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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
1 min read
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

Reference

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

PermalinkQiita LLM
Research#llm🔬 ResearchAnalyzed: Dec 25, 2025 11:16

Diffusion Models in Simulation-Based Inference: A Tutorial Review

Published:Dec 25, 2025 05:00
1 min read
ArXiv Stats ML

Analysis

This arXiv paper presents a tutorial review of diffusion models in the context of simulation-based inference (SBI). It highlights the increasing importance of diffusion models for estimating latent parameters from simulated and real data. The review covers key aspects such as training, inference, and evaluation strategies, and explores concepts like guidance, score composition, and flow matching. The paper also discusses the impact of noise schedules and samplers on efficiency and accuracy. By providing case studies and outlining open research questions, the review offers a comprehensive overview of the current state and future directions of diffusion models in SBI, making it a valuable resource for researchers and practitioners in the field.
Reference

“Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real data.”

PermalinkArXiv Stats ML
Research#quantum computing🔬 ResearchAnalyzed: Jan 4, 2026 07:33

Benchmarking the Impact of Active Space Selection on the VQE Pipeline for Quantum Drug Discovery

Published:Dec 20, 2025 03:56
1 min read
ArXiv

Analysis

This article likely presents a study that evaluates different methods for selecting the active space in the Variational Quantum Eigensolver (VQE) algorithm, specifically within the context of drug discovery. The focus is on benchmarking these methods to understand their impact on the performance and accuracy of the VQE pipeline. The source, ArXiv, suggests this is a pre-print or research paper.

Key Takeaways

    Reference

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    PermalinkArXiv
    Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:17

    Workload Characterization for Branch Predictability

    Published:Dec 17, 2025 17:12
    1 min read
    ArXiv

    Analysis

    This article likely explores the characteristics of different workloads and their impact on the accuracy of branch prediction in computer systems. It probably analyzes how various factors, such as code structure and data dependencies, influence the ability of a processor to correctly predict the outcome of branch instructions. The research could involve experiments and simulations to identify patterns and develop techniques for improving branch prediction performance.

    Key Takeaways

      Reference

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      PermalinkArXiv
      Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:36

      Leakage-Aware Bandgap Prediction on the JARVIS-DFT Dataset: A Phase-Wise Feature Analysis

      Published:Dec 17, 2025 08:22
      1 min read
      ArXiv

      Analysis

      This article focuses on predicting bandgaps using a leakage-aware approach on the JARVIS-DFT dataset. The phase-wise feature analysis suggests a detailed investigation into the factors influencing bandgap prediction. The use of 'leakage-aware' implies an attempt to address potential data leakage issues, which is crucial for reliable model performance. The research likely explores the impact of different features on the accuracy of bandgap prediction.

      Key Takeaways

        Reference

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        PermalinkArXiv
        Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:04

        Sequential Realization of Quantum Instruments

        Published:Dec 16, 2025 16:56
        1 min read
        ArXiv

        Analysis

        This article likely discusses the implementation of quantum instruments in a sequential manner. The focus is on the methodology and techniques used to realize these instruments, potentially exploring the order of operations and the impact on performance or accuracy. The source, ArXiv, suggests this is a research paper.

        Key Takeaways

          Reference

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          PermalinkArXiv
          Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:19

          Evaluating Adversarial Attacks on Federated Learning for Temperature Forecasting

          Published:Dec 15, 2025 11:22
          1 min read
          ArXiv

          Analysis

          This article likely investigates the vulnerability of federated learning models used for temperature forecasting to adversarial attacks. It would analyze how these attacks can compromise the accuracy and reliability of the forecasting models. The research would likely involve designing and testing different attack strategies and evaluating their impact on the model's performance.
          Reference

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          PermalinkArXiv
          Research#GCN🔬 ResearchAnalyzed: Jan 10, 2026 11:17

          Diagnostic Study Reveals Limitations of Graph Convolutional Networks

          Published:Dec 15, 2025 03:23
          1 min read
          ArXiv

          Analysis

          This ArXiv article provides a diagnostic study on the performance of Graph Convolutional Networks (GCNs), focusing on label scarcity and structural properties. The research offers valuable insights into the practical applicability of GCNs, informing researchers and practitioners about the conditions where they are most and least effective.
          Reference

          “The study focuses on label scarcity and structural properties.”

          PermalinkArXiv
          Research#Causal Reasoning🔬 ResearchAnalyzed: Jan 10, 2026 11:30

          Quantization and GraphRAG Improve Causal Reasoning in AI Systems

          Published:Dec 13, 2025 17:54
          1 min read
          ArXiv

          Analysis

          The study explores the impact of quantization and GraphRAG on the accuracy of interventional and counterfactual reasoning in AI. This research contributes to the ongoing efforts to optimize the performance and efficiency of causal reasoning models.
          Reference

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

          PermalinkArXiv
          Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:41

          Prompting Science Report 4: Playing Pretend: Expert Personas Don't Improve Factual Accuracy

          Published:Dec 5, 2025 16:35
          1 min read
          ArXiv

          Analysis

          This article reports on research that examines the impact of using expert personas in prompts for Large Language Models (LLMs) on factual accuracy. The findings suggest that adopting such personas does not lead to improved accuracy. This is a significant finding for those using LLMs for information retrieval and generation, as it challenges the common assumption that framing prompts in this way is beneficial.
          Reference

          “The study's findings indicate that using expert personas in prompts does not improve factual accuracy.”

          PermalinkArXiv