CaseMem: Closing the Loop from Agent Outcomes to Memory Reuse

Long-term memory lets an agent retain knowledge from a growing interaction history. Most memory systems focus on what to store and how to retrieve it. However, a relevant memory is not necessarily one that should be applied to the current decision, nor one that actually helped when it was used. We introduce $\text{CaseMem}$, which separates reusable knowledge from task-specific application and evidence from later use. Each CaseMem record assigns a stable identity to a piece of knowledge, along with its retrieval address, conditions of use, and supporting sources. Before the agent acts, a grounding step binds reusable procedures to the observable task state. When feedback is enabled, a lightweight ledger records exposure and outcome against supplied records to guide later selection without rewriting memory or retraining the agent. On static memory benchmarks, CaseMem is competitive with recent systems without generative construction calls. On agentic benchmark MemoryArena, retrieved experience and task grounding make complementary contributions under two different model family. Their combination improves string reward over retrieval alone by 24% with Qwen3-32B and 12% with GPT-4.1-mini. Separate controlled experiments on a fixed store show that outcome feedback can recover an overlooked procedure and demote a harmful one; replacing current feedback with earlier outcomes reduces the gains. Together, these findings show that agent memory can improve through how knowledge is applied and selected, alongside how it is stored.