Four Recent Papers on Reliable LLM Agents: Verification, Runtime Policy, Memory, and Privacy
Four Recent Papers on Reliable LLM Agents: Verification, Runtime Policy, Memory, and Privacy This article reviews four recent arXiv papers that approach LLM agent reliability from different system-level angles rather than from single-model accuracy alone. DeepSciVerify focuses on whether generated scientific claims actually match their cited evidence; A Policy-Driven Runtime Layer for Agentic LLM Serving examines how serving infrastructure can enforce cross-cutting policies for multi-agent workloads; PEAM studies how an embodied agent can internalize experience into parameterized skills instead of relying only on retrieval at inference time; and Got a Secret? LLM Agents Can't Keep It evaluates privacy risks when agents interact over time in persistent social settings. Taken together, these papers suggest that making agents more dependable in real environments requires work on verification, runtime control, memory design, and social privacy evaluation at the system level. [S8][S9][...