Search: retrieval-augmented - ai.jp.net

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 #agent 📝 BlogAnalyzed: Jan 17, 2026 22:00

Supercharge Your AI: Build Self-Evaluating Agents with LlamaIndex and OpenAI!

Published:Jan 17, 2026 21:56

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1 min read

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MarkTechPost

Analysis

This tutorial is a game-changer! It unveils how to create powerful AI agents that not only process information but also critically evaluate their own performance. The integration of retrieval-augmented generation, tool use, and automated quality checks promises a new level of AI reliability and sophistication.

Key Takeaways

•Learn to build AI agents that can reason over retrieved evidence.
•Discover how to integrate tools deliberately within an AI workflow.
•Explore the creation of self-evaluating AI systems for enhanced output quality.

Reference

“By structuring the system around retrieval, answer synthesis, and self-evaluation, we demonstrate how agentic patterns […]”

Permalink MarkTechPost

research #rag 📝 BlogAnalyzed: Jan 16, 2026 01:15

Supercharge Your AI: Learn How Retrieval-Augmented Generation (RAG) Makes LLMs Smarter!

Published:Jan 15, 2026 23:37

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1 min read

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Zenn GenAI

Analysis

This article dives into the exciting world of Retrieval-Augmented Generation (RAG), a game-changing technique for boosting the capabilities of Large Language Models (LLMs)! By connecting LLMs to external knowledge sources, RAG overcomes limitations and unlocks a new level of accuracy and relevance. It's a fantastic step towards truly useful and reliable AI assistants.

Key Takeaways

•RAG helps LLMs overcome limitations like lack of access to specific documents.
•It allows LLMs to incorporate up-to-date information, beyond their initial training data.
•RAG is a key technology for reducing the 'hallucination' problem in AI, leading to more reliable outputs.

Reference

“RAG is a mechanism that 'searches external knowledge (documents) and passes that information to the LLM to generate answers.'”

Permalink Zenn GenAI

research #llm 🏛️ OfficialAnalyzed: Jan 16, 2026 01:15

Demystifying RAG: A Hands-On Guide with Practical Code

Published:Jan 15, 2026 10:17

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1 min read

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Zenn OpenAI

Analysis

This article offers a fantastic opportunity to dive into the world of RAG (Retrieval-Augmented Generation) with a practical, code-driven approach. By implementing a simple RAG system on Google Colab, readers gain hands-on experience and a deeper understanding of how these powerful LLM-powered applications work.

Key Takeaways

•The article provides a step-by-step guide to building a RAG system.
•It uses Google Colab, making the implementation accessible and easy to follow.
•Readers will gain a concrete understanding of how LLMs use external documents to generate responses.

Reference

“This article explains the basic mechanisms of RAG using sample code.”

Permalink Zenn OpenAI

research #agent 📝 BlogAnalyzed: Jan 15, 2026 08:30

Agentic RAG: Navigating Complex Queries with Autonomous AI

Published:Jan 15, 2026 04:48

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1 min read

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Zenn AI

Analysis

The article's focus on Agentic RAG using LangGraph offers a practical glimpse into building more sophisticated Retrieval-Augmented Generation (RAG) systems. However, the analysis would benefit from detailing the specific advantages of an agentic approach over traditional RAG, such as improved handling of multi-step queries or reasoning capabilities, to showcase its core value proposition. The brief code snippet provides a starting point, but a more in-depth discussion of agent design and optimization would increase the piece's utility.

Key Takeaways

•Agentic RAG aims to improve information retrieval using autonomous AI agents.
•The article showcases an implementation example using LangGraph.
•The article is a summary of a longer, more in-depth blog post.

Reference

“The article is a summary and technical extract from a blog post at https://agenticai-flow.com/posts/agentic-rag-advanced-retrieval/”

Permalink Zenn AI

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

AI Research & Development #Search Systems, RAG Systems, AI Roadmap 📝 BlogAnalyzed: Jan 16, 2026 01:52

A practical 2026 roadmap for modern AI search & RAG systems

Published:Jan 16, 2026 01:52

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1 min read

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Analysis

The article's title suggests a focus on practical applications and future development of AI search and RAG (Retrieval-Augmented Generation) systems. The timeframe, 2026, implies a forward-looking perspective, likely covering advancements in the field. The source, r/mlops, indicates a community of Machine Learning Operations professionals, suggesting the content will likely be technically oriented and focused on practical deployment and management aspects of these systems. Without the article content, further detailed critique is impossible.

Key Takeaways

Reference

“”

Permalink

AI Technology #RAG (Retrieval-Augmented Generation)📝 BlogAnalyzed: Jan 16, 2026 01:53

RAG Connecting Generative AI and Internal Data: Interest Exists, but Widespread Adoption Lags

Published:Jan 16, 2026 01:53

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1 min read

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Analysis

The article highlights the gap between interest and actual implementation of Retrieval-Augmented Generation (RAG) systems for connecting generative AI with internal data. It implicitly suggests challenges hindering broader adoption.

Key Takeaways

Reference

“”

Permalink

product #rag 📝 BlogAnalyzed: Jan 10, 2026 05:41

Building a Transformer Paper Q&A System with RAG and Mastra

Published:Jan 8, 2026 08:28

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1 min read

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Zenn LLM

Analysis

This article presents a practical guide to implementing Retrieval-Augmented Generation (RAG) using the Mastra framework. By focusing on the Transformer paper, the article provides a tangible example of how RAG can be used to enhance LLM capabilities with external knowledge. The availability of the code repository further strengthens its value for practitioners.

Key Takeaways

•Article demonstrates RAG implementation with Mastra framework.
•Focuses on the Transformer "Attention Is All You Need" paper.
•Provides a GitHub repository with sample code.

Reference

“RAG（Retrieval-Augmented Generation）は、大規模言語モデルに外部知識を与えて回答精度を高める技術です。”

Permalink Zenn LLM

Research #llm 📝 BlogAnalyzed: Jan 3, 2026 05:25

The Case Against RAG: Why I Switched from ChatGPT's RAG to Gemini Pro's 'Brute-Force Long Context'

Published:Jan 3, 2026 02:00

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1 min read

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Zenn AI

Analysis

This article discusses the author's frustration with implementing Retrieval-Augmented Generation (RAG) with ChatGPT and their subsequent switch to using Gemini Pro's long context window capabilities. The author highlights the complexities and challenges associated with RAG, such as data preprocessing, chunking, vector database management, and query tuning. They suggest that Gemini Pro's ability to handle longer contexts directly eliminates the need for these complex RAG processes in certain use cases.

Key Takeaways

•RAG implementation can be complex and time-consuming.
•Gemini Pro's long context window offers an alternative to RAG in some cases.
•Data preprocessing and vector database management are significant challenges in RAG.
•The choice between RAG and long context models depends on the specific use case and requirements.

Reference

“"I was tired of the RAG implementation with ChatGPT, so I completely switched to Gemini Pro's 'brute-force long context'."”

Permalink Zenn AI

Tutorial #RAG 📝 BlogAnalyzed: Jan 3, 2026 02:06

What is RAG? Let's try to understand the whole picture easily

Published:Jan 2, 2026 15:00

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1 min read

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Zenn AI

Analysis

This article introduces RAG (Retrieval-Augmented Generation) as a solution to limitations of LLMs like ChatGPT, such as inability to answer questions based on internal documents, providing incorrect answers, and lacking up-to-date information. It aims to explain the inner workings of RAG in three steps without delving into implementation details or mathematical formulas, targeting readers who want to understand the concept and be able to explain it to others.

Key Takeaways

•RAG addresses the limitations of LLMs in accessing and utilizing external or private data.
•The article focuses on conceptual understanding rather than technical implementation.
•The goal is to enable readers to explain RAG to others.

Reference

“"RAG (Retrieval-Augmented Generation) is a representative mechanism for solving these problems."”

Permalink Zenn AI

Research Paper #Retrieval-Augmented Generation (RAG)🔬 ResearchAnalyzed: Jan 3, 2026 06:12

AdaGReS: Redundancy-Aware Context Selection for RAG

Published:Dec 31, 2025 18:48

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1 min read

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ArXiv

Analysis

This paper addresses a critical issue in Retrieval-Augmented Generation (RAG): the inefficiency of standard top-k retrieval, which often includes redundant information. AdaGReS offers a novel solution by introducing a redundancy-aware context selection framework. This framework optimizes a set-level objective that balances relevance and redundancy, employing a greedy selection strategy under a token budget. The key innovation is the instance-adaptive calibration of the relevance-redundancy trade-off parameter, eliminating manual tuning. The paper's theoretical analysis provides guarantees for near-optimality, and experimental results demonstrate improved answer quality and robustness. This work is significant because it directly tackles the problem of token budget waste and improves the performance of RAG systems.

Key Takeaways

•Addresses the problem of redundant context in RAG.
•Proposes AdaGReS, a redundancy-aware context selection framework.
•Employs a greedy selection strategy with a token budget.
•Features instance-adaptive calibration to eliminate manual tuning.
•Demonstrates improved answer quality and robustness in experiments.

Reference

“AdaGReS introduces a closed-form, instance-adaptive calibration of the relevance-redundancy trade-off parameter to eliminate manual tuning and adapt to candidate-pool statistics and budget limits.”

Permalink ArXiv

Research Paper #AI Privacy, LLMs, RAG 🔬 ResearchAnalyzed: Jan 3, 2026 06:24

PrivacyBench: Evaluating Privacy Risks in Personalized AI

Published:Dec 31, 2025 13:16

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1 min read

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ArXiv

Analysis

This paper introduces PrivacyBench, a benchmark to assess the privacy risks associated with personalized AI agents that access sensitive user data. The research highlights the potential for these agents to inadvertently leak user secrets, particularly in Retrieval-Augmented Generation (RAG) systems. The findings emphasize the limitations of current mitigation strategies and advocate for privacy-by-design safeguards to ensure ethical and inclusive AI deployment.

Key Takeaways

•Personalized AI agents pose privacy risks due to access to sensitive user data.
•PrivacyBench is a benchmark for evaluating secret preservation in conversational AI.
•RAG systems are vulnerable to secret leakage.
•Current mitigation strategies are insufficient.
•Privacy-by-design safeguards are crucial for ethical AI deployment.

Reference

“RAG assistants leak secrets in up to 26.56% of interactions.”

Permalink ArXiv

Paper #LLM 🔬 ResearchAnalyzed: Jan 3, 2026 08:48

R-Debater: Retrieval-Augmented Debate Generation

Published:Dec 31, 2025 07:33

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1 min read

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ArXiv

Analysis

This paper introduces R-Debater, a novel agentic framework for generating multi-turn debates. It's significant because it moves beyond simple LLM-based debate generation by incorporating an 'argumentative memory' and retrieval mechanisms. This allows the system to ground its arguments in evidence and prior debate moves, leading to more coherent, consistent, and evidence-supported debates. The evaluation on standardized debates and comparison with strong LLM baselines, along with human evaluation, further validates the effectiveness of the approach. The focus on stance consistency and evidence use is a key advancement in the field.

Key Takeaways

•R-Debater is an agentic framework for generating multi-turn debates.
•It uses an 'argumentative memory' to retrieve evidence and prior debate moves.
•The system is evaluated on ORCHID debates and compared with LLM baselines.
•R-Debater achieves higher scores and demonstrates improved consistency and evidence use compared to baselines.

Reference

“R-Debater achieves higher single-turn and multi-turn scores compared with strong LLM baselines, and human evaluation confirms its consistency and evidence use.”

Permalink ArXiv

Paper #LLM Security 🔬 ResearchAnalyzed: Jan 3, 2026 15:42

Defenses for RAG Against Corpus Poisoning

Published:Dec 30, 2025 14:43

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1 min read

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ArXiv

Analysis

This paper addresses a critical vulnerability in Retrieval-Augmented Generation (RAG) systems: corpus poisoning. It proposes two novel, computationally efficient defenses, RAGPart and RAGMask, that operate at the retrieval stage. The work's significance lies in its practical approach to improving the robustness of RAG pipelines against adversarial attacks, which is crucial for real-world applications. The paper's focus on retrieval-stage defenses is particularly valuable as it avoids modifying the generation model, making it easier to integrate and deploy.

Key Takeaways

•Proposes two retrieval-stage defenses (RAGPart and RAGMask) against corpus poisoning in RAG.
•Defenses are computationally lightweight and do not require modification of the generation model.
•Demonstrates effectiveness in reducing attack success rates across various benchmarks and poisoning strategies.
•Introduces an interpretable attack to stress-test the defenses.

Reference

“The paper states that RAGPart and RAGMask consistently reduce attack success rates while preserving utility under benign conditions.”

Permalink ArXiv

Technology #Artificial Intelligence 👥 CommunityAnalyzed: Jan 3, 2026 06:58

The Power of RAG: Why It's Essential for Modern AI Applications

Published:Dec 30, 2025 13:08

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1 min read

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r/LanguageTechnology

Analysis

This article provides a concise overview of Retrieval-Augmented Generation (RAG) and its importance in modern AI applications. It highlights the benefits of RAG, including enhanced context understanding, content accuracy, and the ability to provide up-to-date information. The article also offers practical use cases and best practices for integrating RAG. The language is clear and accessible, making it suitable for a general audience interested in AI.

Key Takeaways

•RAG improves AI by providing more contextually relevant and up-to-date information.
•RAG is useful in chatbots, content generation, and data insights.
•Successful RAG implementation requires careful assessment, pilot projects, and high-quality data.

Reference

“RAG enhances the way AI systems process and generate information. By pulling from external data, it offers more contextually relevant outputs.”

Permalink r/LanguageTechnology

Research Paper #Personalized Search, LLM Agents, Information Retrieval 🔬 ResearchAnalyzed: Jan 3, 2026 15:56

SPARK: Agent-Driven Personalized Search

Published:Dec 30, 2025 06:09

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1 min read

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ArXiv

Analysis

This paper introduces SPARK, a novel framework for personalized search using coordinated LLM agents. It addresses the limitations of static profiles and monolithic retrieval pipelines by employing specialized agents that handle task-specific retrieval and emergent personalization. The framework's focus on agent coordination, knowledge sharing, and continuous learning offers a promising approach to capturing the complexity of human information-seeking behavior. The use of cognitive architectures and multi-agent coordination theory provides a strong theoretical foundation.

Key Takeaways

•SPARK utilizes coordinated LLM agents for personalized search.
•The framework employs a persona space and a Persona Coordinator for dynamic query interpretation.
•Agents use retrieval-augmented generation, memory stores, and reasoning modules.
•Inter-agent collaboration is facilitated through structured communication.
•SPARK aims to capture the complexity of human information-seeking behavior.

Reference

“SPARK formalizes a persona space defined by role, expertise, task context, and domain, and introduces a Persona Coordinator that dynamically interprets incoming queries to activate the most relevant specialized agents.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 15:57

Efficient Long-Context Attention

Published:Dec 30, 2025 03:39

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1 min read

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ArXiv

Analysis

This paper introduces LongCat ZigZag Attention (LoZA), a sparse attention mechanism designed to improve the efficiency of long-context models. The key contribution is the ability to transform existing full-attention models into sparse versions, leading to speed-ups in both prefill and decode phases, particularly relevant for retrieval-augmented generation and tool-integrated reasoning. The claim of processing up to 1 million tokens is significant.

Key Takeaways

•Introduces LongCat ZigZag Attention (LoZA) for sparse attention.
•Enables speed-ups in long-context scenarios.
•Applicable to prefill and decode phases.
•Claims processing up to 1 million tokens.

Reference

“LoZA can achieve significant speed-ups both for prefill-intensive (e.g., retrieval-augmented generation) and decode-intensive (e.g., tool-integrated reasoning) cases.”

Permalink ArXiv

Research Paper #Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Hypergraphs 🔬 ResearchAnalyzed: Jan 3, 2026 16:54

Hypergraph Memory for Multi-step RAG

Published:Dec 30, 2025 03:13

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1 min read

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ArXiv

Analysis

This paper addresses the limitations of existing memory mechanisms in multi-step retrieval-augmented generation (RAG) systems. It proposes a hypergraph-based memory (HGMem) to capture high-order correlations between facts, leading to improved reasoning and global understanding in long-context tasks. The core idea is to move beyond passive storage to a dynamic structure that facilitates complex reasoning and knowledge evolution.

Key Takeaways

•Proposes HGMem, a hypergraph-based memory mechanism for multi-step RAG.
•HGMem captures high-order correlations between facts.
•Improves reasoning and global understanding in long-context tasks.
•Outperforms strong baseline systems on challenging datasets.

Reference

“HGMem extends the concept of memory beyond simple storage into a dynamic, expressive structure for complex reasoning and global understanding.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 16:58

LLMs and Retrieval: Knowing When to Say 'I Don't Know'

Published:Dec 29, 2025 19:59

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1 min read

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ArXiv

Analysis

This paper addresses a critical issue in retrieval-augmented generation: the tendency of LLMs to provide incorrect answers when faced with insufficient information, rather than admitting ignorance. The adaptive prompting strategy offers a promising approach to mitigate this, balancing the benefits of expanded context with the drawbacks of irrelevant information. The focus on improving LLMs' ability to decline requests is a valuable contribution to the field.

Key Takeaways

•LLMs struggle with admitting ignorance in retrieval-augmented question answering.
•Adaptive prompting, splitting retrieved information into chunks, can improve performance.
•Enhancing LLMs' ability to decline requests is crucial for accuracy.

Reference

“The LLM often generates incorrect answers instead of declining to respond, which constitutes a major source of error.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 29, 2025 09:30

Latest 2025 Edition: How to Build Your Own AI with Gemini's Free Tier

Published:Dec 29, 2025 09:04

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1 min read

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Qiita AI

Analysis

This article, likely a tutorial, focuses on leveraging Gemini's free tier to create a personalized AI using Retrieval-Augmented Generation (RAG). RAG allows users to augment the AI's knowledge base with their own data, enabling it to provide more relevant and customized responses. The article likely walks through the process of adding custom information to Gemini, effectively allowing it to "consult" user-provided resources when generating text. This approach is valuable for creating AI assistants tailored to specific domains or tasks, offering a practical application of RAG techniques for individual users. The "2025" in the title suggests forward-looking relevance, possibly incorporating future updates or features of the Gemini platform.

Key Takeaways

•Gemini's free tier can be used to create custom AI solutions.
•RAG allows for augmenting AI knowledge with user-specific data.
•Personalized AI can be tailored to specific tasks and domains.

Reference

“AI that answers while looking at your own reference books, instead of only talking from its own memory.”

Permalink Qiita AI

Research Paper #Adversarial Robustness, Neural Ranking, Information Retrieval 🔬 ResearchAnalyzed: Jan 3, 2026 16:08

RobustMask: Certified Robustness for Neural Ranking

Published:Dec 29, 2025 08:51

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1 min read

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ArXiv

Analysis

This paper addresses the critical vulnerability of neural ranking models to adversarial attacks, a significant concern for applications like Retrieval-Augmented Generation (RAG). The proposed RobustMask defense offers a novel approach combining pre-trained language models with randomized masking to achieve certified robustness. The paper's contribution lies in providing a theoretical proof of certified top-K robustness and demonstrating its effectiveness through experiments, offering a practical solution to enhance the security of real-world retrieval systems.

Key Takeaways

•Proposes RobustMask, a novel defense against adversarial attacks on neural ranking models.
•Combines pre-trained language models with randomized masking for robustness.
•Provides a theoretical proof of certified top-K robustness.
•Demonstrates effectiveness in certifying a significant portion of ranked documents against perturbations.

Reference

“RobustMask successfully certifies over 20% of candidate documents within the top-10 ranking positions against adversarial perturbations affecting up to 30% of their content.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 29, 2025 01:43

RAG: Accuracy Didn't Improve When Converting PDFs to Markdown with Gemini 3 Flash

Published:Dec 29, 2025 01:00

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1 min read

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Qiita LLM

Analysis

The article discusses an experiment using Gemini 3 Flash for Retrieval-Augmented Generation (RAG). The author attempted to improve accuracy by converting PDF documents to Markdown format before processing them with Gemini 3 Flash. The core finding is that this conversion did not lead to the expected improvement in accuracy. The article's brevity suggests it's a quick report on a failed experiment, likely aimed at sharing preliminary findings and saving others time. The mention of pdfplumber and tesseract indicates the use of specific tools for PDF processing and OCR, respectively. The focus is on the practical application of LLMs and the challenges of improving their performance in real-world scenarios.

Key Takeaways

•Experiment tested the impact of PDF to Markdown conversion on RAG accuracy using Gemini 3 Flash.
•The conversion process did not improve the accuracy of the RAG system.
•The article highlights a practical experiment in LLM application and its limitations.

Reference

“The article mentions the use of pdfplumber, tesseract, and Gemini 3 Flash for PDF processing and Markdown conversion.”

Permalink Qiita LLM

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 16:31

Seeking Collaboration on Financial Analysis RAG Bot Project

Published:Dec 28, 2025 16:26

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1 min read

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r/deeplearning

Analysis

This post highlights a common challenge in AI development: the need for collaboration and shared knowledge. The user is working on a Retrieval-Augmented Generation (RAG) bot for financial analysis, allowing users to upload reports and ask questions. They are facing difficulties and seeking assistance from the deep learning community. This demonstrates the practical application of AI in finance and the importance of open-source resources and collaborative problem-solving. The request for help suggests that while individual effort is valuable, complex AI projects often benefit from diverse perspectives and shared expertise. The post also implicitly acknowledges the difficulty of implementing RAG systems effectively, even with readily available tools and libraries.

Key Takeaways

•RAG bots are being applied to financial analysis.
•Collaboration is crucial for overcoming challenges in AI projects.
•Open-source resources and community support are valuable for AI development.

Reference

“"I am working on a financial analysis rag bot it is like user can upload a financial report and on that they can ask any question regarding to that . I am facing issues so if anyone has worked on same problem or has came across a repo like this kindly DM pls help we can make this project together"”

Permalink r/deeplearning

Research #llm 🏛️ OfficialAnalyzed: Dec 28, 2025 21:58

Testing Context Relevance of RAGAS (Nvidia Metrics)

Published:Dec 28, 2025 15:22

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1 min read

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Qiita OpenAI

Analysis

This article discusses the use of RAGAS, a metric developed by Nvidia, to evaluate the context relevance of search results in a retrieval-augmented generation (RAG) system. The author aims to automatically assess whether search results provide sufficient evidence to answer a given question using a large language model (LLM). The article highlights the potential of RAGAS for improving search systems by automating the evaluation process, which would otherwise require manual prompting and evaluation. The focus is on the 'context relevance' aspect of RAGAS, suggesting an exploration of how well the retrieved context supports the generated answers.

Key Takeaways

•The article explores using RAGAS for automated evaluation of search results in RAG systems.
•The focus is on the 'context relevance' metric within RAGAS.
•The goal is to improve search systems by assessing the quality of retrieved context.

Reference

“The author wants to automatically evaluate whether search results provide the basis for answering questions using an LLM.”

Permalink Qiita OpenAI

Research Paper #Code Optimization, LLMs, Python 🔬 ResearchAnalyzed: Jan 3, 2026 19:32

FasterPy: LLM-Based Python Code Optimization

Published:Dec 28, 2025 07:43

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1 min read

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ArXiv

Analysis

This paper introduces FasterPy, a framework leveraging Large Language Models (LLMs) to optimize Python code execution efficiency. It addresses the limitations of traditional rule-based and existing machine learning approaches by utilizing Retrieval-Augmented Generation (RAG) and Low-Rank Adaptation (LoRA) to improve code performance. The use of LLMs for code optimization is a significant trend, and this work contributes a practical framework with demonstrated performance improvements on a benchmark dataset.

Key Takeaways

•FasterPy is a framework for optimizing Python code execution efficiency using LLMs.
•It utilizes Retrieval-Augmented Generation (RAG) and Low-Rank Adaptation (LoRA).
•The framework is evaluated on the Performance Improving Code Edits (PIE) benchmark.
•The authors provide a publicly available tool and experimental results.

Reference

“FasterPy combines Retrieval-Augmented Generation (RAG), supported by a knowledge base constructed from existing performance-improving code pairs and corresponding performance measurements, with Low-Rank Adaptation (LoRA) to enhance code optimization performance.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 27, 2025 23:00

Help Needed with RAG Systems

Published:Dec 27, 2025 22:53

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1 min read

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r/learnmachinelearning

Analysis

This is a very short post on Reddit's r/learnmachinelearning forum where the author is asking for resources to learn about creating Retrieval-Augmented Generation (RAG) systems. The post lacks specific details about the author's current knowledge level or the specific challenges they are facing, making it difficult to provide targeted recommendations. However, the request is clear and concise, indicating a genuine interest in learning about RAG systems. The lack of context makes it a general request for introductory material on the topic. The post's simplicity suggests the author is likely a beginner in the field.

Key Takeaways

•RAG systems are becoming increasingly important in NLP.
•Many online resources are available for learning about RAG.
•Community forums like Reddit can be valuable for finding learning materials.

Reference

“I need help learning how to create a RAG system, do you guys have any recommendations on which material to learn from, it would really help me figuring out stuff.”

Permalink r/learnmachinelearning

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 16:23

DICE: A New Framework for Evaluating Retrieval-Augmented Generation Systems

Published:Dec 27, 2025 16:02

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1 min read

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ArXiv

Analysis

This paper introduces DICE, a novel framework for evaluating Retrieval-Augmented Generation (RAG) systems. It addresses the limitations of existing evaluation metrics by providing explainable, robust, and efficient assessment. The framework uses a two-stage approach with probabilistic scoring and a Swiss-system tournament to improve interpretability, uncertainty quantification, and computational efficiency. The paper's significance lies in its potential to enhance the trustworthiness and responsible deployment of RAG technologies by enabling more transparent and actionable system improvement.

Key Takeaways

•DICE is a two-stage framework for RAG evaluation.
•It uses probabilistic scoring (A, B, Tie) for transparent judgments.
•Employs a Swiss-system tournament for computational efficiency.
•Achieves high agreement with human experts.
•Aims to improve trustworthiness and responsible deployment of RAG systems.

Reference

“DICE achieves 85.7% agreement with human experts, substantially outperforming existing LLM-based metrics such as RAGAS.”

Permalink ArXiv

Research Paper #Artificial Intelligence, Neuroscience, LLMs 🔬 ResearchAnalyzed: Jan 3, 2026 16:25

Neuroscience-Inspired AI: Integrating Actions, Structure, and Memory

Published:Dec 27, 2025 11:54

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1 min read

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ArXiv

Analysis

This paper argues for incorporating principles from neuroscience, specifically action integration, compositional structure, and episodic memory, into foundation models to address limitations like hallucinations, lack of agency, interpretability issues, and energy inefficiency. It suggests a shift from solely relying on next-token prediction to a more human-like AI approach.

Key Takeaways

•Foundation models currently lack key components found in advanced predictive coding models of the brain.
•Integrating actions, compositional structure, and episodic memory could improve safety, interpretability, and efficiency.
•The paper suggests augmenting current trends like Chain-of-Thought and Retrieval-Augmented Generation with brain-inspired components.
•A renewed exchange between brain science and AI is crucial for human-centered AI development.

Reference

“The paper proposes that to achieve safe, interpretable, energy-efficient, and human-like AI, foundation models should integrate actions, at multiple scales of abstraction, with a compositional generative architecture and episodic memory.”

Permalink ArXiv

Paper #RAG, LLM, Information Retrieval 🔬 ResearchAnalyzed: Jan 3, 2026 20:02

HiFi-RAG: Improved RAG for Open-Domain QA

Published:Dec 27, 2025 02:37

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1 min read

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ArXiv

Analysis

This paper presents HiFi-RAG, a novel Retrieval-Augmented Generation (RAG) system that won the MMU-RAGent NeurIPS 2025 competition. The core innovation lies in a hierarchical filtering approach and a two-pass generation strategy leveraging different Gemini 2.5 models for efficiency and performance. The paper highlights significant improvements over baselines, particularly on a custom dataset focusing on post-cutoff knowledge, demonstrating the system's ability to handle recent information.

Key Takeaways

•HiFi-RAG is a novel RAG system employing hierarchical filtering and two-pass generation.
•It leverages Gemini 2.5 Flash for efficiency and Gemini 2.5 Pro for reasoning.
•The system achieves significant performance gains, especially on post-cutoff knowledge tasks.
•The approach demonstrates the effectiveness of multi-stage pipelines in RAG.

Reference

“HiFi-RAG outperforms the parametric baseline by 57.4% in ROUGE-L and 14.9% in DeBERTaScore on Test2025.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 27, 2025 01:31

Chroma Introduction (Part 1): Registering Text to VectorStore

Published:Dec 26, 2025 23:21

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1 min read

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Qiita LLM

Analysis

This article introduces Chroma, a free VectorStore usable with Python, and focuses on the initial step of registering text. It's a practical guide for those building RAG systems, highlighting the importance of VectorStores in vectorizing and storing text. The article's focus on a specific tool and a fundamental task makes it immediately useful for developers. However, the title suggests it's part one, implying further articles will be needed for a complete understanding of Chroma and its capabilities. The article's value lies in its hands-on approach to a crucial aspect of RAG implementation.

Key Takeaways

•Chroma is a free VectorStore usable with Python.
•VectorStores are crucial for RAG systems.
•The article focuses on registering text to VectorStore.

Reference

“When building a RAG (Retrieval-Augmented Generation) system, VectorStore, which vectorizes and stores text, plays an important role.”

Permalink Qiita LLM

Research #llm 🔬 ResearchAnalyzed: Dec 27, 2025 04:01

MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation

Published:Dec 26, 2025 05:00

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1 min read

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ArXiv AI

Analysis

This paper introduces MegaRAG, a novel approach to retrieval-augmented generation that leverages multimodal knowledge graphs to enhance the reasoning capabilities of large language models. The key innovation lies in incorporating visual cues into the knowledge graph construction, retrieval, and answer generation processes. This allows the model to perform cross-modal reasoning, leading to improved content understanding, especially for long-form, domain-specific content. The experimental results demonstrate that MegaRAG outperforms existing RAG-based approaches on both textual and multimodal corpora, suggesting a significant advancement in the field. The approach addresses the limitations of traditional RAG methods in handling complex, multimodal information.

Key Takeaways

•Introduces MegaRAG, a multimodal knowledge graph-based RAG approach.
•Incorporates visual cues for enhanced reasoning and content understanding.
•Demonstrates improved performance on both textual and multimodal corpora.

Reference

“Our method incorporates visual cues into the construction of knowledge graphs, the retrieval phase, and the answer generation process.”

Permalink ArXiv AI

Research Paper #AI Security, Code Generation, Backdoor Attacks 🔬 ResearchAnalyzed: Jan 4, 2026 00:17

Retriever Backdoors Pose a Practical Threat to Code Generation

Published:Dec 25, 2025 13:53

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1 min read

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ArXiv

Analysis

This paper highlights a critical and previously underexplored security vulnerability in Retrieval-Augmented Code Generation (RACG) systems. It introduces a novel and stealthy backdoor attack targeting the retriever component, demonstrating that existing defenses are insufficient. The research reveals a significant risk of generating vulnerable code, emphasizing the need for robust security measures in software development.

Key Takeaways

•Retriever backdoors are a practical and stealthy threat to RACG systems.
•Existing defenses are ineffective against the proposed attack.
•A small amount of poisoned code can lead to significant vulnerability in generated code.
•The research highlights the urgent need for improved security measures in code generation.

Reference

“By injecting vulnerable code equivalent to only 0.05% of the entire knowledge base size, an attacker can successfully manipulate the backdoored retriever to rank the vulnerable code in its top-5 results in 51.29% of cases.”

Permalink ArXiv

Research #llm 🏛️ OfficialAnalyzed: Dec 25, 2025 17:58

Framework Created for Easy RAG Performance Evaluation Using the Digital Agency's Public QA Dataset lawqa_jp

Published:Dec 25, 2025 08:53

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1 min read

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Zenn OpenAI

Analysis

This article discusses the creation of a framework for easily evaluating Retrieval-Augmented Generation (RAG) performance using the Japanese Digital Agency's publicly available QA dataset, lawqa_jp. The dataset consists of multiple-choice questions related to Japanese laws and regulations. The author highlights the limited availability of suitable Japanese datasets for RAG and positions lawqa_jp as a valuable resource. The framework aims to simplify the process of assessing RAG models on this dataset, potentially accelerating research and development in the field of legal information retrieval and question answering in Japanese. The article is relevant for data scientists and researchers working on RAG systems and natural language processing in the Japanese language.

Key Takeaways

•lawqa_jp is a valuable resource for evaluating RAG performance in Japanese legal domain.
•The framework simplifies the evaluation process of RAG models on lawqa_jp.
•The dataset consists of multiple-choice questions based on Japanese laws and regulations.

Reference

“本データセットは、総務省のポータルサイト e-Gov などで公開されている法令文書などを参照した質問・回答ペアをまとめたデータセットであり、全ての質問が a ~ d の4択式の問題で構成されています。”

Permalink Zenn OpenAI

Research #llm 🏛️ OfficialAnalyzed: Dec 27, 2025 00:01

A Framework for Easily Evaluating RAG Performance with the Digital Agency's Public QA Dataset lawqa_jp

Published:Dec 25, 2025 08:53

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1 min read

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Zenn OpenAI

Analysis

This article introduces a framework for evaluating Retrieval-Augmented Generation (RAG) performance using the lawqa_jp dataset released by Japan's Digital Agency. The dataset consists of multiple-choice questions related to Japanese laws, making it a valuable resource for training and evaluating RAG models in the legal domain. The article highlights the limited availability of Japanese datasets suitable for RAG and positions lawqa_jp as a significant contribution. The framework aims to simplify the evaluation process, potentially encouraging wider adoption and improvement of RAG models for legal applications. It's a practical approach to leveraging a newly available resource for advancing NLP in a specific domain.

Key Takeaways

•lawqa_jp dataset from the Digital Agency is a valuable resource for RAG in the legal domain.
•The framework simplifies the evaluation of RAG models using this dataset.
•Limited availability of Japanese datasets for RAG makes this contribution significant.

Reference

Permalink Zenn OpenAI

Research #llm 📝 BlogAnalyzed: Dec 25, 2025 06:19

[Preliminary Round 1st Place Method Revealed] AWS AI League 2025: The Strategy of "Quality over Quantity" and the Realization of the Necessity of "RAG" in the Finals

Published:Dec 25, 2025 05:51

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1 min read

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Qiita LLM

Analysis

This article discusses the winning strategy employed in the preliminary round of the AWS AI League 2025, emphasizing a "quality over quantity" approach. It highlights the participant's experience in the DNP competition, a private event organized by AWS. The article further delves into the realization of the critical need for Retrieval-Augmented Generation (RAG) techniques, particularly during the final stages of the competition. The piece likely provides insights into the specific methods and challenges faced, offering valuable lessons for future participants and those interested in applying AI in competitive settings. It underscores the importance of strategic data selection and the limitations of relying solely on large datasets without effective retrieval mechanisms.

Key Takeaways

•Quality over quantity is crucial in AI competitions.
•RAG is essential for achieving high performance in complex AI tasks.
•Understanding the specific challenges of the competition is key to developing an effective strategy.

Reference

“"量より質"の戦略と、決勝で痛感した"RAG"の必要性”

Permalink Qiita LLM

Artificial Intelligence #Retrieval-Augmented Generation 📝 BlogAnalyzed: Dec 24, 2025 17:41

Comprehensive Guide to Evaluating RAG Systems

Published:Dec 24, 2025 06:59

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1 min read

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Zenn LLM

Analysis

This article provides a concise overview of evaluating Retrieval-Augmented Generation (RAG) systems. It introduces the concept of RAG and highlights its advantages over traditional LLMs, such as improved accuracy and adaptability through external knowledge retrieval. The article promises to explore various evaluation methods for RAG, making it a useful resource for practitioners and researchers interested in understanding and improving the performance of these systems. The brevity suggests it's an introductory piece, potentially lacking in-depth technical details but serving as a good starting point.

Key Takeaways

•RAG enhances LLMs with external knowledge retrieval.
•RAG improves accuracy, up-to-dateness, and domain adaptation.
•The article focuses on methods for evaluating RAG systems.

Reference

“RAG (Retrieval-Augmented Generation) is an architecture where LLMs (Large Language Models) retrieve external knowledge and generate text based on the results.”

Permalink Zenn LLM

Research #llm 🔬 ResearchAnalyzed: Dec 25, 2025 02:34

M$^3$KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation

Published:Dec 24, 2025 05:00

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1 min read

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ArXiv NLP

Analysis

This paper introduces M$^3$KG-RAG, a novel approach to Retrieval-Augmented Generation (RAG) that leverages multi-hop multimodal knowledge graphs (MMKGs) to enhance the reasoning and grounding capabilities of multimodal large language models (MLLMs). The key innovations include a multi-agent pipeline for constructing multi-hop MMKGs and a GRASP (Grounded Retrieval And Selective Pruning) mechanism for precise entity grounding and redundant context pruning. The paper addresses limitations in existing multimodal RAG systems, particularly in modality coverage, multi-hop connectivity, and the filtering of irrelevant knowledge. The experimental results demonstrate significant improvements in MLLMs' performance across various multimodal benchmarks, suggesting the effectiveness of the proposed approach in enhancing multimodal reasoning and grounding.

Key Takeaways

•Introduces M$^3$KG-RAG for enhanced multimodal RAG.
•Utilizes multi-hop MMKGs to improve reasoning depth.
•Employs GRASP for precise entity grounding and context pruning.

Reference

“To address these limitations, we propose M$^3$KG-RAG, a Multi-hop Multimodal Knowledge Graph-enhanced RAG that retrieves query-aligned audio-visual knowledge from MMKGs, improving reasoning depth and answer faithfulness in MLLMs.”

Permalink ArXiv NLP

Artificial Intelligence #Retrieval-Augmented Generation 📝 BlogAnalyzed: Dec 24, 2025 13:53

RAG Accuracy Depends on Question Design: Improving Accuracy Before Search with HyDE

Published:Dec 23, 2025 22:00

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1 min read

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Zenn LLM

Analysis

This article highlights a crucial aspect often overlooked in RAG (Retrieval-Augmented Generation) implementations: the quality of the initial question. While much focus is placed on optimizing chunking and reranking after the search, the article argues that the question itself significantly impacts retrieval accuracy. It introduces HyDE (Hypothetical Document Embeddings) as a method to improve search precision by generating a virtual document tailored to the query, thereby enhancing the relevance of retrieved information. The article promises to offer a new perspective on RAG search accuracy by emphasizing the importance of question design.

Key Takeaways

•Question design is crucial for RAG accuracy.
•HyDE improves search precision by generating virtual documents.
•Focusing on question design offers a new perspective on RAG optimization.

Reference

“多くの場合、精度改善の議論は「検索後」の工程に集中しがちですが、実はその前段階である「質問そのもの」が精度改善を大きく左右しています。”

Permalink Zenn LLM

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

Boosting Foundation Models: Retrieval-Augmented Prompt Learning

Published:Dec 23, 2025 08:15

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1 min read

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ArXiv

Analysis

This research explores enhancing pre-trained foundation models using retrieval-augmented prompt learning. The study likely examines methods to improve model performance by integrating external knowledge sources during the prompting process.

Key Takeaways

•Focuses on improving the performance of existing foundation models.
•Employs retrieval-augmented methods to enrich prompts.
•Suggests potential advancements in prompt engineering.

Reference

“The research is based on a study from ArXiv.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:56

M$^3$KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation

Published:Dec 23, 2025 07:54

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1 min read

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ArXiv

Analysis

The article introduces M$^3$KG-RAG, a system that combines multi-hop reasoning, multimodal data, and knowledge graphs to improve retrieval-augmented generation (RAG) for language models. The focus is on enhancing the accuracy and relevance of generated text by leveraging structured knowledge and diverse data types. The use of multi-hop reasoning suggests an attempt to address complex queries that require multiple steps of inference. The integration of multimodal data (likely images, audio, etc.) indicates a move towards more comprehensive and contextually rich information retrieval. The paper likely details the architecture, training methodology, and evaluation metrics of the system.

Key Takeaways

•M$^3$KG-RAG is a system for improving Retrieval-Augmented Generation (RAG).
•It uses multi-hop reasoning, multimodal data, and knowledge graphs.
•The goal is to enhance the accuracy and relevance of generated text.

Reference

“The paper likely details the architecture, training methodology, and evaluation metrics of the system.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:09

Adaptive Financial Sentiment Analysis for NIFTY 50 via Instruction-Tuned LLMs, RAG and Reinforcement Learning Approaches

Published:Dec 23, 2025 06:27

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1 min read

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ArXiv

Analysis

This article describes a research paper exploring the use of Large Language Models (LLMs) for financial sentiment analysis, specifically focusing on the NIFTY 50 index. It mentions the use of instruction-tuned LLMs, Retrieval-Augmented Generation (RAG), and Reinforcement Learning (RL) techniques. The focus is on adapting these methods for financial applications.

Key Takeaways

Reference

“The article is sourced from ArXiv, indicating it's a research paper.”

Permalink ArXiv

Research #Agent 🔬 ResearchAnalyzed: Jan 10, 2026 08:25

Multi-Agent Retrieval-Augmented Framework Improves Work-in-Progress Prediction

Published:Dec 22, 2025 19:51

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1 min read

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ArXiv

Analysis

This research, published on ArXiv, introduces a multi-agent retrieval-augmented framework, which is a promising approach for improving work-in-progress prediction. The paper's novelty lies in its multi-agent approach, offering potential for enhanced accuracy and efficiency in complex prediction tasks.

Key Takeaways

•The core innovation is the use of a multi-agent framework.
•The framework incorporates retrieval-augmentation for improved prediction.
•The application area is work-in-progress prediction.

Reference

“The research focuses on a multi-agent retrieval-augmented framework for work-in-progress prediction.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:37

MaP-AVR: A Meta-Action Planner for Agents Leveraging Vision Language Models and Retrieval-Augmented Generation

Published:Dec 22, 2025 14:58

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1 min read

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ArXiv

Analysis

This article introduces MaP-AVR, a novel meta-action planner. The core idea is to combine Vision Language Models (VLMs) and Retrieval-Augmented Generation (RAG) for agent planning. The use of RAG suggests an attempt to improve the agent's ability to access and utilize external knowledge, potentially mitigating some limitations of VLMs. The title clearly indicates the focus on agent planning within the context of AI research.

Key Takeaways

•Focuses on agent planning.
•Leverages Vision Language Models (VLMs) and Retrieval-Augmented Generation (RAG).
•Aims to improve agent's knowledge access and utilization.

Reference

“The article is sourced from ArXiv, indicating it's a research paper.”

Permalink ArXiv

Research #RAG 🔬 ResearchAnalyzed: Jan 10, 2026 08:44

QuCo-RAG: Improving Retrieval-Augmented Generation with Uncertainty Quantification

Published:Dec 22, 2025 08:28

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1 min read

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ArXiv

Analysis

This research explores a novel approach to enhance Retrieval-Augmented Generation (RAG) by quantifying uncertainty derived from the pre-training corpus. The method, QuCo-RAG, could lead to more reliable and contextually aware AI models.

Key Takeaways

•QuCo-RAG aims to improve RAG models.
•The approach leverages uncertainty quantification from the pre-training data.
•This research has implications for more reliable AI generation.

Reference

“The paper focuses on quantifying uncertainty from the pre-training corpus for Dynamic Retrieval-Augmented Generation.”

Permalink ArXiv

Research #RAG 🔬 ResearchAnalyzed: Jan 10, 2026 09:07

Bidirectional RAG: Enhancing LLM Reliability with Multi-Stage Validation

Published:Dec 20, 2025 19:42

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1 min read

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ArXiv

Analysis

This research explores a novel approach to Retrieval-Augmented Generation (RAG) models, focusing on enhancing their safety and reliability. The multi-stage validation process signifies a potential leap in mitigating risks associated with LLM outputs, promising more trustworthy AI systems.

Key Takeaways

•Proposes a multi-stage validation process for RAG models.
•Aims to improve the safety and reliability of LLM outputs.
•Focuses on a bidirectional approach to information retrieval and validation within RAG.

Reference

“The research focuses on Bidirectional RAG, implying an improved flow of information and validation.”

Permalink ArXiv

Research #RAG 🔬 ResearchAnalyzed: Jan 10, 2026 09:12

Lightweight Reranking Framework Enhances Retrieval-Augmented Generation

Published:Dec 20, 2025 11:53

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1 min read

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ArXiv

Analysis

This research introduces a novel framework, LIR^3AG, aimed at improving Retrieval-Augmented Generation (RAG) models. The focus on a 'lightweight' approach suggests potential efficiency gains in processing and resource utilization, which is a key consideration for practical applications.

Key Takeaways

•LIR^3AG is designed to improve the performance of RAG models.
•The framework emphasizes a lightweight design, potentially leading to efficiency improvements.
•The research likely targets the efficiency and effectiveness of document retrieval and generation processes.

Reference

“LIR^3AG is a Lightweight Rerank Reasoning Strategy Framework for Retrieval-Augmented Generation.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:51

MMRAG-RFT: Two-stage Reinforcement Fine-tuning for Explainable Multi-modal Retrieval-augmented Generation

Published:Dec 19, 2025 03:19

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1 min read

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ArXiv

Analysis

The article introduces a novel approach, MMRAG-RFT, for improving explainability in multi-modal retrieval-augmented generation. The two-stage reinforcement fine-tuning strategy likely aims to optimize the model's ability to generate coherent and well-supported outputs by leveraging both retrieval and generation components. The focus on explainability suggests an attempt to address the 'black box' nature of many AI models, making the reasoning process more transparent.

Key Takeaways

•MMRAG-RFT is a new approach for explainable multi-modal retrieval-augmented generation.
•It utilizes a two-stage reinforcement fine-tuning strategy.
•The goal is to improve the model's ability to generate coherent and well-supported outputs.
•The focus on explainability aims to make the model's reasoning process more transparent.

Reference

“”

Permalink ArXiv

Research #RAG 🔬 ResearchAnalyzed: Jan 10, 2026 09:56

Augmentation Strategies in Biomedical RAG: A Glycobiology Question Answering Study

Published:Dec 18, 2025 17:35

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1 min read

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ArXiv

Analysis

This ArXiv paper investigates advanced techniques in Retrieval-Augmented Generation (RAG) within a specialized domain. The focus on multi-modal data and glycobiology provides a specific and potentially impactful application of AI.

Key Takeaways

•Focuses on improving RAG performance in biomedical question answering.
•Explores augmentation strategies, likely including techniques beyond basic retrieval.
•Applies findings to the specific field of glycobiology.

Reference

“The study evaluates question answering in Glycobiology.”

Permalink ArXiv