{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7ZOZRNWCQ7X2IYBJKQVN4ADXJF","short_pith_number":"pith:7ZOZRNWC","schema_version":"1.0","canonical_sha256":"fe5d98b6c287efa46029542ade00774967b470dd3fa2c9edb8c7d666e14e9927","source":{"kind":"arxiv","id":"2607.22588","version":1},"attestation_state":"computed","paper":{"title":"ParBench: A Benchmark for Reliable Evaluation of LLM Parallel Code Translation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.AI","authors_text":"Erel Kaplan, Gal Oren, Le Chen, Niranjan Hasabnis, Samyak Jhaveri, Tomer Bitan, Tom Yotam","submitted_at":"2026-06-09T19:00:09Z","abstract_excerpt":"Modern compute-intensive software must migrate across a changing ecosystem of accelerators, programming APIs, compiler stacks, and portability layers, including CUDA, OpenMP, OpenCL, and OpenMP target offload. Large language models and autonomous coding agents are increasingly proposed for such migration, but the field lacks reliable ways to measure whether they preserve the low-level parallel semantics that make translations behaviorally valid, including thread indexing, synchronization, memory management, host-device coordination, and API-specific execution structure.\n  We present ParBench, "},"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":"2607.22588","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-09T19:00:09Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"bed4fc75cf8ea243aea8c539824c82d0aea1ee38f3124a02d1133fcfd73eb9de","abstract_canon_sha256":"daa5d3587b773dc121b0fb547f6e653cfc595ac3c33b7de6c5942bde0b6e63e9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T00:21:43.723240Z","signature_b64":"xcYo2Fb+XrBusdQTZyzXME18sWaZzfZ+lyxzoumnWb3x7N6nZR8wgsF8jqLmlnZkUTmxKfn5PJCcFEohmyWHAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe5d98b6c287efa46029542ade00774967b470dd3fa2c9edb8c7d666e14e9927","last_reissued_at":"2026-07-28T00:21:43.722357Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T00:21:43.722357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ParBench: A Benchmark for Reliable Evaluation of LLM Parallel Code Translation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.AI","authors_text":"Erel Kaplan, Gal Oren, Le Chen, Niranjan Hasabnis, Samyak Jhaveri, Tomer Bitan, Tom Yotam","submitted_at":"2026-06-09T19:00:09Z","abstract_excerpt":"Modern compute-intensive software must migrate across a changing ecosystem of accelerators, programming APIs, compiler stacks, and portability layers, including CUDA, OpenMP, OpenCL, and OpenMP target offload. Large language models and autonomous coding agents are increasingly proposed for such migration, but the field lacks reliable ways to measure whether they preserve the low-level parallel semantics that make translations behaviorally valid, including thread indexing, synchronization, memory management, host-device coordination, and API-specific execution structure.\n  We present ParBench, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22588","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/2607.22588/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":"2607.22588","created_at":"2026-07-28T00:21:43.722777+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.22588v1","created_at":"2026-07-28T00:21:43.722777+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22588","created_at":"2026-07-28T00:21:43.722777+00:00"},{"alias_kind":"pith_short_12","alias_value":"7ZOZRNWCQ7X2","created_at":"2026-07-28T00:21:43.722777+00:00"},{"alias_kind":"pith_short_16","alias_value":"7ZOZRNWCQ7X2IYBJ","created_at":"2026-07-28T00:21:43.722777+00:00"},{"alias_kind":"pith_short_8","alias_value":"7ZOZRNWC","created_at":"2026-07-28T00:21:43.722777+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/7ZOZRNWCQ7X2IYBJKQVN4ADXJF","json":"https://pith.science/pith/7ZOZRNWCQ7X2IYBJKQVN4ADXJF.json","graph_json":"https://pith.science/api/pith-number/7ZOZRNWCQ7X2IYBJKQVN4ADXJF/graph.json","events_json":"https://pith.science/api/pith-number/7ZOZRNWCQ7X2IYBJKQVN4ADXJF/events.json","paper":"https://pith.science/paper/7ZOZRNWC"},"agent_actions":{"view_html":"https://pith.science/pith/7ZOZRNWCQ7X2IYBJKQVN4ADXJF","download_json":"https://pith.science/pith/7ZOZRNWCQ7X2IYBJKQVN4ADXJF.json","view_paper":"https://pith.science/paper/7ZOZRNWC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.22588&json=true","fetch_graph":"https://pith.science/api/pith-number/7ZOZRNWCQ7X2IYBJKQVN4ADXJF/graph.json","fetch_events":"https://pith.science/api/pith-number/7ZOZRNWCQ7X2IYBJKQVN4ADXJF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7ZOZRNWCQ7X2IYBJKQVN4ADXJF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7ZOZRNWCQ7X2IYBJKQVN4ADXJF/action/storage_attestation","attest_author":"https://pith.science/pith/7ZOZRNWCQ7X2IYBJKQVN4ADXJF/action/author_attestation","sign_citation":"https://pith.science/pith/7ZOZRNWCQ7X2IYBJKQVN4ADXJF/action/citation_signature","submit_replication":"https://pith.science/pith/7ZOZRNWCQ7X2IYBJKQVN4ADXJF/action/replication_record"}},"created_at":"2026-07-28T00:21:43.722777+00:00","updated_at":"2026-07-28T00:21:43.722777+00:00"}