{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:E45GPYAGUDSAPPQECQ46QXT4DT","short_pith_number":"pith:E45GPYAG","schema_version":"1.0","canonical_sha256":"273a67e006a0e407be041439e85e7c1cd9883971a6c38a565e674f9cd19a9dbc","source":{"kind":"arxiv","id":"2606.17058","version":1},"attestation_state":"computed","paper":{"title":"Evaluating LLM Coding Agents on SZ-Family Lossy Compression Across Architectures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"(2) Florida State University, (3) Argonne National Laboratory), Changqing Li (1), Kai Zhao (2), Sheng Di (3), Shouwei Gao (1), Wenqian Dong (1) ((1) Oregon State University","submitted_at":"2026-04-22T16:53:54Z","abstract_excerpt":"Large language model (LLM) coding agents are increasingly applied to code translation and optimization, yet their effectiveness in performance-critical high-performance computing (HPC) settings remains poorly characterized. This paper evaluates LLM-based coding workflows on SZ-family error-bounded lossy compression kernels, which combine numerical constraints with memory-intensive and control-flow-heavy implementations. We study two representative CUDA workloads (SZp and SZx) and target two heterogeneous execution platforms: NVIDIA GPUs and Cerebras wafer-scale accelerators. Focusing on single"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.17058","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2026-04-22T16:53:54Z","cross_cats_sorted":[],"title_canon_sha256":"b95e10e9335484479455a4c00bbc6cc42138d5859ed5d09f107f6236aa44c830","abstract_canon_sha256":"8a6781d2b278ff9681868430e83293a3b6e2e4df05cd5af92b7ad17a1fd497fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:02.286082Z","signature_b64":"uRyMyKcnBXUhocmNpyE9DAcYWd1d6zgxeBgppbHAtsikKQ0jEfMR6ELk8vYN6FQwdJ8vi87kYLP6/0h2Osi9Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"273a67e006a0e407be041439e85e7c1cd9883971a6c38a565e674f9cd19a9dbc","last_reissued_at":"2026-06-19T16:10:02.285728Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:02.285728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating LLM Coding Agents on SZ-Family Lossy Compression Across Architectures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"(2) Florida State University, (3) Argonne National Laboratory), Changqing Li (1), Kai Zhao (2), Sheng Di (3), Shouwei Gao (1), Wenqian Dong (1) ((1) Oregon State University","submitted_at":"2026-04-22T16:53:54Z","abstract_excerpt":"Large language model (LLM) coding agents are increasingly applied to code translation and optimization, yet their effectiveness in performance-critical high-performance computing (HPC) settings remains poorly characterized. This paper evaluates LLM-based coding workflows on SZ-family error-bounded lossy compression kernels, which combine numerical constraints with memory-intensive and control-flow-heavy implementations. We study two representative CUDA workloads (SZp and SZx) and target two heterogeneous execution platforms: NVIDIA GPUs and Cerebras wafer-scale accelerators. Focusing on single"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.17058","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.17058/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.17058","created_at":"2026-06-19T16:10:02.285788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.17058v1","created_at":"2026-06-19T16:10:02.285788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.17058","created_at":"2026-06-19T16:10:02.285788+00:00"},{"alias_kind":"pith_short_12","alias_value":"E45GPYAGUDSA","created_at":"2026-06-19T16:10:02.285788+00:00"},{"alias_kind":"pith_short_16","alias_value":"E45GPYAGUDSAPPQE","created_at":"2026-06-19T16:10:02.285788+00:00"},{"alias_kind":"pith_short_8","alias_value":"E45GPYAG","created_at":"2026-06-19T16:10:02.285788+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E45GPYAGUDSAPPQECQ46QXT4DT","json":"https://pith.science/pith/E45GPYAGUDSAPPQECQ46QXT4DT.json","graph_json":"https://pith.science/api/pith-number/E45GPYAGUDSAPPQECQ46QXT4DT/graph.json","events_json":"https://pith.science/api/pith-number/E45GPYAGUDSAPPQECQ46QXT4DT/events.json","paper":"https://pith.science/paper/E45GPYAG"},"agent_actions":{"view_html":"https://pith.science/pith/E45GPYAGUDSAPPQECQ46QXT4DT","download_json":"https://pith.science/pith/E45GPYAGUDSAPPQECQ46QXT4DT.json","view_paper":"https://pith.science/paper/E45GPYAG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.17058&json=true","fetch_graph":"https://pith.science/api/pith-number/E45GPYAGUDSAPPQECQ46QXT4DT/graph.json","fetch_events":"https://pith.science/api/pith-number/E45GPYAGUDSAPPQECQ46QXT4DT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E45GPYAGUDSAPPQECQ46QXT4DT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E45GPYAGUDSAPPQECQ46QXT4DT/action/storage_attestation","attest_author":"https://pith.science/pith/E45GPYAGUDSAPPQECQ46QXT4DT/action/author_attestation","sign_citation":"https://pith.science/pith/E45GPYAGUDSAPPQECQ46QXT4DT/action/citation_signature","submit_replication":"https://pith.science/pith/E45GPYAGUDSAPPQECQ46QXT4DT/action/replication_record"}},"created_at":"2026-06-19T16:10:02.285788+00:00","updated_at":"2026-06-19T16:10:02.285788+00:00"}