composable simulator infrastructure
A composable simulator infrastructure is a toolkit you can mix and match like Lego blocks to build simulations for different scenarios. Instead of writing a whole new simulator from scratch for each AI system you want to test, you use pre-built, interchangeable components that snap together. This lets you quickly test new features, like a robot that works across your phone and laptop, without rebuilding everything, making the simulators faster to create and easier to update as the technology changes.
The papers all build simulators using this modular approach: one creates a framework to safely test AI agents by combining risk taxonomies and executable test cases, another simulates agents coordinating across different operating systems, and a third uses a coding agent to automatically extend a simulator's components. They find that without composable design, simulators are slow to update and miss critical failures, like agents failing when they must juggle tasks across multiple devices or handle advanced attacks. By making infrastructure composable, these tools reveal dangerous gaps that simpler, fixed simulators overlook.
- Papers
- 3
- Released code
- 1
- First seen
- July 2026
- Latest
- Sept 2026
Start here
Two papers to read first, in the order they came out- 1Automated safety testing reveals 93.9% attack success rate across four agent frameworks
LLM agents can be systematically safety tested using automated combinatorial risk exploration and evidence-grounded verification.
July 2026 - 2Cross-device workflows expose major weaknesses in today’s GUI agents
JarvisGUI reveals that open-source GUI agents fail at cross-device tasks due to poor state-transfer and multi-platform reasoning.
Sept 2026
How fast it is moving
Often studied with
agent-driven simulationAutonomous agents automatically generate, control, or extend simulations to improve accuracy, interactivity, or performance.1 togetheragentic evaluationMeasuring AI agents' performance in complex tasks, highlighting failures in planning, monitoring, and real1 togetherai controlA system for monitoring and regulating AI agent behavior to prevent covert or harmful actions during tasks.1 togetherThe papers
Most central to this idea first, not most recent.
- Chinese Techcs.AIcode
Automated safety testing reveals 93.9% attack success rate across four agent frameworks
LLM agents can be systematically safety tested using automated combinatorial risk exploration and evidence-grounded verification.
AntGroup, Zhejiang University · July 2026
- Top Universitycs.AI
Cross-device workflows expose major weaknesses in today’s GUI agents
JarvisGUI reveals that open-source GUI agents fail at cross-device tasks due to poor state-transfer and multi-platform reasoning.
Beihang University, Beijing Institute of Technology · Sept 2026
- Top Universitycs.AR
Coding agents extend LLM serving simulators with greater fidelity and speed
Simthesizer uses an agent to automatically extend LLM serving simulators from natural language requests, achieving higher accuracy and speed.
KAIST · Aug 2026
Concepts are extracted from each paper and reused across the corpus, so this page grows on its own as the desk reads.