arXiv:2606.26094v1 Announce Type: new Abstract: For most of scientific history, researchers studying behavior could only infer hidden mechanisms from outward actions: an inverse problem that becomes more tractable when observation is augmented by targeted intervention. We pose a computational analogue: given only behavioral traces of an agent in a game environment, can a learner reconstruct the underlying decision program as executable code, and how much does this reconstruction improve with the ability to design controlled experiments? We introduce RevengeBench, a benchmark of 75 LLM generate
The proliferation of complex AI systems, particularly large language models, necessitates advanced methods to understand and reverse-engineer their decision-making processes.
Understanding and reconstructing AI decision programs is crucial for debugging, auditing, security, and ensuring predictability and safety, especially as autonomous systems become more integrated into critical infrastructure.
The ability to reverse-engineer AI policies from behavioral experiments could lead to more robust, transparent, and controllable AI systems, moving beyond black-box analysis.
- · AI Safety Researchers
- · AI Developers
- · Cybersecurity Firms
- · Auditing and Compliance Sector
- · Malicious AI Developers
- · Proprietary Black-Box AI Models
This research provides a new toolset for analyzing and understanding the internal logic of AI agents.
Improved transparency and interpretability could accelerate the development and deployment of trusted AI systems across various industries.
The methodology could be adapted to infer human decision-making processes from behavioral data, bridging AI research with cognitive science.
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Read at arXiv cs.LG