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

business #automation 📝 BlogAnalyzed: Jan 16, 2026 01:17

Sansan's "Bill One": A Refreshing Approach to Accounting Automation

Published:Jan 15, 2026 23:00

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1 min read

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ITmedia AI+

Analysis

In a world dominated by generative AI, Sansan's "Bill One" takes a bold and fascinating approach. This accounting automation service carves its own path, offering a unique value proposition by forgoing the use of generative AI. This innovative strategy promises a fresh perspective on how we approach financial processes.

Key Takeaways

•Sansan's "Bill One" is a new accounting automation service.
•It intentionally avoids using generative AI.
•This unique approach stems from the specific demands of accounting operations.

Reference

“The article suggests that the decision not to use generative AI is based on "non-negotiable principles" specific to accounting tasks.”

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business #llm 📝 BlogAnalyzed: Jan 4, 2026 02:51

Gemini CLI for Core Systems: Double-Entry Bookkeeping and Credit Creation

Published:Jan 4, 2026 02:33

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1 min read

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Qiita LLM

Analysis

This article explores the potential of using Gemini CLI to build core business systems, specifically focusing on double-entry bookkeeping and credit creation. While the concept is intriguing, the article lacks technical depth and practical implementation details, making it difficult to assess the feasibility and scalability of such a system. The reliance on natural language input for accounting tasks raises concerns about accuracy and security.

Key Takeaways

•The article discusses using Gemini CLI for building core systems.
•It focuses on double-entry bookkeeping and credit creation.
•The approach aims to simplify system development without requiring extensive programming knowledge.

Reference

“今回は、プログラミングの専門知識がなくても、対話AI（Gemini CLI）を使って基幹システムに挑戦です。”

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Research #Diffusion Model 🔬 ResearchAnalyzed: Jan 10, 2026 08:13

CoDi: Low-Shot Counting with Exemplar-Conditioned Diffusion Models

Published:Dec 23, 2025 08:31

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1 min read

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ArXiv

Analysis

This research explores a novel application of diffusion models for low-shot object counting, a challenging computer vision task. The paper's strength lies in demonstrating the effectiveness of exemplar conditioning, allowing the model to learn from limited examples.

Key Takeaways

•Proposes a novel approach to low-shot object counting using diffusion models.
•Employs exemplar conditioning to enable learning from limited data.
•Represents an advancement in applying diffusion models to the counting task.

Reference

“CoDi is an exemplar-conditioned diffusion model.”

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Research #Accounting 🔬 ResearchAnalyzed: Jan 10, 2026 08:48

Backward Growth Accounting: A Novel Approach for Strategic Business Planning

Published:Dec 22, 2025 05:01

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1 min read

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ArXiv

Analysis

This article, sourced from ArXiv, suggests a potential application of economic tools in a novel way for business strategy. The focus on 'Backward Growth Accounting' highlights an interesting area for future research and practical application.

Key Takeaways

•Explores a new method for business growth planning.
•Utilizes economic principles for strategic decision-making.
•Potentially provides a different perspective on business growth strategies.

Reference

“The article proposes 'Backward Growth Accounting' as an economic tool.”

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Research #Vision Transformer 🔬 ResearchAnalyzed: Jan 10, 2026 09:24

Self-Explainable Vision Transformers: A Breakthrough in AI Interpretability

Published:Dec 19, 2025 18:47

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1 min read

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ArXiv

Analysis

This research from ArXiv focuses on enhancing the interpretability of Vision Transformers. By introducing Keypoint Counting Classifiers, the study aims to achieve self-explainable models without requiring additional training.

Key Takeaways

•The research aims to improve the understanding of how Vision Transformers make decisions.
•The proposed method achieves self-explainability without extra training.
•The work potentially increases the trustworthiness and application range of Vision Transformers.

Reference

“The study introduces Keypoint Counting Classifiers to create self-explainable models.”

Permalink ArXiv

Research #User Modeling 🔬 ResearchAnalyzed: Jan 10, 2026 10:01

Abacus: A Novel Self-Supervised Approach to Sequential User Modeling

Published:Dec 18, 2025 14:24

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1 min read

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ArXiv

Analysis

This research introduces a novel self-supervised learning technique for sequential user modeling, potentially improving the accuracy of predictions based on user behavior. The paper's focus on distributional pretraining and event counting alignment suggests a sophisticated approach to capturing user patterns.

Key Takeaways

•Proposes a self-supervised learning method for sequential user modeling.
•Employs distributional pretraining and event counting alignment.
•Aims to improve accuracy in predicting user behavior.

Reference

“The research is sourced from ArXiv.”

Permalink ArXiv

Research #Counting 🔬 ResearchAnalyzed: Jan 10, 2026 10:05

CountZES: Zero-Shot Counting with Exemplar Selection

Published:Dec 18, 2025 11:12

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1 min read

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ArXiv

Analysis

This research explores zero-shot counting using exemplar selection, a novel approach with potential applications in various fields. The focus on zero-shot learning suggests a push towards more efficient and adaptable AI models.

Key Takeaways

•Focuses on zero-shot learning, allowing for counting without class-specific training.
•Utilizes exemplar selection, which might involve choosing relevant examples for counting.
•Potentially applicable in fields like image analysis or object detection.

Reference

“The paper likely introduces a new method for counting objects or instances without prior training data for a specific class.”

Permalink ArXiv

Research #Computer Vision 🔬 ResearchAnalyzed: Jan 10, 2026 10:28

Evaluating Visual Counting Skills in AI: Architectures vs. Vision-Language Models

Published:Dec 17, 2025 09:56

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1 min read

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ArXiv

Analysis

This ArXiv paper provides a comparative analysis of specialized counting architectures and vision-language models in their ability to perform visual enumeration tasks. The research likely contributes to a better understanding of the strengths and weaknesses of different AI approaches in visual understanding.

Key Takeaways

•Compares visual enumeration performance of specialized architectures and vision-language models.
•Likely identifies the most effective approaches for counting objects in images.
•Contributes to advancements in computer vision and AI understanding of visual scenes.

Reference

“The study assesses the visual enumeration abilities.”

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Research #Benchmarking 🔬 ResearchAnalyzed: Jan 10, 2026 11:12

Finch: Benchmarking AI in Spreadsheet-Centric Finance & Accounting Workflows

Published:Dec 15, 2025 10:28

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1 min read

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ArXiv

Analysis

This article discusses the benchmarking of AI within finance and accounting workflows heavily reliant on spreadsheets. The focus on spreadsheets highlights a specific, and often overlooked, area of AI application in enterprise systems.

Key Takeaways

•Focuses on a niche, yet crucial, application of AI within enterprise finance.
•Highlights the importance of benchmarking AI performance in real-world spreadsheet environments.
•Potentially addresses the challenges of automating or augmenting spreadsheet-based tasks.

Reference

“The article's context revolves around benchmarking AI in finance and accounting workflows.”

Permalink ArXiv

Research #Domain Adaptation 🔬 ResearchAnalyzed: Jan 10, 2026 11:41

Diffusion-Based Domain Adaptation for Improved Cell Counting

Published:Dec 12, 2025 18:19

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1 min read

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ArXiv

Analysis

This research explores using diffusion models to address the domain gap problem in cell counting, which often arises when models are trained on one dataset and applied to another. The approach suggests a promising path for enhancing the generalizability and performance of cell counting algorithms across different datasets.

Key Takeaways

•Applies diffusion models to domain adaptation in cell counting.
•Addresses the challenge of domain gaps in medical image analysis.
•Aims to improve the robustness and generalizability of cell counting algorithms.

Reference

“The article focuses on reducing the domain gap.”

Permalink ArXiv

Research #Radar 🔬 ResearchAnalyzed: Jan 10, 2026 12:06

mmCounter: Advancing Indoor People Counting with mmWave Radar

Published:Dec 11, 2025 07:16

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1 min read

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ArXiv

Analysis

This research explores a novel application of mmWave radar for people counting in crowded indoor environments, a crucial need for various applications. The ArXiv source suggests that the work presents a static approach, implying the potential for real-time monitoring and analysis.

Key Takeaways

•mmWave radar is utilized for people counting in indoor environments.
•The approach is designed for dense or crowded scenarios.
•The focus is on static people counting, suggesting real-time monitoring capabilities.

Reference

“The study focuses on static people counting in dense indoor scenarios.”

Permalink ArXiv

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counting

Sansan's "Bill One": A Refreshing Approach to Accounting Automation

Analysis

Key Takeaways

Gemini CLI for Core Systems: Double-Entry Bookkeeping and Credit Creation

Analysis

Key Takeaways

CoDi: Low-Shot Counting with Exemplar-Conditioned Diffusion Models

Analysis

Key Takeaways

Backward Growth Accounting: A Novel Approach for Strategic Business Planning

Analysis

Key Takeaways

Self-Explainable Vision Transformers: A Breakthrough in AI Interpretability

Analysis

Key Takeaways

Abacus: A Novel Self-Supervised Approach to Sequential User Modeling

Analysis

Key Takeaways

CountZES: Zero-Shot Counting with Exemplar Selection

Analysis

Key Takeaways

Evaluating Visual Counting Skills in AI: Architectures vs. Vision-Language Models

Analysis

Key Takeaways

Finch: Benchmarking AI in Spreadsheet-Centric Finance & Accounting Workflows

Analysis

Key Takeaways

Diffusion-Based Domain Adaptation for Improved Cell Counting

Analysis

Key Takeaways

mmCounter: Advancing Indoor People Counting with mmWave Radar

Analysis

Key Takeaways

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Trending Topics

Sansan's "Bill One": A Refreshing Approach to Accounting Automation

Analysis

Key Takeaways

Gemini CLI for Core Systems: Double-Entry Bookkeeping and Credit Creation

Analysis

Key Takeaways

CoDi: Low-Shot Counting with Exemplar-Conditioned Diffusion Models

Analysis

Key Takeaways

Backward Growth Accounting: A Novel Approach for Strategic Business Planning

Analysis

Key Takeaways

Self-Explainable Vision Transformers: A Breakthrough in AI Interpretability

Analysis

Key Takeaways

Abacus: A Novel Self-Supervised Approach to Sequential User Modeling

Analysis

Key Takeaways

CountZES: Zero-Shot Counting with Exemplar Selection

Analysis

Key Takeaways

Evaluating Visual Counting Skills in AI: Architectures vs. Vision-Language Models

Analysis

Key Takeaways

Finch: Benchmarking AI in Spreadsheet-Centric Finance & Accounting Workflows

Analysis

Key Takeaways

Diffusion-Based Domain Adaptation for Improved Cell Counting

Analysis

Key Takeaways

mmCounter: Advancing Indoor People Counting with mmWave Radar

Analysis

Key Takeaways

📬 Get AI News Delivered

Browse by Category

Trending Topics