniversary, it continues to be a vibrant hub of information and collaborative editing. The platform's resilience in the face of evolving challenges showcases its enduring value and adaptability in the digital age.

Key Takeaways

•Wikipedia is celebrating its 25th anniversary, marking a significant milestone for the online encyclopedia.
•The article discusses the challenges Wikipedia faces, offering insight into its continued evolution.
•This coverage highlights the platform's enduring influence and importance in the information landscape.

Reference

“As the website turns 25, it faces myriad challenges...”

Permalink Techmeme

research #llm 🔬 ResearchAnalyzed: Jan 15, 2026 07:04

Tri-Agent Framework Enhances LLM Stability & Explainability Through Recursive Knowledge Synthesis

Published:Jan 15, 2026 05:00

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1 min read

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ArXiv NLP

Analysis

This research is significant because it tackles the critical challenge of ensuring stability and explainability in increasingly complex multi-LLM systems. The use of a tri-agent architecture and recursive interaction offers a promising approach to improve the reliability of LLM outputs, especially when dealing with public-access deployments. The application of fixed-point theory to model the system's behavior adds a layer of theoretical rigor.

Key Takeaways

•A tri-agent framework (semantic generation, consistency check, transparency audit) is used to enhance multi-LLM system reliability.
•Recursive Knowledge Synthesis (RKS) is achieved through iterative interaction of the three agents.
•Empirical evaluation shows high convergence rates and strong transparency scores in public-access LLM deployments.

Reference

“Approximately 89% of trials converged, supporting the theoretical prediction that transparency auditing acts as a contraction operator within the composite validation mapping.”

Permalink ArXiv NLP

product #llm 📝 BlogAnalyzed: Jan 15, 2026 07:01

Boosting Obsidian Productivity: How Claude Desktop Solves Knowledge Management Challenges

Published:Jan 15, 2026 02:54

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1 min read

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Zenn Claude

Analysis

This article highlights a practical application of AI, using Claude Desktop to enhance personal knowledge management within Obsidian. It addresses common pain points such as lack of review, information silos, and knowledge reusability, demonstrating a tangible workflow improvement. The value proposition centers on empowering users to transform their Obsidian vaults from repositories into actively utilized knowledge assets.

Key Takeaways

•Claude Desktop is used to improve Obsidian's knowledge management capabilities.
•The article focuses on addressing challenges related to review, information access, and knowledge reuse.
•The integration aims to transform Obsidian into a more actively used knowledge asset.

Reference

“This article will introduce how to achieve the following three things with Claude Desktop × Obsidian: have AI become a reviewer, cross-reference information, and accumulate and reuse development insights.”

Permalink Zenn Claude

product #llm 📝 BlogAnalyzed: Jan 14, 2026 07:30

Unlocking AI's Potential: Questioning LLMs to Improve Prompts

Published:Jan 14, 2026 05:44

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1 min read

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Zenn LLM

Analysis

This article highlights a crucial aspect of prompt engineering: the importance of extracting implicit knowledge before formulating instructions. By framing interactions as an interview with the LLM, one can uncover hidden assumptions and refine the prompt for more effective results. This approach shifts the focus from directly instructing to collaboratively exploring the knowledge space, ultimately leading to higher quality outputs.

Key Takeaways

•Implicit knowledge is a significant barrier to effective LLM interaction.
•Prompt engineering benefits from treating the interaction as an interview process.
•Questioning the LLM can reveal hidden assumptions and refine prompts.

Reference

“This approach shifts the focus from directly instructing to collaboratively exploring the knowledge space, ultimately leading to higher quality outputs.”

Permalink Zenn LLM

business #agent 📝 BlogAnalyzed: Jan 15, 2026 07:00

Daily Routine for Aspiring CAIOs: A Structured Approach

Published:Jan 13, 2026 23:00

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1 min read

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Zenn GenAI

Analysis

This article outlines a structured daily routine designed for individuals aiming to become CAIOs, emphasizing consistent workflows and the accumulation of knowledge. The framework's focus on structured thinking (Why, How, What, Impact, Me) offers a practical approach to analyzing information and developing critical thinking skills vital for leadership roles.

Key Takeaways

•The routine focuses on a Monday-Saturday workflow.
•The primary goal is to establish a consistent daily flow.
•The analysis framework includes 'Why, How, What, Impact, and Me' perspectives.

Reference

“The article emphasizes a structured approach, focusing on 'Why, How, What, Impact, and Me' perspectives for analysis.”

Permalink Zenn GenAI

ethics #llm 📝 BlogAnalyzed: Jan 11, 2026 19:15

Why AI Hallucinations Alarm Us More Than Dictionary Errors

Published:Jan 11, 2026 14:07

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1 min read

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Zenn LLM

Analysis

This article raises a crucial point about the evolving relationship between humans, knowledge, and trust in the age of AI. The inherent biases we hold towards traditional sources of information, like dictionaries, versus newer AI models, are explored. This disparity necessitates a reevaluation of how we assess information veracity in a rapidly changing technological landscape.

Key Takeaways

•AI hallucinations are immediately exposed, leading to greater scrutiny.
•Dictionaries benefit from a long-standing societal trust, making errors less noticeable.
•The article explores the mechanics of human knowledge and trust, highlighting biases.

Reference

“Dictionaries, by their very nature, are merely tools for humans to temporarily fix meanings. However, the illusion of 'objectivity and neutrality' that their format conveys is the greatest...”

Permalink Zenn LLM

research #llm 📝 BlogAnalyzed: Jan 10, 2026 22:00

AI: From Tool to Silent, High-Performing Colleague - Understanding the Nuances

Published:Jan 10, 2026 21:48

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1 min read

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Qiita AI

Analysis

The article highlights a critical tension in current AI development: high performance in specific tasks versus unreliable general knowledge and reasoning leading to hallucinations. Addressing this requires a shift from simply increasing model size to improving knowledge representation and reasoning capabilities. This impacts user trust and the safe deployment of AI systems in real-world applications.

Key Takeaways

•AI models can achieve high scores on standardized tests.
•AI models are prone to hallucinations, or generating false information.
•Addressing AI hallucinations is crucial for trustworthy AI applications.

Reference

“"AIは難関試験に受かるのに、なぜ平気で嘘をつくのか？"”

Permalink Qiita AI

infrastructure #git 📝 BlogAnalyzed: Jan 10, 2026 20:00

Beyond GitHub: Designing Internal Git for Robust Development

Published:Jan 10, 2026 15:00

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1 min read

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Zenn ChatGPT

Analysis

This article highlights the importance of internal-first Git practices for managing code and decision-making logs, especially for small teams. It emphasizes architectural choices and rationale rather than a step-by-step guide. The approach caters to long-term knowledge preservation and reduces reliance on a single external platform.

Key Takeaways

•The article advocates for an internal-first approach to Git repository management.
•It emphasizes the importance of documenting design decisions alongside code.
•The rationale is to reduce dependency on external platforms like GitHub and ensure long-term knowledge retention.

Reference

“なぜ GitHub だけに依存しない構成を選んだのかどこを一次情報（正）として扱うことにしたのかその判断を、どう構造で支えることにしたのか”

Permalink Zenn ChatGPT

product #rag 📝 BlogAnalyzed: Jan 10, 2026 05:00

Package-Based Knowledge for Personalized AI Assistants

Published:Jan 9, 2026 15:11

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1 min read

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Zenn AI

Analysis

The concept of modular knowledge packages for AI assistants is compelling, mirroring software dependency management for increased customization. The challenge lies in creating a standardized format and robust ecosystem for these knowledge packages, ensuring quality and security. The idea would require careful consideration of knowledge representation and retrieval methods.

Key Takeaways

•The article proposes a 'knowledge npm' for AI assistants.
•Users could install specialized knowledge via command line.
•Examples include Next.js expertise and freelance tax knowledge.

Reference

“"If knowledge bases could be installed as additional options, wouldn't it be possible to customize AI assistants?"”

Permalink Zenn AI

Machine Learning #Time Series Analysis, Knowledge Distillation, Efficiency 📝 BlogAnalyzed: Jan 16, 2026 01:52

MemKD: Memory-Discrepancy Knowledge Distillation for Efficient Time Series Classification

Published:Jan 16, 2026 01:52

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1 min read

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Analysis

The article introduces a new method called MemKD for efficient time series classification. This suggests potential improvements in speed or resource usage compared to existing methods. The focus is on Knowledge Distillation, which implies transferring knowledge from a larger or more complex model to a smaller one. The specific area is time series data, indicating a specialization in this type of data analysis.

Key Takeaways

•MemKD is a new method for time series classification.
•It utilizes Knowledge Distillation to potentially improve efficiency.
•Focuses on optimizing performance for time series data.

Reference

“”

Permalink

product #llm 📝 BlogAnalyzed: Jan 6, 2026 18:01

SurfSense: Open-Source LLM Connector Aims to Rival NotebookLM and Perplexity

Published:Jan 6, 2026 12:18

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1 min read

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r/artificial

Analysis

SurfSense's ambition to be an open-source alternative to established players like NotebookLM and Perplexity is promising, but its success hinges on attracting a strong community of contributors and delivering on its ambitious feature roadmap. The breadth of supported LLMs and data sources is impressive, but the actual performance and usability need to be validated.

Key Takeaways

•SurfSense is an open-source project aiming to connect LLMs to various knowledge sources.
•It supports over 100 LLMs, 6000+ embedding models, and 50+ file extensions.
•The project is seeking contributors with expertise in AI agents, RAG, and browser extensions.

Reference

“Connect any LLM to your internal knowledge sources (Search Engines, Drive, Calendar, Notion and 15+ other connectors) and chat with it in real time alongside your team.”

Permalink r/artificial

product #agent 📝 BlogAnalyzed: Jan 6, 2026 07:10

Context Engineering with Notion AI: Beyond Chatbots

Published:Jan 6, 2026 05:51

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1 min read

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Zenn AI

Analysis

This article highlights the potential of Notion AI beyond simple chatbot functionality, emphasizing its ability to leverage workspace context for more sophisticated AI applications. The focus on "context engineering" is a valuable framing for understanding how to effectively integrate AI into existing workflows. However, the article lacks specific technical details on the implementation of these context-aware features.

Key Takeaways

•Notion AI can be used for more than just simple chatbot interactions.
•Page mentions enable agent design within Notion.
•Databases can be used to inject context into AI interactions.

Reference

“"Notion AIは単なるチャットボットではない。"”

Permalink Zenn AI

product #rag 📝 BlogAnalyzed: Jan 6, 2026 07:11

M4 Mac mini RAG Experiment: Local Knowledge Base Construction

Published:Jan 6, 2026 05:22

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1 min read

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Zenn LLM

Analysis

This article documents a practical attempt to build a local RAG system on an M4 Mac mini, focusing on knowledge base creation using Dify. The experiment highlights the accessibility of RAG technology on consumer-grade hardware, but the limited memory (16GB) may pose constraints for larger knowledge bases or more complex models. Further analysis of performance metrics and scalability would strengthen the findings.

Key Takeaways

•The author is building a local RAG system on an M4 Mac mini.
•They are using Dify's knowledge feature for RAG implementation.
•The initial focus is on basic knowledge registration.

Reference

“"画像がダメなら、テキストだ」ということで、今回はDifyのナレッジ（RAG）機能を使い、ローカルのRAG環境を構築します。”

Permalink Zenn LLM

research #llm 🔬 ResearchAnalyzed: Jan 6, 2026 07:21

Unveiling 'Intention Collapse': A Novel Approach to Understanding Reasoning in Language Models

Published:Jan 6, 2026 05:00

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1 min read

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ArXiv NLP

Analysis

This paper introduces a novel concept, 'intention collapse,' and proposes metrics to quantify the information loss during language generation. The initial experiments, while small-scale, offer a promising direction for analyzing the internal reasoning processes of language models, potentially leading to improved model interpretability and performance. However, the limited scope of the experiment and the model-agnostic nature of the metrics require further validation across diverse models and tasks.

Key Takeaways

•Introduces the concept of 'intention collapse' in language models.
•Proposes three model-agnostic intention metrics: Hint, dimeff, and Recov.
•Preliminary experiments show CoT reduces intention entropy and increases effective dimensionality.

Reference

“Every act of language generation compresses a rich internal state into a single token sequence.”

Permalink ArXiv NLP

product #agent 📝 BlogAnalyzed: Jan 4, 2026 09:24

Building AI Agents with Agent Skills and MCP (ADK): A Deep Dive

Published:Jan 4, 2026 09:12

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1 min read

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Qiita AI

Analysis

This article likely details a practical implementation of Google's ADK and MCP for building AI agents capable of autonomous data analysis. The focus on BigQuery and marketing knowledge suggests a business-oriented application, potentially showcasing a novel approach to knowledge management within AI agents. Further analysis would require understanding the specific implementation details and performance metrics.

Key Takeaways

•Article discusses building AI agents using Google's ADK and MCP.
•The agents are designed to autonomously analyze BigQuery data.
•The application focuses on accumulating and utilizing marketing knowledge as 'Skills'.

Reference

“はじめに”

Permalink Qiita AI

product #llm 📝 BlogAnalyzed: Jan 4, 2026 14:42

Transforming ChatGPT History into a Local Knowledge Base with Markdown

Published:Jan 4, 2026 07:58

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1 min read

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Zenn ChatGPT

Analysis

This article addresses a common pain point for ChatGPT users: the difficulty of retrieving specific information from past conversations. By providing a Python-based solution for converting conversation history into Markdown, it empowers users to create a searchable, local knowledge base. The value lies in improved information accessibility and knowledge management for individuals heavily reliant on ChatGPT.

Key Takeaways

•The article provides a method to convert ChatGPT's `conversations.json` to Markdown.
•The conversion is done using Python.
•The resulting Markdown files can be used for local full-text search.

Reference

“"あの結論、どのチャットだっけ？"”

Permalink Zenn ChatGPT

Research #Knowledge Graph 🔬 ResearchAnalyzed: Jan 10, 2026 07:13

Beyond Entity Variance: Deep Dive into Uncertainty in Knowledge Graph Embeddings

Published:Dec 26, 2025 12:38

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1 min read

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ArXiv

Analysis

This ArXiv paper addresses a crucial aspect of knowledge graph embeddings by moving beyond simple variance measures of entities. The research likely offers valuable insights into more robust and nuanced uncertainty modeling for knowledge graph representation and inference.

Key Takeaways

•Explores uncertainty modeling in the context of knowledge graph embeddings.
•Suggests limitations of using only entity variance as a measure of uncertainty.
•Likely proposes new methods for representing and reasoning with uncertain knowledge.

Reference

“The research focuses on decomposing uncertainty in probabilistic knowledge graph embeddings.”

Permalink ArXiv

Research #Model Merging 🔬 ResearchAnalyzed: Jan 10, 2026 07:34

Novel Approach to Model Merging: Leveraging Multi-Teacher Knowledge Distillation

Published:Dec 24, 2025 17:10

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1 min read

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ArXiv

Analysis

This ArXiv paper explores a new methodology for model merging, utilizing multi-teacher knowledge distillation to improve performance and efficiency. The approach likely addresses challenges related to integrating knowledge from multiple models, potentially enhancing their overall capabilities.

Key Takeaways

•Focuses on a new model merging technique.
•Utilizes multi-teacher knowledge distillation.
•Research paper published on ArXiv.

Reference

“The paper focuses on model merging via multi-teacher knowledge distillation.”

Permalink ArXiv

Research #Privacy 🔬 ResearchAnalyzed: Jan 10, 2026 07:43

zkFL-Health: Advancing Privacy in Medical AI with Blockchain and Zero-Knowledge Proofs

Published:Dec 24, 2025 08:29

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1 min read

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ArXiv

Analysis

This research explores a crucial area: protecting patient data privacy in medical AI. The use of blockchain and zero-knowledge federated learning is a promising approach to address these sensitive privacy concerns within healthcare.

Key Takeaways

•Addresses privacy concerns in medical AI.
•Combines blockchain and zero-knowledge proof technologies.
•Focuses on federated learning for collaborative AI development.

Reference

“The article's context highlights the use of blockchain-enabled zero-knowledge federated learning for medical AI privacy.”

Permalink ArXiv

Research #Agent 🔬 ResearchAnalyzed: Jan 10, 2026 07:46

DAO-Agent: Verified Incentives for Decentralized Multi-Agent Systems

Published:Dec 24, 2025 06:00

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1 min read

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ArXiv

Analysis

This research introduces a novel approach to incentivize coordination within decentralized multi-agent systems using zero-knowledge verification. The paper likely explores how to ensure trust and verifiable actions in a distributed environment, potentially impacting the development of more robust and secure AI systems.

Key Takeaways

•Focuses on incentivizing coordination in decentralized multi-agent systems.
•Employs zero-knowledge verification for enhanced security and trust.
•Potentially contributes to more robust and verifiable AI systems.

Reference

“The research focuses on zero-knowledge-verified incentives.”

Permalink ArXiv

Research #AI 🔬 ResearchAnalyzed: Jan 10, 2026 07:48

Unlocking Biomedical Insights: Interpretable AI via Knowledge Graphs

Published:Dec 24, 2025 04:42

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1 min read

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ArXiv

Analysis

This research explores a novel application of knowledge graphs in the field of biomedical research, potentially leading to improved interpretability of AI models. The use of perturbation modeling suggests a method to understand the causal relationships within biomedical data.

Key Takeaways

•Applies knowledge graphs to enhance AI interpretability in biomedical research.
•Employs perturbation modeling to understand causal relationships.
•Focuses on improving the transparency and understanding of AI models.

Reference

“The research focuses on interpretable perturbation modeling.”

Permalink ArXiv

Research #CPS 🔬 ResearchAnalyzed: Jan 10, 2026 07:51

Knowledge Systemization for Resilient Cyber-Physical Systems

Published:Dec 24, 2025 01:30

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1 min read

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ArXiv

Analysis

This ArXiv article likely explores techniques for organizing and structuring knowledge within cyber-physical systems to enhance their robustness. The focus on resilience and fault tolerance suggests a strong emphasis on reliability and safety in critical applications.

Key Takeaways

•Explores methods for systematically organizing knowledge in cyber-physical systems.
•Addresses the improvement of resilience and fault tolerance in these systems.
•Potentially relevant for applications demanding high reliability and safety.

Reference

“The article's core focus is on enhancing the robustness of cyber-physical systems through structured knowledge representation.”

Permalink ArXiv

Research #Multimodal AI 🔬 ResearchAnalyzed: Jan 10, 2026 08:01

Advancing AI: Enhanced Multimodal Understanding and Knowledge Transfer

Published:Dec 23, 2025 16:46

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1 min read

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ArXiv

Analysis

This ArXiv article likely presents novel research in the field of multimodal AI, focusing on improving systems that can process and understand information from different sources like text, images, and audio. The focus on knowledge transfer suggests an attempt to improve AI's ability to generalize and apply learned information across various tasks.

Key Takeaways

•Focus on improving AI's ability to integrate information from multiple data types.
•Potential for advancements in areas like image recognition, natural language processing, and audio analysis.
•Addresses the challenge of transferring learned knowledge between different AI tasks.

Reference

“The article's context indicates it's a research paper published on ArXiv.”

Permalink ArXiv

Research #Agriculture 🔬 ResearchAnalyzed: Jan 10, 2026 08:03

Efficient Deep Learning for Smart Agriculture: A Multi-Objective Hybrid Approach

Published:Dec 23, 2025 15:33

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1 min read

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ArXiv

Analysis

This ArXiv article likely presents a novel method for improving the efficiency of deep learning models used in smart agriculture. The focus on knowledge distillation and multi-objective optimization suggests an attempt to balance model accuracy and computational cost, which is crucial for real-world deployment.

Key Takeaways

Reference

“The article's context suggests the research focuses on applying deep learning to smart agriculture.”

Permalink ArXiv

Research #Reasoning 🔬 ResearchAnalyzed: Jan 10, 2026 08:13

Accelerating Multi-hop Reasoning with Early Knowledge Alignment

Published:Dec 23, 2025 08:14

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1 min read

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ArXiv

Analysis

The research focuses on enhancing multi-hop reasoning in AI, a critical area for complex question answering and knowledge extraction. Early knowledge alignment shows promise in improving efficiency and accuracy in these tasks, as it addresses a core challenge in knowledge-intensive AI applications.

Key Takeaways

•Addresses the challenge of efficient multi-hop reasoning.
•Proposes a novel technique of early knowledge alignment.
•Potentially improves accuracy and efficiency in knowledge-intensive AI.

Reference

“The research is sourced from ArXiv, indicating a potential for further peer review and validation.”

Permalink ArXiv

Research #Fashion AI 🔬 ResearchAnalyzed: Jan 10, 2026 08:16

IRSN: A Fashion Style Classifier Using Expert Fashion Knowledge

Published:Dec 23, 2025 06:30

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1 min read

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ArXiv

Analysis

This research presents a novel approach to fashion style classification by incorporating domain expertise. The Item Region-based Style Classification Network (IRSN) could significantly improve accuracy by leveraging expert knowledge, making it a promising direction in fashion AI.

Key Takeaways

•The IRSN utilizes domain-specific knowledge to enhance fashion style classification.
•The model is based on the ArXiv publication.
•This research focuses on improvements to the accuracy of fashion classification.

Reference

“The study is based on domain knowledge of fashion experts.”

Permalink ArXiv

Research #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 08:30

Reassessing Knowledge: The Impact of Large Language Models on Epistemology

Published:Dec 22, 2025 16:52

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1 min read

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ArXiv

Analysis

This ArXiv article explores the philosophical implications of Large Language Models (LLMs) on how we understand knowledge and collective intelligence. It likely delves into critical questions about the reliability of information sourced from LLMs and the potential shift in how institutions manage and disseminate knowledge.

Key Takeaways

•LLMs challenge traditional notions of authorship and knowledge creation.
•The article likely raises concerns about the provenance and validation of information.
•The study probably suggests institutional changes needed to manage LLM generated content.

Reference

“The article likely examines the epistemological consequences of LLMs.”

Permalink ArXiv

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