Large-scale scientific simulations generate substantial data volumes, making lossy compression essential for reducing storage and data movement costs. However, users configure compressors through numerical error bounds (EBs) while often evaluating results using quality metrics and end-to-end performance. Because the re...
Khondoker Mirazul Mumenin, Dong Dai, Sheng Di et al.· 0 citations
LAM is proposed, a Lossy Agent Memory system with three components: a deterministic deduplication rule with a substitution bound on retrieval scores - a bound on score perturbation, not a certificate of unchanged ranking; a memory manager that preserves the cached prefix and overlaps compaction with inference; and a pe...
Bai-Xi Sun, Le Chen, Anjir Ahmed Chowdhury et al.· 0 citations
XnYZip is introduced, an error-bounded lossy compressor based on provably optimal Truncated Octahedron quantization, combined with a locality-aware encoding pipeline using space-filling curves and run-length encoding, to develop a theory of point cloud compressibility for dense data.
You-Yuan Liu, Longtao Zhang, Ruoyu Li et al.· Proceedings of the VLDB Endo...· 0 citations
FaCTz is presented, the first GPU-based error-bounded lossy compressor that guarantees critical-point preservation and provides a block-wise mode optimized for throughput and a speculative per-point mode optimized for compression ratio.
Mingze Xia, Yu-Xiao Li, Sheng Di et al.· 0 citations
TOPIQ is presented, a statistical error-propagation framework that predicts QoI-level bias and uncertainty from compact compression metadata (less than 0.1% of original data) and decomposes QoIs into primitive operators with closed-form propagation rules accounting for spatial error correlation and data-error coupling.
You-Yuan Liu, Bo Jiang, Taolue Yang et al.· 0 citations
This work focuses on semantic heterogeneity and studies how it should shape the management and evaluation of working memory in coding agents, finding that semantically different working-memory objects exhibit distinct retention and compression behavior.
Le Chen, Zi-Shen Wan, Bai-Xi Sun et al.· 1 citation
ParticleGS is a visualization-aware framework based on 3D Gaussian Splatting (3DGS) that learns a compact representation directly optimized for rendered image quality, combining a multi-stage, multi-orbit training pipeline and a lightweight network that adapts a single trained model to user-specified visualization para...
Bo Jiang, You-Yuan Liu, Taolue Yang et al.· arXiv.org· 1 citation
Evaluation on production-scale scientific datasets demonstrates that TZ achieves approximately 10 × higher compression ratios than state-of-the-art GPU compressors under the same error bound, while maintaining competitive, high-throughput performance.
Zhuoxun Yang, Ruoyu Li, A. Subrahmanya et al.· IEEE International Symposium...· 0 citations
This paper develops a novel framework that characterizes compressibility limits for scientific datasets under realistic tiling constraints, and is the first framework to rigorously characterize lossy compressibility limits for scientific datasets and compressor, moving beyond classical asymptotic 1D source models.
Sujata Sinha, Sheng Di, Vishwas Rao et al.· IEEE International Symposium...· 0 citations
The OPAL universal framework is the OPAL universal framework, an adaptive architecture that endows generic lossy compressors with on-demand retrieval capabilities, enabling flexible and progressive access across multiple spatial regions, resolutions, and precisions.
Longtao Zhang, Ruoyu Li, Zhuoxun Yang et al.· IEEE International Symposium...· 2 citations
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