Do Agents Learn the User? Evaluating Personalization Across Heterogeneous LLM Agents

A personalized language-model agent should use earlier user feedback in later responses and actions. Users should not have to repeat a preference for shorter answers on every task. Task completion alone cannot show whether an agent has learned such preferences. We introduce AgentBake, a framework and benchmark for comparing personalization methods across agent frameworks with shared tasks and execution interfaces. Simulated users provide feedback from written profiles hidden from the agents. Separate evaluators check later responses and actions, judging task correctness separately. We compare reusing learned user state, learning it from scratch, and continuing to update it. With a shared feedback extractor, agents complete most tasks but often miss answer-style preferences; further updating shows no clear style benefit. Using released learning procedures, we compare continued updating with state frozen after eight feedback opportunities. Under a secondary judge from the host's model family, PAHF improves style matching and task success, while CIPHER shows no clear style gain. Accurate instructions improve style but lower task success relative to no instruction. Training on simulated-user conversations improves preference extraction. However, some preferences are missing from saved instructions, and saved instructions are not always supplied to the host on later tasks.