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Review regressions before expanding the test." ], "measure": "Numerical differences, end-to-end latency, throughput, and memory at realistic shapes.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2610.09090", "title": "Tucker Bottleneck Attention for Multi-Dimensional Sequence Modeling", "summary": "TuBA replaces full self-attention over long multidimensional token sequences (video frames, weather grids) with attention performed on compact low-rank Tucker cores, projecting hidden tensors down, mixing globally in the compressed space, and writing updates back. 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