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  "text": "Coding agents learn from experience, but that knowledge stays locked in silos.\n\nSolve a thousand SWE tasks, and none of that wisdom helps with competitive coding.\n\nWhat if memories could transfer across domains?\n\nThe work introduces Memory Transfer Learning, a framework where coding agents share a unified memory pool across 6 heterogeneous benchmarks. They test four memory formats ranging from raw execution traces to high-level insights, and find that cross-domain memory improves average performance by 3.7%.\n\nWhy does it matter?\n\nThe transferable value isn't task-specific code. It's meta-knowledge: validation routines, structured action workflows, safe interaction patterns with execution environments. Algorithmic strategy transfer accounts for only 5.5% of the gains. The real benefit comes from procedural guidance on how to act, not what to code. Abstraction dictates transferability: high-level insights generalize well, while low-level execution traces often cause negative transfer by anchoring agents to incompatible implementation details.\n\nPaper: https://t.co/XPD5kczsoZ\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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