Search: retrieved - ai.jp.net

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

•

1 min read

•

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 Paper #Video Compression, Autoregressive Models, Pretraining 🔬 ResearchAnalyzed: Jan 3, 2026 16:00

Pretraining for Long Video Compression

Published:Dec 29, 2025 20:29

•

1 min read

•

ArXiv

Analysis

This paper introduces a novel pretraining method (PFP) for compressing long videos into shorter contexts, focusing on preserving high-frequency details of individual frames. This is significant because it addresses the challenge of handling long video sequences in autoregressive models, which is crucial for applications like video generation and understanding. The ability to compress a 20-second video into a context of ~5k length with preserved perceptual quality is a notable achievement. The paper's focus on pretraining and its potential for fine-tuning in autoregressive video models suggests a practical approach to improving video processing capabilities.

Key Takeaways

•Proposes a pretraining method (PFP) for video compression.
•Focuses on preserving high-frequency details of individual frames.
•Achieves compression of 20-second videos into ~5k context length.
•Suitable for fine-tuning in autoregressive video models.

Reference

“The baseline model can compress a 20-second video into a context at about 5k length, where random frames can be retrieved with perceptually preserved appearances.”

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

•

1 min read

•

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 🏛️ OfficialAnalyzed: Dec 28, 2025 21:58

Testing Context Relevance of RAGAS (Nvidia Metrics)

Published:Dec 28, 2025 15:22

•

1 min read

•

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 #llm 🔬 ResearchAnalyzed: Dec 25, 2025 00:04

PhysMaster: Autonomous AI Physicist for Theoretical and Computational Physics Research

Published:Dec 24, 2025 05:00

•

1 min read

•

ArXiv AI

Analysis

This ArXiv paper introduces PhysMaster, an LLM-based agent designed to function as an autonomous physicist. The core innovation lies in its ability to integrate abstract reasoning with numerical computation, addressing a key limitation of existing LLM agents in scientific problem-solving. The use of LANDAU for knowledge management and an adaptive exploration strategy are also noteworthy. The paper claims significant advancements in accelerating, automating, and enabling autonomous discovery in physics research. However, the claims of autonomous discovery should be viewed cautiously until further validation and scrutiny by the physics community. The paper's impact will depend on the reproducibility and generalizability of PhysMaster's performance across a wider range of physics problems.

Key Takeaways

•PhysMaster is an LLM-based agent for autonomous physics research.
•It integrates abstract reasoning with numerical computation.
•It uses LANDAU for knowledge management and an adaptive exploration strategy.

Reference

“PhysMaster couples absract reasoning with numerical computation and leverages LANDAU, the Layered Academic Data Universe, which preserves retrieved literature, curated prior knowledge, and validated methodological traces, enhancing decision reliability and stability.”

Permalink ArXiv AI

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

•

1 min read

•

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 4, 2026 09:53

MemR^3: Memory Retrieval via Reflective Reasoning for LLM Agents

Published:Dec 23, 2025 10:49

•

1 min read

•

ArXiv

Analysis

This article introduces MemR^3, a novel approach for memory retrieval in LLM agents. The core idea revolves around using reflective reasoning to improve the accuracy and relevance of retrieved information. The paper likely details the architecture, training methodology, and experimental results demonstrating the effectiveness of MemR^3 compared to existing memory retrieval techniques. The focus is on enhancing the agent's ability to access and utilize relevant information from its memory.

Key Takeaways

•Focuses on improving memory retrieval for LLM agents.
•Employs reflective reasoning to enhance accuracy and relevance.
•Likely presents architecture, training, and experimental results.

Reference

“The article likely presents a new method for improving memory retrieval in LLM agents.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:27

Auto-Prompting with Retrieval Guidance for Frame Detection in Logistics

Published:Dec 22, 2025 10:29

•

1 min read

•

ArXiv

Analysis

This article, sourced from ArXiv, likely presents a novel approach to frame detection within the logistics domain. The core concept revolves around 'auto-prompting' which suggests the use of automated techniques to generate prompts for a model, potentially an LLM. The inclusion of 'retrieval guidance' indicates that the prompting process is informed by retrieved information, likely from a knowledge base or dataset relevant to logistics. This could improve the accuracy and efficiency of frame detection, which is crucial for tasks like understanding and processing logistics documents or events. The research likely explores the effectiveness of this approach compared to existing methods.

Key Takeaways

•Focuses on frame detection in the logistics domain.
•Employs auto-prompting techniques.
•Utilizes retrieval guidance to inform the prompting process.
•Aims to improve accuracy and efficiency of frame detection.

Reference

“The article's specific methodologies and experimental results would be crucial to assess its contribution. The effectiveness of the retrieval mechanism and the prompt generation strategy are key aspects to evaluate.”

Permalink ArXiv

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

Comparative Analysis of Retrieval-Augmented Generation for Bengali Translation with LLMs

Published:Dec 16, 2025 08:18

•

1 min read

•

ArXiv

Analysis

This article focuses on a specific application of LLMs: Bengali language translation. It investigates different Retrieval-Augmented Generation (RAG) techniques, which is a common approach to improve LLM performance by providing external knowledge. The focus on Bengali dialects suggests a practical application with potential for cultural preservation and improved communication within the Bengali-speaking community. The use of ArXiv as the source indicates this is a research paper, likely detailing the methodology, results, and comparison of different RAG approaches.

Key Takeaways

•Focus on a specific language (Bengali) and task (standard-to-dialect translation).
•Investigates Retrieval-Augmented Generation (RAG) techniques.
•Likely compares different RAG approaches.
•Aims to improve LLM performance for a practical translation task.

Reference

“The article likely explores how different RAG techniques (e.g., different retrieval methods, different ways of integrating retrieved information) impact the accuracy and fluency of Bengali standard-to-dialect translation.”

Permalink ArXiv

Research #RAG 🔬 ResearchAnalyzed: Jan 10, 2026 13:09

Boosting RAG: Self-Explaining Contrastive Evidence Re-ranking for Enhanced Factuality

Published:Dec 4, 2025 17:24

•

1 min read

•

ArXiv

Analysis

This research explores a novel approach to enhance Retrieval-Augmented Generation (RAG) models, focusing on improving factuality and transparency. The use of self-explaining contrastive evidence re-ranking is a promising technique for better aligning generated text with retrieved information.

Key Takeaways

•Focuses on improving the factuality of RAG systems.
•Employs self-explaining contrastive evidence re-ranking.
•The approach aims to enhance transparency in the generation process.

Reference

“Self-Explaining Contrastive Evidence Re-ranking”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:24

Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models

Published:Nov 19, 2025 00:51

•

1 min read

•

ArXiv

Analysis

This article, sourced from ArXiv, likely presents research on improving the efficiency and robustness of retrieval-augmented language models. The focus is on abstractive compression, which aims to summarize and condense information while maintaining key details, and how to make this process more resilient to noisy or imperfect data often encountered in real-world applications. The research likely explores techniques to enhance the performance of these models in scenarios where the retrieved information is not perfectly accurate or complete.

Key Takeaways

Reference

“”

Permalink ArXiv

Application #LLM, Semantic Search, Bible, RAG 👥 CommunityAnalyzed: Jan 3, 2026 06:47

Biblos: Semantic Bible Search with LLM

Published:Oct 27, 2023 16:28

•

1 min read

•

Hacker News

Analysis

Biblos is a Retrieval Augmented Generation (RAG) application that leverages vector search and a Large Language Model (LLM) to provide semantic search and summarization of Bible passages. It uses Chroma for vector search with BAAI BGE embeddings and Anthropic's Claude LLM for summarization. The application is built with Python and a Streamlit Web UI, deployed on render.com. The focus is on semantic understanding of the Bible, allowing users to search by topic or keywords and receive summarized results.

Key Takeaways

•Semantic search of Bible passages using vector embeddings.
•Summarization of search results using Claude LLM.
•Built with Python and Streamlit, deployed on render.com.
•Uses a Retrieval Augmented Generation (RAG) architecture.

Reference

“The tool employs Anthropic's Claude LLM model for generating high-quality summaries of retrieved passages, contextualizing your search topic.”

Permalink Hacker News

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

Analysis

Key Takeaways

Pretraining for Long Video Compression

Analysis

Key Takeaways

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

Analysis

Key Takeaways

Testing Context Relevance of RAGAS (Nvidia Metrics)

Analysis

Key Takeaways

PhysMaster: Autonomous AI Physicist for Theoretical and Computational Physics Research

Analysis

Key Takeaways

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

Analysis

Key Takeaways

MemR^3: Memory Retrieval via Reflective Reasoning for LLM Agents

Analysis

Key Takeaways

Auto-Prompting with Retrieval Guidance for Frame Detection in Logistics

Analysis

Key Takeaways

Comparative Analysis of Retrieval-Augmented Generation for Bengali Translation with LLMs

Analysis

Key Takeaways

Boosting RAG: Self-Explaining Contrastive Evidence Re-ranking for Enhanced Factuality

Analysis

Key Takeaways

Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models

Analysis

Key Takeaways

Biblos: Semantic Bible Search with LLM

Analysis

Key Takeaways

📬 Get AI News Delivered

Browse by Category

Trending Topics

📬 Get AI News Delivered

Browse by Category

Trending Topics