acceleration

An article chronicling the timeline of AI model releases since GPT-2, highlighting the accelerating pace of model launches over time.

This article argues that AI creates a fast feedback loop where humans and machines mutually shape truth, accelerating consensus shifts and making truth increasingly synthetic and detached from reality.

This paper proposes Speculative Pipeline Decoding (SPD), a framework that uses pipeline parallelism within a single LLM to enable parallel token speculation, avoiding the latency bubbles and accuracy degradation of multi-token prediction in traditional speculative decoding.

Greg Brockman highlights how AI gives researchers like mathematician Terence Tao the freedom to explore bolder, more creative ideas in their work.

RT-Lynx proposes using activation sparsity instead of weight sparsity to accelerate diffusion models, achieving up to 1.55× linear-layer speedup while maintaining generation quality, and is accepted at ICML 2026.

Global warming has accelerated to twice the rate of previous decades, with a 98% confidence that the acceleration is due to climate change. If warming continues at this pace, the 1.5°C Paris Agreement limit could be breached by 2028.

Sam Altman shares three areas of excitement for AGI: accelerating research, companies, and personal goals. He also notes recent announcements including a unit distance result and $2M in OpenAI credits for Y Combinator startups.

This paper introduces CATS, a cascaded adaptive tree speculation framework designed to accelerate LLM inference on memory-constrained edge devices by optimizing memory usage while maintaining high token acceptance rates.

This paper introduces PARD-2, a dual-mode speculative decoding framework that uses target-aligned parallel draft models to accelerate LLM inference, achieving up to 6.94x lossless acceleration on Llama 3.1-8B.

This paper introduces DARE, a method for improving the inference efficiency of Diffusion Large Language Models by reusing cached key-value and output activations to reduce computational redundancy with negligible quality loss.

This paper introduces SpecBlock, a block-iterative speculative decoding method that combines path dependence with efficient drafting to accelerate LLM inference. It demonstrates improved speedup over existing methods like EAGLE-3 while maintaining lower drafting costs.

This paper introduces Normalizing Trajectory Models (NTM), a novel approach to diffusion-based generation that models reverse steps as conditional normalizing flows with exact likelihood training. NTM enables high-quality text-to-image generation in just four steps while retaining the likelihood framework, outperforming baselines on standard benchmarks.