# Main Content

## Pico-Banana-400K
- **Pico-Banana-400K** is a comprehensive dataset featuring **~400K text–image–edit triplets**, facilitating advancements in **text-guided image editing** through diverse transformations across **35 edit operations** and **8 semantic categories**.

## Definition of AGI
- This paper defines **Artificial General Intelligence (AGI)** as the ability to match the cognitive versatility of a well-educated adult, utilizing a framework based on the **Cattell-Horn-Carroll theory** to assess AI systems across ten cognitive domains.

## Nvidia DGX Spark: When Benchmark Numbers Meet Production Reality
- **DGX Lab benchmarks reveal significant discrepancies** between expected and actual performance metrics, highlighting the need for more accurate testing environments.

## TOON – Token Oriented Object Notation
- **Token-Oriented Object Notation (TOON)** is a **compact** format that reduces token usage by **30-60%** compared to JSON, making it ideal for structured data input to Large Language Models (LLMs) while maintaining readability through an indentation-based structure.

## DeepAgent: A General Reasoning Agent with Scalable Toolsets
- **DeepAgent** is an end-to-end deep reasoning agent that autonomously discovers tools and executes actions, addressing the limitations of traditional agent frameworks that rely on predefined workflows.

## We're in the Wrong Moment
- **Generative AI** has transformed the coding landscape, diminishing the joy of programming for many, as it automates tasks that once required creativity and problem-solving skills.

## Cutting Inference Costs from $46K to $7.5K by Fine-Tuning Qwen-Image-Edit
- **Fine-tuning Qwen-Image-Edit** reduced inference costs from **$46K to $7.5K**, enabling the generation of a **1.2 million image catalog** with significant efficiency improvements.

## PKBoost: Gradient Boosting that Stays Accurate Under Data Drift
- **PKBoost** demonstrates superior performance in handling **data drift**, achieving only a **2% degradation** in PR-AUC compared to **XGBoost's 32%**, making it a robust choice for imbalanced datasets like credit card fraud detection.

## The New Calculus of AI-Based Coding
- **AI agents** like Amazon Q and Kiro are revolutionizing coding practices, enabling teams to achieve **10x throughput** by collaborating with human engineers who ensure code quality through rigorous review processes.

## Sparser Block-Sparse Attention via Token Permutation
- **PBS-Attn** enhances **block-sparse attention** by utilizing token permutation, significantly improving computational efficiency in large language models (LLMs) during long-context prefilling.

## Streaming Datasets: 100x More Efficient
- **Streaming datasets have achieved a remarkable 100x efficiency improvement**, allowing users to train on multi-TB datasets without the hassle of downloading, thus eliminating common issues like "disk out of space" errors and significantly speeding up data resolution times.

## A Geometric Interpretation of the Weight Update in GPTQ Quantization Algorithm and a Novel Solution
- **GPTQ quantization** modifies weights row-wise, utilizing a Lagrangian approach to derive updates, which enhances efficiency in matrix quantization.

## huggingface_hub v1.0: Five Years of Building the Foundation of Open Machine Learning
- **huggingface_hub v1.0** marks a significant milestone, supporting **200,000 dependent libraries** and providing access to over **2 million models**, **500,000 datasets**, and **1 million Spaces**, reflecting its evolution into a robust platform for open machine learning.

## Co-Sight: Enhancing LLM-Based Agents
- **Co-Sight** enhances LLM-based agents by implementing **Conflict-Aware Meta-Verification (CAMV)** and **Trustworthy Reasoning with Structured Facts (TRSF)**, transforming reasoning into a **falsifiable** and **auditable** process that improves efficiency and reliability.

## Boosting Accuracy and Efficiency of Budget Forcing in LLMs via Reinforcement Learning for Mathematical Reasoning
- **Reinforcement learning (RL)** enhances **budget forcing** in Large Language Models (LLMs), improving mathematical reasoning accuracy while reducing token usage by over **40%** compared to supervised fine-tuning (SFT) alone.