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MatFormer: Nested Transformer for Elastic Inference
In a collaboration between Google DeepMind, University of Texas at Austin, University of Washington and Harvard published on December 2024 researchers introduce MatFormer, a novel elastic Transformer…
Apple's Speculative Streaming: Fast LLM Inference without Auxiliary Models
Speculative Streaming is a novel inference method designed to accelerate large language model (LLM) generation without the need for traditional auxiliary "draft" models. By integrating multi-stream…
Apple's Mirror Speculative Decoding: Parallel LLM Inference via Heterogeneous Accelerators
Apple researchers have introduced on December 2025 Mirror Speculative Decoding (Mirror-SD), an advanced inference algorithm designed to accelerate large language models by overcoming the sequential…
EAGLE: Evolution of Lossless Acceleration for LLM Inference
The provided documents describe the development and evolution of EAGLE, a high-efficiency framework designed to accelerate Large Language Model (LLM) inference through speculative sampling. By…
Fast Inference from Transformers via Speculative Decoding
These sources review historically speculative decoding, an innovative technique designed to accelerate Large Language Model (LLM) inference without reducing output quality. Large models are…
Building Production-Ready Speculative Decoding with TensorRT-LLM
This article outlines how Baseten optimized speculative decoding using the TensorRT-LLM framework to accelerate model inference. The authors detail overcoming technical hurdles such as inefficient…
QuantSpec: Hierarchical KV Cache for Self-Speculative Decoding
QuantSpec is a novel self-speculative decoding framework designed to accelerate the inference of Large Language Models, particularly in long-context scenarios. The system addresses memory and latency…
CXL-SpecKV: Bridging the LLM Memory Wall with Speculative FPGA Disaggregation
The researchers introduce CXL-SpecKV, a specialized architecture designed to overcome the memory bottlenecks of large language model serving by offloading key-value caches to remote memory. By…
Unified Latents (UL): How to train your latents
On the February 19, 2026 paper Google Deepmind introduces Unified Latents (UL), a novel framework for generative modeling that jointly trains an encoder, a diffusion prior, and a diffusion decoder.…
MagicDec: Breaking Latency-Throughput Tradeoffs via KV-Compressed Speculative Decoding
We review an April 3, 2025 research collaboration between CMU, Moffett AI and Together AI which introduces MagicDec, a new framework designed to accelerate the serving of long-context large language…
KV selection algorithms: static (SnapKV) Vs dynamic (PQCache)
We review three different papers which focus on different KV cache optimizations techniques using different KV selection algorithms types: static vs dynamic. StreamingLLM and SnapKV use static KV…
Adaptive Control for Batched Speculative Decoding in LLM Serving
We review two papers which examine the integration of speculative decoding and request batching to accelerate Large Language Model (LLM) inference. While both techniques aim to improve GPU hardware…
Optimizing Verification and Efficiency in Multi-Draft Speculative Decoding
These sources explore advanced techniques for accelerating **Large Language Model (LLM) inference** through **speculative decoding**, a process where smaller "draft" models predict tokens for a…
Evaluating Collective Behaviour of Hundreds of LLM Agents
This research collaboration between King’s College London, Google DeepMind on a research paper published on February 19, 2026 introduces a novel framework for evaluating the **collective behavior**…
Measuring LLM Reasoning Effort via Deep-Thinking Tokens
The February 12.2026 research from the University of Virginia and Google introduces the deep-thinking ratio (DTR), a novel metric designed to measure the true reasoning effort of large language…
Deep Learning Frameworks for Robust Quadrupedal Locomotion
These sources detail advanced **reinforcement learning frameworks** designed to improve how **quadruped robots** navigate difficult, real-world environments. The first source introduces a…
MEDUSA: Parallel Decoding Heads for Accelerated LLM Inference
MEDUSA is a novel framework introduced on June 24 2024 designed to accelerate Large Language Model (LLM) inference by overcoming the delays caused by sequential token generation. Instead of relying…
Taming the Long-Tail: Efficient Reasoning RL with Adaptive Drafters
On a paper published January 21, 2026 researchers from MIT and NVIDIA explain how they have have developed a new system called Taming the Long Tail (TLT) to solve computational inefficiencies in…
FastGRPO: Concurrency-Aware Speculative Decoding for Policy Optimization
The September 26 2025 research paper introduces FastGRPO, a high-efficiency framework designed to accelerate the training of large language models using Group Relative Policy Optimization. The…
Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding
Researchers introduced on May 2024 self-speculative decoding, a novel "plug-and-play" inference scheme designed to accelerate Large Language Models (LLMs) without requiring auxiliary models or extra…
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AI: post transformers has published 448 episodes since August 2025, covering topics in Technology.
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