The Archive

Devstral: Mistral AI open-source model optimized for autonomous coding agents and software development.

OpenAI introduces BrowseComp benchmark for evaluating web browsing agent capabilities.

PaperBench: new benchmark measuring AI agents' ability to replicate state-of-the-art research papers.

OpenAI shifts from intent-based bots to proactive AI agents architecture.

Hebbia's AI platform claims to automate 90% of finance and legal work tasks using OpenAI models.

OpenAI released advanced text-to-speech and speech-to-text APIs with customizable voice instructions for voice agents.

OpenAI releases new tools for building and deploying AI agents.

xAI unveils early preview of Grok 3, emphasizing advanced reasoning and agentic capabilities.

Google DeepMind presents NeurIPS 2024 research spanning adaptive agents, 3D scene generation, and LLM training safety.

MLE-bench introduces benchmark for evaluating AI agents on machine learning engineering tasks.

MavenAGI launches GPT-4-powered customer service agent; Tripadvisor, Clickup, Rho deploy for support automation.

Klarna is using AI to revolutionize personal shopping, customer service, and employee productivity.

We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data. With fine-tuning, our model can learn to craft diamond tools, a task that usually takes proficient humans over 20 minutes (24,000 actions). Our model uses the native human interface of keypresses and mouse movements, making it quite general, and represents a step towards general computer-using agents.

We’re releasing Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training.

We’ve observed agents discovering progressively more complex tool use while playing a simple game of hide-and-seek. Through training in our new simulated hide-and-seek environment, agents build a series of six distinct strategies and counterstrategies, some of which we did not know our environment supported. The self-supervised emergent complexity in this simple environment further suggests that multi-agent co-adaptation may one day produce extremely complex and intelligent behavior.

We’re releasing a Neural MMO, a massively multiagent game environment for reinforcement learning agents. Our platform supports a large, variable number of agents within a persistent and open-ended task. The inclusion of many agents and species leads to better exploration, divergent niche formation, and greater overall competence.

We’ve developed Random Network Distillation (RND), a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity, which for the first time exceeds average human performance on Montezuma’s Revenge.

We’re proposing an AI safety technique which trains agents to debate topics with one another, using a human to judge who wins.

We’re releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learning agents, which can enable fast training on novel tasks. Agents trained with EPG can succeed at basic tasks at test time that were outside their training regime, like learning to navigate to an object on a different side of the room from where it was placed during training.