{"ID":2843788,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06786","arxiv_id":"2511.06786","title":"Rethinking Parameter Sharing as Graph Coloring for Structured Compression","abstract":"Modern deep models have massive parameter sizes, leading to high inference-time memory usage that limits practical deployment. Parameter sharing, a form of structured compression, effectively reduces redundancy, but existing approaches remain heuristic-restricted to adjacent layers and lacking a systematic analysis for cross-layer sharing. However, extending sharing across multiple layers leads to an exponentially expanding configuration space, making exhaustive search computationally infeasible and forming a critical bottleneck for parameter sharing. We recast parameter sharing from a group-theoretic perspective as introducing structural symmetries in the model's parameter space. A sharing configuration can be described by a coloring function $α:L\\rightarrow C$ (L: layer indices and C: sharing classes), which determines inter-layer sharing groups while preserving structural symmetry. To determine the coloring function, we propose a second-order geometric criterion based on Taylor expansion and the Hessian spectrum. By projecting perturbations onto the Hessian's low-curvature eigensubspace, the criterion provides an analytic rule for selecting sharing groups that minimize performance impact, yielding a principled and scalable configuration procedure. Across diverse architectures and tasks, Geo-Sharing consistently outperforms state-of-the-art heuristic sharing strategies, achieving higher compression ratios with smaller accuracy degradation.","short_abstract":"Modern deep models have massive parameter sizes, leading to high inference-time memory usage that limits practical deployment. Parameter sharing, a form of structured compression, effectively reduces redundancy, but existing approaches remain heuristic-restricted to adjacent layers and lacking a systematic analysis for...","url_abs":"https://arxiv.org/abs/2511.06786","url_pdf":"https://arxiv.org/pdf/2511.06786v1","authors":"[\"Boyang Zhang\",\"Daning Cheng\",\"Yunquan Zhang\"]","published":"2025-11-10T07:21:35Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}