TMDPs for adversarial AI, AI tunes quantum devices, digital twin robot driving

New frameworks and methods are being developed for AI applications in scientific research. One approach, Threatened Markov Decision Processes (TMDPs), helps reinforcement learning agents operate in adversarial environments where opponents interfere with rewards. Other developments include an AI-driven method for autonomously tuning quantum devices and a digital-twin framework for teaching human-like driving behaviors to robots in virtual factories. Additionally, new platforms aim to automate complex tasks such as chemical synthesis and the assembly of optical experiments.

Researchers introduced Threatened Markov Decision Processes (TMDPs), a framework for training AI agents in reinforcement learning environments with adversaries. This framework supports agents against opponents attempting to manipulate reward processes. Experimental results from learning approaches derived from TMDPs demonstrate the utility of agents understanding adversarial actions.
An AI-driven method was developed for autonomously tuning quantum devices. This approach used cross-platform transfer learning to identify specific operating conditions, called "sweet spots," for emergent Majorana zero modes. A convolutional neural network predicted physical rates, enabling automated adjustment of device parameters and rapid sweet spot location.
A study proposed a simulation-based digital-twin imitation learning framework for autonomous mobile robots. This framework enables learning human-like driving behaviors within virtual factory environments, using a digital twin to simulate manufacturing settings.
A paper introduced the Reinforcement Learning Chemistry Laboratory (RLCL), a conceptual framework for autonomous chemical synthesis and molecular discovery. RLCL adapts AlphaZero principles, treating synthesis as a Markov Decision Process, and integrates Monte Carlo Tree Search with a policy-value network to evaluate experimental paths. It uses a multi-objective reward function to guide discovery.
Researchers developed a platform that automates free-space optical experiments. It integrates generative AI, computer vision, and precision robotics to automate experiment design, assembly, and alignment. The system translates user goals into optical configurations, assembles components with submillimeter accuracy, performs micrometer-scale fine alignment, and executes measurements.
An evidence-based psychotherapy with AI framework was introduced. This framework guides the development of clinical AI applications, outlining principles such as psychodiagnostic assessment, longitudinal case conceptualization, intervention planning, and validation. It also presents technical questions for evaluating clinical large language models and AIs.
Research provided a methodology for pre-training Physical AI within simulated environments. This approach allows a tunable balance between task efficiency and human-like behavior, intended for use prior to AI field deployment.
A survey indicates Explainable AI (XAI) techniques increasingly utilize Large Language Models (LLMs) and Vision-Language Models (VLMs. These models help automate and improve the interpretability of other machine learning models by providing high-level, semantically meaningful explanations.
Current AI technologies do not produce sustained clinical change in psychological treatment.
The described digital twin system for robots uses proximal policy optimization and integrates generative adversarial imitation learning.
Large Foundation Models have expanded the potential of Human-AI Collaboration.
A framework named Deep-PrAE improves rare-event simulation for black box systems using neural networks.
A survey describes methods in explainable AI.
Software ecosystems for laboratory automation increasingly embed machine learning.

Sources

Opponent aware reinforcement learning
In certain reinforcement learning (RL) scenarios there are adversaries trying to interfere with the underlying reward process for their own benefit. We introduce Threatened Markov Decision Processes (TMDPs) as a framework to support an agent against potential opponents in an RL context as well as schemes resulting in novel learning approaches to deal with TMDPs. After introducing our framework and deriving theoretical results, empirical evidence is given via extensive experiments, showing the importance for an RL agent of acknowledging adversarial awareness.
Cross-Platform Autonomous Control of Minimal Kitaev Chains
Contemporary quantum devices are reaching new limits in size and complexity, allowing for the experimental exploration of emergent quantum modes. However, this increased complexity introduces significant challenges in device tuning and control. Here, we demonstrate autonomous tuning of emergent Poor Man’s Majorana zero modes in a minimal realization of a Kitaev chain. We achieve this task using cross-platform transfer learning. First, we train a tuning model on a theory model. Next, we retrain it using a Kitaev chain realization in a two-dimensional electron gas. Finally, we apply this model…
Digital Twin-Based Imitation Learning for Human-Like and Efficient Driving of Mobile Robots in Virtual Factory Environments
Abstract In smart manufacturing and flexible production systems, autonomous mobile robots must complete logistics tasks efficiently while exhibiting driving behaviors that human operators can interpret and accept. This study proposes a simulation-based digital-twin imitation learning framework for learning human-like driving patterns in virtual factory environments. The proposed system is built on the proximal policy optimization algorithm and integrates generative adversarial imitation learning to establish a dual-reward structure that combines extrinsic rewards with demonstration-derived…
Reinforcement Learning Chemistry Laboratory (RLCL): An AlphaZero-Inspired Framework for Autonomous Chemical Synthesis and Discovery
This paper introduces the Reinforcement Learning Chemistry Laboratory (RLCL), a conceptual and theoretical framework that adapts the core principles of AlphaZero to the chemical domain for autonomous synthesis and molecular discovery. Traditional artificial intelligence in chemistry relies heavily on static dataset prediction, lacking strategic foresight and active exploratory capability. RLCL reformulates chemical synthesis as a Markov Decision Process defined by state spaces S, action spaces A, transition dynamics P, and a comprehensive reward function R. By integrating Monte Carlo Tree…
AI-driven robotics for optics
Optical experiments are essential across science and technology, yet their design, assembly, and alignment remain predominantly manual, limiting throughput, reproducibility, and scalability. Automating such experiments is challenging because of stringent precision requirements and the diversity of setups in typical real-world optical laboratory environments. Here, we introduce a platform that integrates generative artificial intelligence, computer vision, and precision robotics to automate free-space optical experiments. The system translates user-defined goals into valid optical…
A framework for evidence-based psychotherapy with AI (EBP-AI).
Artificial intelligence (AI) systems and large language models offer substantial potential to augment or even fundamentally change elements of psychological assessment and treatment. However, current AI technologies have yet to demonstrate the capacity to effect meaningful and sustained clinical change. This gap reflects both the limited integration of clinical science knowledge into language models and applications built using them, as well as the mismatch between the brief, minutes-long nature of most AI interactions and the months-long course of most evidence-based treatments. Here we…
Explainable artificial intelligence (XAI): From inherent explainability to large language models
Artificial Intelligence (AI) has continued to achieve tremendous success in recent times. However, the decision logic of these frameworks is often not transparent, making it difficult for stakeholders to understand, interpret or explain their behavior. This limitation hinders trust in machine learning systems and causes a general reluctance towards their adoption in practical applications, particularly in mission-critical domains like healthcare and autonomous driving. Explainable AI (XAI) techniques facilitate the explainability or interpretability of machine learning models, enabling users…

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Full transcript

An AI agent can be taught to operate effectively, but what happens when an adversary is deliberately trying to make it fail? Addressing this problem is a focus for developers, and a topic we're examining on Agents in Research. Here are the latest approaches. A new conceptual framework has been introduced for autonomous chemical synthesis, called the Reinforcement Learning Chemistry Laboratory, or RLCL. So it’s taking principles from game-playing AI, like AlphaZero, and applying them to the chemistry domain? Exactly. It reframes chemical synthesis as a Markov Decision Process. It uses Monte Carlo Tree Search combined with a policy-value network to figure out the best experimental paths to take. And the goal isn't just to find a path, but to discover new things. The framework uses a multi-objective reward function to guide the system toward finding new chemical entities. Moving from chemistry to physics, another area of lab automation is using a platform with generative AI, computer vision, and robotics for optical experiments. What does that actually automate? The whole process? Pretty close. The system takes high-level goals—what the researcher wants to achieve—and translates them into the required optical configurations. It then assembles the physical components. And it does this with submillimeter accuracy, and then performs fine-tuning down to the micrometer scale. That's a level of precision that goes beyond just setting up the equipment. Right. After that alignment, it can execute measurements like beam characterization and spectroscopy on its own. It's a foundation for reconfigurable optics automation. Speaking of automation, we followed a story last month about tuning quantum devices. There's been a development there. Yes, a new AI-driven method has been demonstrated for doing this autonomously. It uses cross-platform transfer learning to identify specific operating conditions—what they call "sweet spots"—for new quantum modes. How does it find them? Is it just trial and error? It's more guided than that. A convolutional neural network predicts physical rates based on the experimental data, which allows for automated adjustment of the device's parameters. And it successfully finds these sweet spots within minutes. The speed is the key factor there. Shifting from physical labs to learning theory, a framework called Threatened Markov Decision Processes has been introduced. This is for reinforcement learning agents that have to operate against an adversary. So this isn't just dealing with a noisy or unpredictable environment. It's modeling a scenario where an opponent is actively trying to interfere with the reward process. Correct. And the learning approaches that come from this framework provide evidence that an agent's awareness of an adversary is valuable. In a related area, a study has proposed a simulation-based digital-twin framework for teaching autonomous mobile robots. And the goal here is to get them to exhibit human-like driving behaviors inside virtual factory environments. The digital twin simulates these complex manufacturing settings for training. This lets them pre-train the AI, and specifically, it provides a way to tune the balance between task efficiency and behaving in a way that's predictable to humans, all before it's deployed. Right. The system itself is built on proximal policy optimization and integrates generative adversarial imitation learning. Moving to a completely different domain, a new framework aims to guide the development of AI for evidence-based psychotherapy. It outlines core principles for building clinical AI applications, including psychodiagnostic assessment, longitudinal case conceptualization, and rigorous validation. It also presents a set of technical questions for evaluating large language models that are intended for clinical use. Which brings up the topic of model interpretation. A recent survey on Explainable AI, or XAI, points out that these techniques are increasingly using Large Language Models and Vision-Language Models themselves. So, using AI to explain AI? To automate the process of getting high-level, meaningful explanations for why another model made a particular decision. Exactly. Just a few other developments to note as well. One is that current AI technologies still lack the ability to produce sustained clinical change in psychological treatment. And on a more technical note, there's a new framework named Deep-PrAE that improves rare-event simulation for black box systems using neural networks. New frameworks and methods continue to emerge. We'll track what comes next. From Agents in Research, thanks for listening.