The Missing Layer: Specification Infrastructure for AI Oversight

arXiv:2607.24866 · cs.CR · Submitted 2026-07-26 · Read on arXiv

Satyam Kumar, Saurabh Jha

cs.CR

Submitted: 2026-07-26

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Alignment — 21× in this paper · explained in 1 episode
• Interpretability — 17× in this paper · explained in 15 episodes
• Accountability — 14× in this paper · explained in 2 episodes
• Robustness — 13× in this paper · explained in 5 episodes
• Formal Methods — 11× in this paper · explained in 3 episodes
• Trace — 7× in this paper · explained in 2 episodes
• Adversarial Defense — 6× in this paper · explained in 1 episode
• Open Policy Agent — 6× in this paper
• Verification — 5× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Agentic System — 2× in this paper · explained in 2 episodes
• Mechanistic interpretability — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Formal Verification — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes

Sources

• Constitutional AI: Harmlessness from AI Feedback
• The Capacity for Moral Self-Correction in Large Language Models
• Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
• Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
• Frontier Models are Capable of In-context Scheming

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