Tracked topic
Large language models are machine learning models trained to predict and generate text and other language-based outputs.
Vector Search with LLMs - Computerphile
Nicholas Carlini - Black-hat LLMs | [un]prompted 2026
AgentMerge: Enhancing Battlefield Issue Management with LLMs | AI and Games Conference 2024
A Compilation of Robots Falling Down at the DARPA Robotics Challenge
DARPA Robotics Challenge 2013: A Woodstock for Robots | The New York Times
By Julien Simon Chief Evangelist Hugging Face Large language models (LLMs) are taking the machine learning world by storm. Thanks to their Transformer architecture, LLMs have an uncanny ability to learn from…
…Multi-Agent Self-Evolution for LLM Reasoning (2026) Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs (2026) $\pi$-Play: Multi-Agent Self-Play via Privileged Self-Distillation…
…By transforming graph topology and node information into graph tokens , GLMs allow LLMs to jointly process structured graph inputs and textual instructions. Yet, it remains unclear how LLMs internally interpret these graph…
…Across 12 frontier LLMs, we find a pervasive optimism bias: under standard prompting, models frequently rate low-soundness proposals as sound, while aggressive prompting largely shifts errors from false positives to false…
LLMs and Almost Good Code
LLMs and Almost Good Code
Using LLMs to secure source code
Measuring LLMs' ability to develop exploits
Measuring LLMs' ability to develop exploits
…Efficient Generative LLM Inference with Asynchronous Two-level Sparse Attention (2026) Flux Attention: Context-Aware Hybrid Attention for Efficient LLMs Inference (2026) StructKV: Preserving the Structural Skeleton for Scalable Long-Context Inference…
…Related 7 things I wish I knew when I started self-hosting LLMs I've been self-hosting LLMs for quite a while now, and these are all of the things I…
…Storm Reply helps its customers deploy large language models (LLMs) and Generative AI solutions, among many other services. Storm Reply needed a cost-efficient, high-availability hosting environment to build its LLM…
…Building on this property, we train a lightweight Distiller at test time to predict deep-layer hidden representations of the LLM from its shallow-layer representations to model the LLM's depth…
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