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The paper introduces KEEPER, a comparative study of four auxiliary objectives for building control-sufficient textual memory in long-horizon LLM agents, finding that reward-related prediction yields the best task performance and introducing self-consistency bias as a measure of error persistence in recursive memory updates.
The Agent Memory Leaderboard is a public benchmark platform for evaluating and comparing textual and coding-agent memory systems with standardized tests and rankings.