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  "text": "// Think Harder or Know More //\n\nChain-of-thought prompting enables reasoning in LLMs but requires explicit verbalization of intermediate steps.\n\nLooped transformers offer an alternative by iteratively refining representations within hidden states, but they sacrifice storage capacity in the process.\n\nThis paper investigates combining both: adaptive per-layer looping with gated memory banks.\n\nEach transformer block learns when to iterate its hidden state and when to access stored knowledge.\n\nThe key finding:\n\nLooping primarily benefits mathematical reasoning, while memory banks recover performance on commonsense tasks. Combining both yields a model that outperforms an iso-FLOP baseline with three times the number of layers on math benchmarks.\n\nAnalysis of model internals reveals layer specialization.\n\nEarly layers learn to loop minimally and access memory sparingly, while later layers do both more heavily. The model learns to choose between thinking harder and knowing more, and where to do each.\n\nPaper: https://t.co/0Gl77zMwOY\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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