Traditionally, evaluation of the agent monitoring mechanisms involves an attempt to game them, as it was my case when I attempted to test whether monitors would be able to identify the problem in the run and not in the beginning. The input prompt may look perfect until a certain issue...
# Trying to measure whether agent monitors actually catch problems during the run The usual way people score monitors on these agent systems ends up being easy to game. I found that out after trying to measure whether they actually catch problems during the run instead of just at the...
Rank-weighted FAISS voting: building adversarially robust semantic signals for AI agent governance Why single nearest-neighbour lookup fails in adversarial AI settings — and the 25-line fix Crossposted from Substack (https://ajaswanth.substack.com/p/rank-weighted-faiss-voting-building). Also on dev.to (https://dev.to/alkur_jaswanth_ce4f9fc791/how-to-build-a-22ms-agent-goal-drift-detector-5hjd) Summary: Standard FAISS nearest-neighbour lookup has a structural weakness in adversarial settings -a single boundary attack...