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Residual Modeling for High-Fidelity Learned Compression of Scientific Data

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arXiv:2606. 05389v1 Announce Type: new Abstract: Lossy compression is essential for massive spatiotemporal data from scientific simulations.

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Authors: Liangji Zhu, Sanjay Ranka, Anand Rangarajan
Released: 2026-06-06 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Residual Modeling for High-Fidelity Learned Compression of Scientific Data overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, rect, correct, eval, line, rate, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.05389

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