x

75% of people globally want unbiased news (Pew Research) but are concerned they're not getting that.

Get balanced coverage with Symby. See how stories are covered across diverse outlets and spot when one side covers a story the other skips.

PODCAST

AI: post transformers

The transformer architecture revolutionized the world of Neural Networks. It was a springboard for what we know today as modern artificial intelligence. This podcast focuses on modern state of the art research paper reviews starting from the transformer and on.

technology

All Episodes

00:15:02

Mamba-360: State Space Models for Long Sequence Modeling

AI: post transformers ·

2025/11/19

The April 24, 2024 paper provides a comprehensive **survey of State Space Models (SSMs)**, outlining their evolution, fundamental mathematical principles, and recent advances in comparison to **Transformer architectures**. A major theme is the **trade-off between SSM efficiency and Transformer performance**, particularly concerning the quadratic computational complexity of Transformers in handling **long sequences**, which SSMs often address with **linear complexity**. The text categorizes SSMs into

en

00:10:52

Mixture-of-Depths: Dynamic Compute Allocation in...

AI: post transformers ·

2025/11/19

These April 4, 2024 Google Deepmind paper introduces the **Mixture-of-Depths (MoD)** transformer architecture, a method that improves efficiency by learning to dynamically allocate compute to only the necessary tokens within a sequence. This is achieved by setting a static capacity, C (or k), which **limits the total number of tokens** that can participate in the expensive self-attention and Multi-Layer Perceptron (MLP) computations at any given layer. The sources explain that this capacity

en

00:13:17

MLP Mixer Models

AI: post transformers ·

2025/11/19

These sources collectively explore the **MLP-Mixer architecture** and its numerous extensions across computer vision and audio tasks. The core concept of the Mixer is to separate and blend information—originally via **token-mixing** (spatial locations) and **channel-mixing** (features)—using only **Multi-Layer Perceptrons (MLPs)**, which is seen as a simpler alternative to CNNs and Vision Transformers. One source introduces **KAN-Mixers**, replacing standard MLPs with **Kolmogorov-Arnold Networks

en

00:34:49

Marin: Open LLM Optimization & Diagnostics

AI: post transformers ·

2025/11/19

Marin is an open lab dedicated to the transparent research and development of foundation models (FMs), focusing its core mission on identifying **how to build the best model with a fixed resource budget**, encompassing both compute and data. The lab employs a philosophy of complete transparency from day one, organizing its entire research and development process through **GitHub**. Every research effort, from wish list items to completed runs, is tracked by a dedicated **GitHub issue** that serves

en

00:35:04

vAttention Vs Strata: advanced GPU memory management

AI: post transformers ·

2025/11/19

We compare and contrast two advanced 2025 memory management and scheduling techniques for optimizing Large Language Model (LLM) serving throughput and latency:vAttention Vs StrataOne core innovation discussed is **vAttention**, which improves upon the popular PagedAttention method by leveraging CUDA Virtual Memory Management (VMM) APIs to keep the KV cache virtually contiguous, thereby simplifying **attention kernel portability** and reducing performance overheads associated with non-contiguous

en

00:10:15

AMD: Instella: Fully Open Language Models with...

AI: post transformers ·

2025/11/16

The November 13, 2025 paper by AMD introducs **Instella**, a new family of **fully open-source** three-billion-parameter large language models (LLMs) developed by AMD and powered by their Instinct MI300X GPUs. The central focus is on advancing transparency and reproducibility in LLMs by releasing not only the model weights but also the **complete training pipeline, datasets, and optimization details**. Instella achieves **state-of-the-art performance** among fully open models of its size, remaining

en

00:29:19

Mechanistic interpretability: Decoding the AI's Inner...

AI: post transformers ·

2025/11/15

Ten different sources are used in this episode which are excerpts from academic papers and technical reports focusing on mechanistic interpretability and sparse autoencoders in language models (LLMs) and vision-language models (VLMs). This episode explores the state-of-the-art in **Mechanistic Interpretability** (MI), focusing on how researchers are decomposing large language models (LLMs) and multimodal models (MLLMs) into understandable building blocks. A central theme is the power of **Sparse

en

00:14:59

Spectral Gap: Analysis of Attention Layers and Graph...

AI: post transformers ·

2025/11/10

We review two papers on Spectral Gap, one 2021 and another from 2025. The first source presents the **Spectral Attention Network (SAN)**, a novel Transformer-based architecture for graph neural networks that addresses the difficulty of defining positional encodings in graphs by leveraging the **full Laplacian spectrum** to learn node positions. This approach, which involves a **Learned Positional Encoding (LPE)**, enables the fully-connected Transformer to overcome limitations of traditional Graph

en

00:18:40

CARTRIDGE: Efficient In-Context Learning via...

AI: post transformers ·

2025/11/10

The June 13, 2025 joint collaboration between Stanford University, Caltech and University at Buffalo introduces a novel method called **CARTRIDGE** for efficiently handling long text corpora in large language models, addressing the high memory cost associated with standard **In-Context Learning (ICL)** and its required **KV cache**. A CARTRIDGE is a smaller, trained KV cache representation of a corpus, which is created offline using a technique termed **SELF-STUDY**. This training process involves

en

00:10:59

Metacognition and Skill Discovery in LLM Math Reasoning

AI: post transformers ·

2025/11/10

The May 20, 2024 academic paper explores the **metacognitive capabilities of Large Language Models (LLMs)**, specifically focusing on mathematical problem-solving. The core approach involves developing a method for a powerful LLM, such as GPT-4, to **identify and label mathematical questions with specific skills**, which are then organized into broader, interpretable categories. This process creates a **Skill Exemplar Repository** containing skill names matched with question-answer pairs

en

00:33:23

Context Distillation for Language Models

AI: post transformers ·

2025/11/10

These five papers from 2022 up to 2025 discuss various **knowledge distillation techniques** aimed at transferring the capabilities of large language models (LLMs) to smaller, more efficient models, often without the need for explicit context during inference. One paper introduces **Contextualization Distillation** (CD) for Knowledge Graph Completion (KGC), demonstrating that utilizing LLMs like PaLM2 to generate descriptive context for triplets significantly enhances the performance of smaller,

en

00:14:52

Tempo: SLO-Aware LLM Serving Maximizing Service Gain

AI: post transformers ·

2025/11/10

The April 24, 2025 academic paper introduces **Tempo**, a novel scheduling system designed to optimize Large Language Model (LLM) serving by addressing the wide variety of Service Level Objectives (**SLOs**) in modern LLM applications. The authors categorize requests into three types—**latency-sensitive**, **throughput-intensive**, and **collective requests**—each with distinct performance requirements that existing schedulers fail to manage effectively. Tempo maximizes "service gain" by allocating

en

00:14:26

LLM-AutoDiff: Auto-Differentiate Any LLM Workflow

AI: post transformers ·

2025/11/10

The January 30, 2025 paper introduces **LLM-AutoDiff**, a novel framework for **Automatic Prompt Engineering (APE)** that allows for the optimization of complex Large Language Model (LLM) workflows. This framework models an entire LLM application—including multiple LLM calls, functional components like retrievers, and cyclical operations—as a **directed, auto-differentiable graph**. By treating textual inputs as trainable parameters, LLM-AutoDiff uses a separate "backward engine" LLM to generate

en

00:19:58

Confucius: Intent-Driven Network Management with...

AI: post transformers ·

2025/11/10

The August 27, 2025 paper introduces **Confucius**, a novel multi-agent Large Language Model (LLM) framework developed by Meta for **intent-driven network management** in hyper-scale environments. The framework models complex management tasks as **directed acyclic graphs (DAGs)** and integrates LLMs with existing tools using domain-specific languages (**DSLs**) to enhance planning and execution. Confucius leverages **Retrieval-Augmented Generation (RAG)** for long-term memory and employs specialized

en

00:16:45

SYMPHONY: Memory Management for LLM Multi-Turn Inference

AI: post transformers ·

2025/11/10

The 2024 paper introduces **SYMPHONY**, a novel system designed to improve memory management and scheduling for **Large Language Model (LLM) inference workloads**, particularly those involving multi-turn interactions like chatbots and AI agents. The authors, researchers from the University of Texas-Austin and the University of Wisconsin-Madison, explain that existing LLM serving engines either waste computation by **recomputing Key-Value (K,V) caches** or suffer from **load imbalance** by offloading

en

00:15:54

DSPy and TextGrad: Compiling Language Model Systems

AI: post transformers ·

2025/11/10

These two academic papers introduce novel programming models aimed at systematically optimizing complex AI systems, particularly those built using Large Language Models (LLMs). The first source presents **DSPy**, a framework that abstracts traditional, hard-coded LLM pipelines into parameterized, declarative modules that can be automatically optimized using a compiler and **teleprompters**, demonstrating superior performance compared to hand-crafted prompts on tasks like math word problems. The

en

00:18:45

Vidur: Simulation for Efficient LLM Inference Deployment

AI: post transformers ·

2025/11/10

The May 21, 2024 paper introduces **Vidur**, a new, high-fidelity simulation framework designed to optimize the deployment and performance of Large Language Model (LLM) inference. The authors explain that experimentally optimizing LLM deployment is **prohibitively expensive**, requiring exploration of a vast configuration space of system parameters like parallelization strategies and batching techniques, which can cost hundreds of thousands of dollars and thousands of GPU hours. Vidur addresses this

en

00:18:10

Continuous Autoregressive Language Models: CALM

AI: post transformers ·

2025/11/10

The October 31, 2025 paper introduces **Continuous Autoregressive Language Models (CALM)**, a new paradigm designed to overcome the efficiency bottleneck of traditional Large Language Models (LLMs) by shifting from discrete token-by-token prediction to **continuous next-vector prediction**. This approach compresses a chunk of multiple tokens into a single continuous vector using a **high-fidelity autoencoder**, thereby reducing the number of generative steps and significantly improving the

en

00:15:52

A Framework for LLM Application Safety Evaluation

AI: post transformers ·

2025/11/10

The July 13, 2025 paper " Measuring What Matters: A Framework for Evaluating Safety Risks in Real-World LLM Applications" introduces a practical **framework for evaluating safety risks** in real-world Large Language Model (LLM) applications, arguing that current methods focusing only on foundation models are inadequate. This framework consists of two main parts: **principles for developing customized safety risk taxonomies** and **practices for evaluating these risks** within the application itself,

en

00:35:23

Doubly Stochastic Attention for Transformers

AI: post transformers ·

2025/11/10

The four papers we review dated from 1967 up to two papers in 2025 collectively discuss the mathematical properties and deep learning applications of **doubly stochastic matrices**, which are nonnegative matrices whose rows and columns sum to one. One paper, "Concerning Nonnegative Matrices and Doubly Stochastic Matrices," provides the **foundational mathematical theory** regarding the convergence of iterative row and column scaling (known as the Sinkhorn algorithm) to a unique doubly stochastic

en

340 results

Similar Podcasts

Lead With AI

Tamara Nall

Linux in the Ham Shack

Black Sparrow Media

Data Engineering Podcast

Tobias Macey

Eye On A.I.

Craig S. Smith

Reflections

Livewire Labs