AI Safety With a Guarantee: How the BEAVER Framework Delivers Provable LLM Safety

BEAVER: Adding a “Mathematical Guarantee” to AI Safety Imagine this: you ask a large language model a question, and it could generate ten different answers. How do you precisely know its “confidence” in giving the correct one? The BEAVER framework provides, for the first time, a deterministic, mathematical answer to this critical question. Here’s a tangible scenario: you instruct an LLM to generate a safe Bash command to list a directory. Most of the time, it might output ls -al. But is there a possibility, however small, that it could output a dangerous command like rm -rf /home? Before deploying …