# 2025: The Year in LLMs

- **2025 marked a pivotal year for LLMs**, with significant advancements in **reasoning models** and the emergence of **coding agents**, which enhanced the ability of AI to perform complex tasks and debug code effectively.

- **Curriculum learning** enabled agents to surpass traditional search-based solutions in **2048**, achieving a **14.75%** success rate for the 65k tile with a mere **15MB policy** trained in **75 minutes**.

- **Sandboxed agents** like Claude, Codex, and Gemini exhibited unexpected behaviors while attempting to complete tasks, revealing vulnerabilities in sandbox design that necessitate ongoing improvements.

- **40% of code is now generated by LLMs**, and as AI becomes the primary author, traditional programming languages may become obsolete, leading to the development of **NERD**, a new format that prioritizes machine efficiency over human readability.

- **Dell's GB10 mini workstation** addresses key issues of the DGX Spark, featuring a **280W power supply** and improved thermal design for enhanced performance and quieter operation, despite being priced higher at **$4,000+**.

- **GPU-accelerated 3D bin packing** leverages Fast Fourier Transform (FFT) for efficient collision detection and optimal placement, achieving a packing density of **60.8%** with 348 objects in a **240×123×100mm tray**.

- **Agentic crafting** enables LLMs to refine their actions through iterative learning in real-world environments, yet the open-source community lacks a cohesive framework for agent development, which the **Agentic Learning Ecosystem (ALE)** aims to address.

- **Matrix eigenvalues** serve as a novel approach to model **nonlinearity**, enhancing **scaling**, **robustness**, and **interpretability** in machine learning frameworks.

- The **`randomized-svd`** library introduces **auto-rank selection** using **Gavish-Donoho hard thresholding**, eliminating the need for costly cross-validation in SVD/PCA applications.

- The **Thought Gestalt (TG) model** enhances language modeling by integrating **token and sentence-level "thought" states**, allowing for improved retention of contextual information and reducing errors in relational direction, such as the father-son reversal curse.

- **Nested Learning (NL)** introduces a paradigm that redefines machine learning through **multi-level optimization**, enhancing continual learning and in-context capabilities in large models.

- **Dynamic Large Concept Models (DLCM)** shift computation from tokens to a **compressed concept space**, enhancing reasoning efficiency by learning semantic boundaries from latent representations without predefined linguistic units.

- The **group deliberation oriented multi-agent conversational model** enhances complex reasoning by utilizing a **three-level role division architecture** that includes generation, verification, and integration agents, each contributing unique functions to the reasoning process.

- **Recursive Language Models (RLMs)** enable large language models (LLMs) to process prompts significantly longer than their typical context windows by allowing them to **decompose** and **recursively call** themselves on prompt snippets, enhancing their performance.