Finally, PDE-Refiner's connection to diffusion models enables an accurate and efficient assessment of the model's predictive uncertainty, allowing us to estimate when the surrogate becomes inaccurate.

Simulation agents are controlled objects that perform realistic behaviors in a virtual world.

The June 2025 briefing to Wes Streeting (2nd left) was released under the Freedom of Information Act. The June 2025 briefing to Wes Streeting (2nd left) was released under the Freedom of Information Act. NHS deal with AI firm Palantir called into question after officials' concerns revealed Health officials fear Palantir's reputation will hinder the delivery of a "vital" £330m NHS contract, according to briefings seen by the Guardian, sparking fresh calls for the deal to be scrapped. In 2023, ministers selected Palantir, a US surveillance technology company that also works for the Israeli military and Donald Trump's ICE operation, to build an AI-enabled data platform to connect disparate health information across the NHS . Now it has emerged that after Keir Starmer demanded faster deployment, Whitehall officials privately warned that the public perception of Palantir would limit its rollout, meaning the contract would not offer value for money.

Different inner loss and outer loss other than what we used can be instantiated in the subclass.

In recent years, model-free reinforcement learning (MFRL) has achieved tremendous success on a wide range of simulated domains, e .

We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain. To this end, we introduce JADAI, a framework that jointly amortizes Bayesian adaptive design and inference by training a policy, a history network, and an inference network end-to-end. The networks minimize a generic loss that aggregates incremental reductions in posterior error along experimental sequences. Inference networks are instantiated with diffusion-based posterior estimators that can approximate high-dimensional and multimodal posteriors at every experimental step. Across standard adaptive design benchmarks, JADAI achieves superior or competitive performance.

Reinforcement learning (RL) traditionally trains agents using interaction data, which limits their capabilities to the scope of the training data. To create more knowledgeable agents, leveraging knowledge from large language models (LLMs) has shown a promising way. Despite various attempts to combine LLMs with RL, there is commonly a semantic gap between action signals and LLM tokens, which hinders their integration. This paper introduces a novel approach, KALM (Knowledgeable Agents from Language Model Rollouts), to learn knowledgeable agents by bridging this gap. KALM extracts knowledge from LLMs in the form of imaginary rollouts, which agents can learn through offline RL. To overcome the limitation that LLMs are inherently text-based and may be incompatible with numerical environmental data, KALM fine-tunes the LLM to perform bidirectional translation between textual goals and rollouts. This process enables the LLM to understand the environment better, facilitating the generation of meaningful rollouts. Experiments on robotic manipulation tasks demonstrate that KALM allows agents to rephrase complex goals and tackle novel tasks requiring new optimal behaviors. KALM achieves a 46% success rate in completing 1400 various novel goals, significantly outperforming the 26% success rate of baseline methods.