Search: vertex - ai.jp.net

product #rag 📝 BlogAnalyzed: Jan 12, 2026 00:15

Exploring Vector Search and RAG with Vertex AI: A Practical Approach

Published:Jan 12, 2026 00:03

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1 min read

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Qiita AI

Analysis

This article's focus on integrating Retrieval-Augmented Generation (RAG) with Vertex AI Search highlights a crucial aspect of developing enterprise AI solutions. The practical application of vector search for retrieving relevant information from internal manuals is a key use case, demonstrating the potential to improve efficiency and knowledge access within organizations.

Key Takeaways

•The article explores the integration of RAG with Vertex AI Search.
•The use case involves automatically searching internal manuals for answers.
•This solution aims to improve efficiency and knowledge access.

Reference

“…AI assistants should automatically search for relevant manuals and answer questions...”

Permalink Qiita AI

research #llm 📝 BlogAnalyzed: Jan 4, 2026 07:06

LLM Prompt Token Count and Processing Time Impact of Whitespace and Newlines

Published:Jan 4, 2026 05:30

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1 min read

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Zenn Gemini

Analysis

This article addresses a practical concern for LLM application developers: the impact of whitespace and newlines on token usage and processing time. While the premise is sound, the summary lacks specific findings and relies on an external GitHub repository for details, making it difficult to assess the significance of the results without further investigation. The use of Gemini and Vertex AI is mentioned, but the experimental setup and data analysis methods are not described.

Key Takeaways

•Investigates the impact of whitespace and newlines in LLM prompts.
•Uses Gemini and Vertex AI for experimentation.
•Relies on a GitHub repository for experimental details.

Reference

“LLMを使用したアプリケーションを開発している際に、空白文字や改行はどの程度料金や処理時間に影響を与えるのかが気になりました。”

Permalink Zenn Gemini

product #llmops 📝 BlogAnalyzed: Jan 5, 2026 09:12

LLMOps in the Generative AI Era: Model Evaluation

Published:Dec 30, 2025 21:00

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1 min read

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Zenn GenAI

Analysis

This article focuses on model evaluation within the LLMOps framework, specifically using Google Cloud's Vertex AI. It's valuable for practitioners seeking practical guidance on implementing model evaluation pipelines. The article's value hinges on the depth and clarity of the Vertex AI examples provided in the full content, which is not available in the provided snippet.

Key Takeaways

•The article is part of a series on LLMOps.
•It focuses on model evaluation, a key aspect of LLMOps.
•It uses Google Cloud's Vertex AI as a practical example.

Reference

“今回はモデルの評価について、Google Cloud の Vertex AI の機能を例に具体的な例を交えて説明します。”

Permalink Zenn GenAI

Research #Algorithms 🔬 ResearchAnalyzed: Jan 10, 2026 07:07

New Algorithms Advance Global Minimum Vertex-Cut in Directed Graphs

Published:Dec 30, 2025 17:06

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1 min read

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ArXiv

Analysis

This ArXiv article presents advancements in algorithms for the global minimum vertex-cut problem within directed graphs. The research likely explores computational complexity and efficiency improvements for network flow and related graph theory applications.

Key Takeaways

•The research focuses on the global minimum vertex-cut problem.
•The work pertains to directed graphs.
•It likely contributes to improved computational efficiency in graph algorithms.

Reference

“The context is from ArXiv, indicating a research paper.”

Permalink ArXiv

Paper #Graph Algorithms 🔬 ResearchAnalyzed: Jan 3, 2026 18:58

HL-index for Hypergraph Reachability

Published:Dec 29, 2025 10:13

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1 min read

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ArXiv

Analysis

This paper addresses the computationally challenging problem of reachability in hypergraphs, which are crucial for modeling complex relationships beyond pairwise interactions. The introduction of the HL-index and its associated optimization techniques (covering relationship detection, neighbor-index) offers a novel approach to efficiently answer max-reachability queries. The focus on scalability and efficiency, validated by experiments on 20 datasets, makes this research significant for real-world applications.

Key Takeaways

•Addresses the problem of reachability in hypergraphs, which are important for modeling complex relationships.
•Introduces the HL-index, a novel vertex-to-hyperedge index for efficient max-reachability queries.
•Employs optimization techniques like covering relationship detection and a neighbor-index to improve efficiency.
•Demonstrates efficiency and scalability through experiments on 20 datasets.

Reference

“The paper introduces the HL-index, a compact vertex-to-hyperedge index tailored for the max-reachability problem.”

Permalink ArXiv

Research Paper #Statistical Mechanics, Physics 🔬 ResearchAnalyzed: Jan 3, 2026 19:05

Five-Vertex Model and Discrete Log-Gas

Published:Dec 29, 2025 05:59

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1 min read

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ArXiv

Analysis

This paper investigates the five-vertex model, a problem in statistical mechanics, by reformulating it as a discrete log-gas. This approach allows the authors to analyze the model's free energy and resolvent, reproducing existing results and providing new insights. The work is a step towards understanding limit shape phenomena in the model.

Key Takeaways

•Reformulates the five-vertex model as a discrete log-gas.
•Analyzes the free-energy density and resolvent.
•Reproduces previous results and provides new insights.
•Contributes to the understanding of limit shape phenomena.

Reference

“The paper provides the explicit form of the resolvent in all possible regimes.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 21:56

Trying out Gemini's Python SDK

Published:Dec 28, 2025 09:55

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1 min read

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Zenn Gemini

Analysis

This article provides a basic overview of using Google's Gemini API with its Python SDK. It focuses on single-turn interactions and serves as a starting point for developers. The author, @to_fmak, shares their experience developing applications using Gemini. The article was originally written on December 3, 2024, and has been migrated to a new platform. It emphasizes that detailed configurations for multi-turn conversations and output settings should be found in the official documentation. The provided environment details specify Python 3.12.3 and vertexai.

Key Takeaways

•The article introduces the basic usage of Gemini's Python SDK.
•It focuses on single-turn interactions.
•Detailed configurations are available in the official documentation.

Reference

“I'm @to_fmak. I've recently been developing applications using the Gemini API, so I've summarized the basic usage of Gemini's Python SDK as a memo.”

Permalink Zenn Gemini

Research Paper #Graph Theory, Combinatorics 🔬 ResearchAnalyzed: Jan 3, 2026 19:33

Exact Rainbow Numbers for Cycle-Related Graphs in Multi-Hubbed Wheels

Published:Dec 28, 2025 06:55

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1 min read

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ArXiv

Analysis

This paper determines the exact rainbow number for specific graph structures (multi-hubbed wheels and chorded cycles) which is important for applications in areas like wireless communication and network analysis. It solves problems proposed by previous researchers and generalizes existing results, providing a complete solution for rainbow numbers of cycles in large wheel graphs.

Key Takeaways

•Provides exact formulas for rainbow numbers in multi-hubbed wheel graphs.
•Solves open problems from previous research.
•Generalizes existing results on rainbow numbers in wheel graphs.
•The results are applicable to diverse fields like wireless communication and network analysis.

Reference

“The paper determines the exact rainbow number rb(G, H) where G is a multi-hubbed wheel graph W_d(s) and H = θ_{t,ℓ} represents a cycle C_t of length t with 0 ≤ ℓ ≤ t-3 chords emanating from a common vertex.”

Permalink ArXiv

Research Paper #Statistical Physics, Integrable Systems, Stochastic Growth Models 🔬 ResearchAnalyzed: Jan 3, 2026 16:25

Stochastic Six Vertex Model and Discrete Orthogonal Polynomials

Published:Dec 27, 2025 10:13

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1 min read

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ArXiv

Analysis

This paper investigates the behavior of the stochastic six-vertex model, a model in the KPZ universality class, focusing on moderate deviation scales. It uses discrete orthogonal polynomial ensembles (dOPEs) and the Riemann-Hilbert Problem (RHP) approach to derive asymptotic estimates for multiplicative statistics, ultimately providing moderate deviation estimates for the height function in the six-vertex model. The work is significant because it addresses a less-understood aspect of KPZ models (moderate deviations) and provides sharp estimates.

Key Takeaways

•Applies dOPEs and RHP to study the stochastic six-vertex model.
•Focuses on moderate deviation scales, a less-understood aspect of KPZ models.
•Provides sharp estimates for the height function in the six-vertex model.
•Uses a novel parameter-dependent local parametrix in the RHP analysis.

Reference

“The paper derives moderate deviation estimates for the height function in both the upper and lower tail regimes, with sharp exponents and constants.”

Permalink ArXiv

Research Paper #Biophysics, Tissue Mechanics, Computational Biology 🔬 ResearchAnalyzed: Jan 3, 2026 23:59

Necking in Epithelial Tissues: A Simulation Study

Published:Dec 26, 2025 04:25

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1 min read

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ArXiv

Analysis

This paper investigates the mechanical behavior of epithelial tissues, crucial for understanding tissue morphogenesis. It uses a computational approach (vertex simulations and a multiscale model) to explore how cellular topological transitions lead to necking, a localized deformation. The study's significance lies in its potential to explain how tissues deform under stress and how defects influence this process, offering insights into biological processes.

Key Takeaways

•The study uses computational modeling to understand the mechanical behavior of epithelial tissues.
•Cellular topological transitions are identified as a key factor in necking.
•Topological defects in the tissue influence the necking process.
•The model predicts and validates necking behavior observed in real tissues.

Reference

“The study finds that necking bifurcation arises from cellular topological transitions and that topological defects influence the process.”

Permalink ArXiv

Physics #Higgs Physics, Beyond the Standard Model 🔬 ResearchAnalyzed: Jan 4, 2026 00:09

Radiative Charged Higgs Vertices in 3HDMs

Published:Dec 25, 2025 18:41

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1 min read

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ArXiv

Analysis

This paper investigates the radiative corrections to charged Higgs boson interactions in three Higgs doublet models (3HDMs). It focuses on the $H^+ W^- Z$ vertex, calculating it in different 3HDM types and comparing them to 2HDMs. The paper also explores the potential for detecting these interactions at the LHC via vector boson fusion (VBF), suggesting a possible smoking gun signal for 3HDMs.

Key Takeaways

•Calculates radiatively induced $H^+ W^- Z$ vertices in 3HDMs.
•Compares results across different 3HDM types and with 2HDMs.
•Suggests a potential LHC signal for 3HDMs via VBF.
•Highlights a significant enhancement in form factors compared to 2HDMs.

Reference

“The results also indicate a sizeable increment ($\sim 100\%$) over the corresponding form factors in 2HDMs. In addition, we probe the $H_{1,2}^+ W^- Z$ vertices at the 14 TeV LHC using vector boson fusion (VBF).”

Permalink ArXiv

Research #VOA 🔬 ResearchAnalyzed: Jan 10, 2026 07:27

Research Paper Explores Bosonic Vertex Operator Algebras

Published:Dec 25, 2025 03:56

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1 min read

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ArXiv

Analysis

This article summarizes a research paper, likely of interest to mathematicians and theoretical physicists. The work explores the mathematical structures of Vertex Operator Algebras, a topic within conformal field theory.

Key Takeaways

•The paper investigates the properties of Bosonic Vertex Operator Algebras (VOAs).
•It likely explores the relationships between these VOAs and boundary VOAs.
•This research contributes to the understanding of conformal field theory.

Reference

“The paper focuses on generators of a Bosonic VOA and their connections.”

Permalink ArXiv

Research #ML 🔬 ResearchAnalyzed: Jan 10, 2026 08:39

Optimizing Sensor Placement with Time-Vertex Machine Learning for Structural Health Monitoring

Published:Dec 22, 2025 11:59

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1 min read

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ArXiv

Analysis

The article proposes a novel application of Time-Vertex Machine Learning for sensor placement, demonstrating a potential improvement in efficiency for Structural Health Monitoring. This approach could lead to more effective and cost-efficient monitoring systems in various infrastructure applications.

Key Takeaways

•Applies Time-Vertex Machine Learning to sensor placement.
•Focuses on Structural Health Monitoring.
•Aims for more effective and cost-efficient monitoring.

Reference

“The research focuses on optimal sensor placement.”

Permalink ArXiv

Research #Algorithms 🔬 ResearchAnalyzed: Jan 10, 2026 09:26

New Research Explores Capacitated Partition Vertex Cover and Partition Edge Cover

Published:Dec 19, 2025 17:48

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1 min read

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ArXiv

Analysis

The article's source being ArXiv suggests this is preliminary research, which is common in the AI field. Further investigation is needed to assess the novelty, impact, and practical applications of this work, as the specific methodologies and results remain unclear without further details from the paper.

Key Takeaways

•The research focuses on Capacitated Partition Vertex Cover and Partition Edge Cover problems.
•The paper is hosted on ArXiv, implying early-stage research.
•Without the paper details, it's hard to gauge the impact.

Reference

“The article is sourced from ArXiv.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:17

Vertex Model Mechanics Explain the Emergence of Centroidal Voronoi Tiling in Epithelia

Published:Dec 15, 2025 09:15

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1 min read

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ArXiv

Analysis

This article likely discusses a research paper that uses vertex models to understand the formation of Centroidal Voronoi Tiling (CVT) patterns in epithelial tissues. The focus is on the mechanical forces and cellular interactions that lead to this specific geometric arrangement. The source, ArXiv, indicates this is a pre-print or published research paper.

•Google provides cloud-based machine learning resources.
•The article likely focuses on Google's specific offerings in this space.
•This may impact developers and businesses that use AI.

Reference

“Google offers cloud-based machine learning tools.”

Permalink Hacker News

Exploring Vector Search and RAG with Vertex AI: A Practical Approach

Analysis

Key Takeaways

LLM Prompt Token Count and Processing Time Impact of Whitespace and Newlines

Analysis

Key Takeaways

LLMOps in the Generative AI Era: Model Evaluation

Analysis

Key Takeaways

New Algorithms Advance Global Minimum Vertex-Cut in Directed Graphs

Analysis

Key Takeaways

HL-index for Hypergraph Reachability

Analysis

Key Takeaways

Five-Vertex Model and Discrete Log-Gas

Analysis

Key Takeaways

Trying out Gemini's Python SDK

Analysis

Key Takeaways

Exact Rainbow Numbers for Cycle-Related Graphs in Multi-Hubbed Wheels

Analysis

Key Takeaways

Stochastic Six Vertex Model and Discrete Orthogonal Polynomials

Analysis

Key Takeaways

Necking in Epithelial Tissues: A Simulation Study

Analysis

Key Takeaways

Radiative Charged Higgs Vertices in 3HDMs

Analysis

Key Takeaways

Research Paper Explores Bosonic Vertex Operator Algebras

Analysis

Key Takeaways

Optimizing Sensor Placement with Time-Vertex Machine Learning for Structural Health Monitoring

Analysis

Key Takeaways

New Research Explores Capacitated Partition Vertex Cover and Partition Edge Cover

Analysis

Key Takeaways

Vertex Model Mechanics Explain the Emergence of Centroidal Voronoi Tiling in Epithelia

Analysis

Key Takeaways

Research Advances on Feedback Vertex Sets in Digraphs

Analysis

Key Takeaways

Gemini 2.0 Flash-Lite Now Generally Available

Analysis

Key Takeaways

Weaviate on Vertex AI RAG Engine: Building RAG Applications on Google Cloud

Analysis

Key Takeaways

Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI

Analysis

Key Takeaways

Making thousands of open LLMs bloom in the Vertex AI Model Garden

Analysis

Key Takeaways

Generative AI support on Vertex AI is now generally available

Analysis

Key Takeaways

Deploying 🤗 ViT on Vertex AI

Analysis

Key Takeaways

(5/5) AlphaVertex - Creating a Worldwide Financial Knowledge Graph - TWiML Talk #18

Analysis

Key Takeaways

Google's Cloud ML Tools: Expanding Access and Capabilities

Analysis

Key Takeaways

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