# Nov 1, 2024

### Physical Intelligence's first generalist robotic model

- **π0** is a groundbreaking generalist robot foundation model designed to enable robots to perform a wide range of tasks by learning from embodied experiences, akin to how large language models (LLMs) operate with text and images.

### TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters

- **TokenFormer** introduces a **natively scalable architecture** that enhances flexibility by treating model parameters as tokens, allowing for efficient scaling without retraining from scratch.

### Oasis: A Universe in a Transformer

- **Oasis** is the first **playable, real-time, open-world AI model**, generating gameplay through user input without a traditional game engine, showcasing the potential of fast transformer inference for generative video.

### Using Large Language Models to Catch Vulnerabilities

- **Project Zero's Big Sleep** has successfully identified a **previously unknown exploitable stack buffer underflow** in SQLite, showcasing the potential of large language models in real-world vulnerability detection.

### [R] Data Poisoning in LLMs: Jailbreak-Tuning and Scaling Laws

- **Data poisoning** can significantly compromise AI models, as demonstrated by the **jailbreak-tuning method**, which allows GPT-4o to answer harmful queries effectively, raising concerns as model sizes increase.

### The National EUV Accelerator comes to Albany

- The **NSTC EUV Accelerator** will be established at the **Albany NanoTech Complex**, enhancing North America's semiconductor research and manufacturing capabilities, crucial for maintaining technological leadership.

### [D] Neural networks based on the spectral theorem for real symmetric matrices?

- **Neural networks inspired by physical interactions** utilize a weight matrix defined by a force-like inverse-square law, paralleling the **Coulomb matrix** in quantum chemistry, suggesting a novel approach to neural architecture design.

### Ethernet at NANOG 92

- **Ethernet technology is evolving rapidly**, with the IEEE's P802.3dj project aiming to standardize 200Gbps Ethernet lanes, paving the way for future capacities of 400GbE, 800GbE, and 1.6TbE, driven by advancements in silicon chip technology.

### DAWN: Designing Distributed Agents in a Worldwide Network

- **DAWN** introduces a **versatile framework** that integrates **LLM-based agents** with traditional software systems, enabling the development of agentic applications for diverse tasks like coding and web browsing.

### BitStack: Fine-Grained Size Control for Compressed Large Language Models in Variable Memory Environments

- **BitStack** introduces a **training-free weight compression** method that allows for **megabyte-level trade-offs** between memory usage and model performance, addressing the challenges of deploying large language models in **variable memory environments**.

### [D] Fine-tuning DINOv2 for semantic segmentation

- **Fine-tuning DINOv2's ViT-B 14** for semantic segmentation on the Cityscapes dataset revealed that an **ERFNet-based decoder** outperformed a **UperNet decoder** with scores of **76.06 mIoU** versus **74.22 mIoU**, challenging assumptions about multi-scale approaches.

### [R] Our results experimenting with different training objectives for an AI evaluator

- **Preference optimisation techniques** like DPO and RPO outperform traditional supervised fine-tuning (SFT) in training LLM-as-a-judge models, suggesting a shift in effective training objectives for AI evaluators.

### [R] Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection (ICLR)

- The paper introduces a **novel method for supervised feature selection** that enhances task-driven image channel selection, significantly impacting MRI acquisition times and multispectral image reconstruction.

### Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs

- **Plan-on-Graph (PoG)** introduces a **self-correcting adaptive planning** approach for **KG-augmented LLMs**, enhancing their ability to navigate knowledge graphs by decomposing questions into sub-objectives and iteratively refining reasoning paths.

### Introducing DRIFT Search: Combining global and local search methods to improve quality and efficiency

- **DRIFT Search** enhances local query responses by integrating community insights, allowing for a broader range of facts and more nuanced answers compared to traditional methods.