1 citations · 1 across the 7 of their papers we have counts for
7 papers
Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling
Fan Feng, Yujia Zheng, Minghao Fu +5
Learning and planning in imagination using world models provides an effective paradigm for training agents for decision-making. However, existing approaches often rely on high-dime…
World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry
Yuejiang Liu, Fan Feng, Lingjing Kong +6
General-purpose world models promise scalable policy evaluation, optimization, and planning, yet achieving the required level of robustness remains challenging. Unlike policy learn…
Agentic Forecasting with Structured Linguistic Beliefs
Kevin Murphy
We propose the Bayesian Linguistic Forecaster (BLF), which is a search-based ReAct style agent for (binary) forecasting that augments its context with a linguistic 'belief state',…
AutoHarness: improving LLM agents by automatically synthesizing a code harness
Xinghua Lou, Miguel Lázaro-Gredilla, Antoine Dedieu +3
Despite significant strides in language models in the last few years, when used as agents, such models often try to perform actions that are not just suboptimal for a given state,…
Code World Models for General Game Playing
Wolfgang Lehrach, Daniel Hennes, Miguel Lazaro-Gredilla +13
Large Language Models (LLMs) reasoning abilities are increasingly being applied to classical board and card games, but the dominant approach -- involving prompting for direct move…
Improving Transformer World Models for Data-Efficient RL
Antoine Dedieu, Joseph Ortiz, Xinghua Lou +5
We present three improvements to the standard model-based RL paradigm based on transformers: (a) "Dyna with warmup", which trains the policy on real and imaginary data, but only st…
Model Predictive Simulation Using Structured Graphical Models and Transformers
Xinghua Lou, Meet Dave, Shrinu Kushagra +2
We propose an approach to simulating trajectories of multiple interacting agents (road users) based on transformers and probabilistic graphical models (PGMs), and apply it to the W…