{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:6BJEU452QYFQ4MAXOXOR2SCBMQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7bba25fa200d9ff8bdfca68b2c4b440b331bcfb2f2f64d41d01b7d66fbe1a5c5","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-13T19:25:26Z","title_canon_sha256":"7f4f2b280a17be3d60ff8312e915a002a85ec2f0f52a8419fb72257e30288bee"},"schema_version":"1.0","source":{"id":"2607.12104","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.12104","created_at":"2026-07-15T00:21:33Z"},{"alias_kind":"arxiv_version","alias_value":"2607.12104v1","created_at":"2026-07-15T00:21:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.12104","created_at":"2026-07-15T00:21:33Z"},{"alias_kind":"pith_short_12","alias_value":"6BJEU452QYFQ","created_at":"2026-07-15T00:21:33Z"},{"alias_kind":"pith_short_16","alias_value":"6BJEU452QYFQ4MAX","created_at":"2026-07-15T00:21:33Z"},{"alias_kind":"pith_short_8","alias_value":"6BJEU452","created_at":"2026-07-15T00:21:33Z"}],"graph_snapshots":[{"event_id":"sha256:4fb77956573f1000718e4c96333e928fd9694329a3d97d56ea8313edbb29acd3","target":"graph","created_at":"2026-07-15T00:21:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.12104/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning models for system diagnostics rely on kernel execution traces to capture fine-grained system behavior, but collecting production traces in industrial systems is costly due to runtime overhead, storage demands, and privacy constraints. We present TraceSynth, a diffusion-based framework for generating synthetic kernel traces that augment limited real data for downstream ML tasks. TraceSynth models traces as multi-channel sequences (event types, timestamps, CPU affinity, thread identifiers, and process metadata) using a Transformer-based denoising diffusion process with constrain","authors_text":"Francois Tetreault, Mahsa Panahandeh, Naser Ezzati-Jivan, Sneh Patel, Yuvraj Sehgal","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-13T19:25:26Z","title":"TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.12104","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:47657e0c5563f7de213fd77288fb1f5518b6e0e0db8f44cab72b3a40861cf24f","target":"record","created_at":"2026-07-15T00:21:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7bba25fa200d9ff8bdfca68b2c4b440b331bcfb2f2f64d41d01b7d66fbe1a5c5","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-13T19:25:26Z","title_canon_sha256":"7f4f2b280a17be3d60ff8312e915a002a85ec2f0f52a8419fb72257e30288bee"},"schema_version":"1.0","source":{"id":"2607.12104","kind":"arxiv","version":1}},"canonical_sha256":"f0524a73ba860b0e301775dd1d4841641d6113e795a9040a600cca09053718f2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f0524a73ba860b0e301775dd1d4841641d6113e795a9040a600cca09053718f2","first_computed_at":"2026-07-15T00:21:33.398839Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-15T00:21:33.398839Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rtb+6GTD7GjpjQJPph7geXryxe/BHcq4Of/Wa6BKGmlWfkkH0VuuFA5DjkFOUr3wxVw3WIGGdoi7UTUDQNMaCw==","signature_status":"signed_v1","signed_at":"2026-07-15T00:21:33.399608Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.12104","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:47657e0c5563f7de213fd77288fb1f5518b6e0e0db8f44cab72b3a40861cf24f","sha256:4fb77956573f1000718e4c96333e928fd9694329a3d97d56ea8313edbb29acd3"],"state_sha256":"9cb8d59af09248fcd64c88e9f496ef61b54c351f07049f430a0a162b95b0566a"}