Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 11·4 min read
ISE: An Execution-Grounded Recipe for Multi-Turn OS-Agent Trajectories
TL;DR
Researchers identified a gap in LLM evaluation benchmarks. They built a synthetic dataset with 10k adversarial prompts targeting reasoning failures.
✦ Why It Matters
Use this benchmark to audit LLM robustness before deploying in production reasoning pipelines.
Key Takeaways
- Utilize the ISE framework for training OS agents.
- Leverage the generated dataset for fine-tuning models.
- Implement multi-turn simulations to improve agent performance.
- Explore the live execution environment for authentic interactions.
How It Works
ISE operates in three stages: first, it generates structured user intents using a 4D framework; second, it simulates multi-turn interactions with a user simulator; and third, it executes tool calls in a live OS environment to create realistic dialogue dynamics.