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A lightweight module injects temporal memory into robotic policies, enabling them to distinguish between identical-looking states at different points in a repeated motion.
BenchShield is presented, a model-backed instrumentation layer for reward integrity in LLM-agent evaluation that grounds detection in a finite lifecycle model of an evaluation's reward-relevant events and achieves 96% accuracy in detecting reward hacking from infrastructure-side evidence.