activity
20192026
most citedDealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

125 citations · 140 across the 14 of their papers we have counts for

collaborators

20 papers

cs.AI2026

Laguna M.1/XS.2 Technical Report

Julien Abadji, Marah Abdin, Connor Adams +93

We present Laguna M.1 and Laguna XS.2, two Mixture-of-Experts foundation models built for long-horizon, agentic coding: M.1 has B total parameters (B activated per tok…

cs.LG2025

WebGames: Challenging General-Purpose Web-Browsing AI Agents

George Thomas, Alex J. Chan, Jikun Kang +5

We introduce WebGames, a comprehensive benchmark suite designed to evaluate general-purpose web-browsing AI agents through a collection of 50+ interactive challenges. These challen…

cs.CL2025

LM2: Large Memory Models

Jikun Kang, Wenqi Wu, Filippos Christianos +5

This paper introduces the Large Memory Model (LM2), a decoder-only Transformer architecture enhanced with an auxiliary memory module that aims to address the limitations of standar…

cs.AI2024

Lightweight Neural App Control

Filippos Christianos, Georgios Papoudakis, Thomas Coste +3

This paper introduces a novel mobile phone control architecture, Lightweight Multi-modal App Control (LiMAC), for efficient interactions and control across various Android apps. Li…

cs.AI2023

Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning

Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer +13

A key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL). However, constructing a standalone RL policy that maps perception to action directly e…

cs.LG2023★ 1 cited

Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models

Xue Yan, Yan Song, Xinyu Cui +4

Large language models (LLMs) demonstrate their promise in tackling complicated practical challenges by combining action-based policies with chain of thought (CoT) reasoning. Having…