Reproducibility Data: Subspace Direction, Not Rank - Scaling Linear Attention via Sweet-Spot Init Transfer

Experimental data underlying the paper 'Subspace Direction, Not Rank: Scaling Linear Attention via Sweet-Spot Init Transfer'. Contains structured per-seed results (best/final accuracy + training curves), SVD-based effective-rank and cosine-similarity analyses (subspace direction alignment), five-task probing comparisons, MQAR benchmark results, direction-ablation results, and raw training logs. Hardware: NVIDIA V100 and MetaX C550 (8-GPU DDP). The included README maps every table and figure in the paper to its source data file, enabling independent verification.