{ "schema_version": 1, "builder_plan": { "scenario": "Start with one repeated product task that has a measurable failure or cost.", "change": "Read the methods and identify one change you can isolate in that workflow.", "baseline": "Your current implementation on the same held-out tasks.", "steps": [ "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.", "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.", "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.", "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test." ], "measure": "Successful outcomes, response time, human rework, and total cost per successful task.", "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": "2606.14353", "title": "Can Deep Neural Networks Improve Compression of Very Large Scientific Data?", "summary": "Replaces traditional predictors in error-bounded compressors such as SZ3.1 with pretrained weather models including GraphCast, Aurora, and CRA5. It adds large-model inference to the compression pipeline and requires suitable domain foundation models. 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Background", "role": "baseline", "latex": "\\operatorname*{arg\\,max}_{\\langle C,D\\rangle}\\frac{|X|}{|C(X)|},\\quad\\text{such that}\\quad\\frac{|X_{i}-D(C(X))_{i}|}{r_{X}}\\leq\\epsilon", "mathml": "arg​max⟨C,D⟩⁡|X||C⁡(X)|,such that|Xi−D​(C⁡(X))i|rX≤ϵ", "context": "For a variable X, let ϵ be a user-specified relative error bound. Let C be a compression function that generates the compressed version Z of X, i.e., Z=C⁡(X), and let D be a decompression function that generates the decompressed version X′ of Z, i.e., X′=D⁡(Z). The process is lossy, therefore X′ is an approximation of X. The goal is to define a pair of functions ⟨C,D⟩ such that the compression ratio ρ of X is maximized, while the relative difference between each original data point Xi and its decompressed counterpart Xi′=D​(C⁡(X))i does not exceed the error bound ϵ. Formally:", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" }, { "anchor": "S3.E10", "label": "(10)", "section": "3. Methodology", "role": "proposed_method", "latex": "\\mathbf{p}_{k}=\\mathcal{F}(\\mathbf{p}^{\\prime}_{k-2},\\,\\mathbf{p}^{\\prime}_{k-1}).", "mathml": "𝐩k=ℱ⁡(𝐩k−2′,𝐩k−1′).", "context": "Let 𝐭k, 𝐩k, and 𝐩k′ be the full multi-variable states at step k, that is, the stacks of the per-variable slices tX,k, pX,k, and pX,k′ over all X∈𝒳. We store the first two states 𝐭0 and 𝐭1 losslessly to seed the loop. For each step k≥2 the model ℱ predicts from the two preceding corrected states:", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" }, { "anchor": "S2.E1", "label": "(1)", "section": "2. 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