If LLMs are to complete multi-hop reasoning in a single forward pass, they'll almost certainly hit the "shallow local storage" wall — bridging entities learned in early layers are lost by the time the second hop retrieves them. Looped Transformers, which re-use parameters to alleviate memory issues, haven't generalized cleanly. UC Berkeley's Stuart Russell team posted DiscoLoop (arXiv:2607.00341) on July 1, attributing the issue directly to representation: the first loop actually decodes the bridging entity nearly perfectly, but the corresponding hidden state is misaligned with this token's embedding. A zero-training-cost realignment intervention can close most of the generalization gap. Based on this observation, the authors propose a dual-channel loop architecture — running a discrete-embedding channel and a continuous hidden-state channel simultaneously. On symbolic and synthetic language multi-hop tasks, DiscoLoop reaches near-100% accuracy with far fewer training steps than the baseline; when transferred to a real pretrained model, training loss is lower and benchmarks are stronger. The most interesting point isn't the SOTA, but the discovery that "a free realignment can fill most of the gap" — it means Looped Transformers have been losing not from parameters but from a too-thin representation channel.