{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ABMIHYAWDQKUNCMT4MHX74KISH","short_pith_number":"pith:ABMIHYAW","canonical_record":{"source":{"id":"2507.09948","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-14T05:48:09Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"ff860669f8c2cd21f21843643602fffb88c6b60a2f2b2b56414fc85b0eabd5a8","abstract_canon_sha256":"b265fdae5145f552d5ab89ec64f7e5c36233025afeb4d984d2be57ea56206d5c"},"schema_version":"1.0"},"canonical_sha256":"005883e0161c15468993e30f7ff14891c7485ed296e9103fe7ee119081aa6149","source":{"kind":"arxiv","id":"2507.09948","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.09948","created_at":"2026-07-05T11:40:01Z"},{"alias_kind":"arxiv_version","alias_value":"2507.09948v2","created_at":"2026-07-05T11:40:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09948","created_at":"2026-07-05T11:40:01Z"},{"alias_kind":"pith_short_12","alias_value":"ABMIHYAWDQKU","created_at":"2026-07-05T11:40:01Z"},{"alias_kind":"pith_short_16","alias_value":"ABMIHYAWDQKUNCMT","created_at":"2026-07-05T11:40:01Z"},{"alias_kind":"pith_short_8","alias_value":"ABMIHYAW","created_at":"2026-07-05T11:40:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ABMIHYAWDQKUNCMT4MHX74KISH","target":"record","payload":{"canonical_record":{"source":{"id":"2507.09948","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-14T05:48:09Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"ff860669f8c2cd21f21843643602fffb88c6b60a2f2b2b56414fc85b0eabd5a8","abstract_canon_sha256":"b265fdae5145f552d5ab89ec64f7e5c36233025afeb4d984d2be57ea56206d5c"},"schema_version":"1.0"},"canonical_sha256":"005883e0161c15468993e30f7ff14891c7485ed296e9103fe7ee119081aa6149","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:40:01.084393Z","signature_b64":"ScIevAtcMiVpFGn55YNJ40TPG/msNoWFC7wDhKgYpPMsz6E2W3qxFztR8Ry5SO0eAmSSswAh8WI0wzHUWFHRCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"005883e0161c15468993e30f7ff14891c7485ed296e9103fe7ee119081aa6149","last_reissued_at":"2026-07-05T11:40:01.083839Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:40:01.083839Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.09948","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:40:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9ZW9RhxK8CZXWX2MRS3Bs0kD7fETiGckycQ3wwUFoku0zV4xyC9W715IuXyzVHwJ1ouUrrUaihkLZFfBdb7TDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:54:19.655042Z"},"content_sha256":"f2d0286996522819987da8863859afb89831e1c54c961d2e7e18199e85111f7e","schema_version":"1.0","event_id":"sha256:f2d0286996522819987da8863859afb89831e1c54c961d2e7e18199e85111f7e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ABMIHYAWDQKUNCMT4MHX74KISH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Iceberg: Enhancing HLS Modeling with Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.LG","authors_text":"Aditya Grover, Jason Cong, Tung Nguyen, Weikai Li, Yizhou Sun, Zijian Ding","submitted_at":"2025-07-14T05:48:09Z","abstract_excerpt":"Deep learning-based prediction models for High-Level Synthesis (HLS) of hardware designs often struggle to generalize. In this paper, we study how to close the generalizability gap of these models through pretraining on synthetic data and introduce Iceberg, a synthetic data augmentation approach that expands both large language model (LLM)-generated programs and weak labels of unseen design configurations. Our weak label generation method is integrated with an in-context model architecture, enabling meta-learning from actual and proximate labels. Iceberg improves the geometric mean modeling ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09948","kind":"arxiv","version":2},"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/2507.09948/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:40:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YM4i3A6g9KVMEkBc0L+FsLvtsWE9WPvAJOG7IAnSn2lw+ejpehQlljiLMElV7wDxSY+fJg/2RZcBCjqAJtXSAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:54:19.655558Z"},"content_sha256":"0ceba075b5dfaf3157213938375f718fc52f17beb3377a0c58297ca05441c731","schema_version":"1.0","event_id":"sha256:0ceba075b5dfaf3157213938375f718fc52f17beb3377a0c58297ca05441c731"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ABMIHYAWDQKUNCMT4MHX74KISH/bundle.json","state_url":"https://pith.science/pith/ABMIHYAWDQKUNCMT4MHX74KISH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ABMIHYAWDQKUNCMT4MHX74KISH/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T13:54:19Z","links":{"resolver":"https://pith.science/pith/ABMIHYAWDQKUNCMT4MHX74KISH","bundle":"https://pith.science/pith/ABMIHYAWDQKUNCMT4MHX74KISH/bundle.json","state":"https://pith.science/pith/ABMIHYAWDQKUNCMT4MHX74KISH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ABMIHYAWDQKUNCMT4MHX74KISH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ABMIHYAWDQKUNCMT4MHX74KISH","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":"b265fdae5145f552d5ab89ec64f7e5c36233025afeb4d984d2be57ea56206d5c","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-14T05:48:09Z","title_canon_sha256":"ff860669f8c2cd21f21843643602fffb88c6b60a2f2b2b56414fc85b0eabd5a8"},"schema_version":"1.0","source":{"id":"2507.09948","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.09948","created_at":"2026-07-05T11:40:01Z"},{"alias_kind":"arxiv_version","alias_value":"2507.09948v2","created_at":"2026-07-05T11:40:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09948","created_at":"2026-07-05T11:40:01Z"},{"alias_kind":"pith_short_12","alias_value":"ABMIHYAWDQKU","created_at":"2026-07-05T11:40:01Z"},{"alias_kind":"pith_short_16","alias_value":"ABMIHYAWDQKUNCMT","created_at":"2026-07-05T11:40:01Z"},{"alias_kind":"pith_short_8","alias_value":"ABMIHYAW","created_at":"2026-07-05T11:40:01Z"}],"graph_snapshots":[{"event_id":"sha256:0ceba075b5dfaf3157213938375f718fc52f17beb3377a0c58297ca05441c731","target":"graph","created_at":"2026-07-05T11:40:01Z","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/2507.09948/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning-based prediction models for High-Level Synthesis (HLS) of hardware designs often struggle to generalize. In this paper, we study how to close the generalizability gap of these models through pretraining on synthetic data and introduce Iceberg, a synthetic data augmentation approach that expands both large language model (LLM)-generated programs and weak labels of unseen design configurations. Our weak label generation method is integrated with an in-context model architecture, enabling meta-learning from actual and proximate labels. Iceberg improves the geometric mean modeling ac","authors_text":"Aditya Grover, Jason Cong, Tung Nguyen, Weikai Li, Yizhou Sun, Zijian Ding","cross_cats":["cs.AR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-14T05:48:09Z","title":"Iceberg: Enhancing HLS Modeling with Synthetic Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09948","kind":"arxiv","version":2},"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:f2d0286996522819987da8863859afb89831e1c54c961d2e7e18199e85111f7e","target":"record","created_at":"2026-07-05T11:40:01Z","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":"b265fdae5145f552d5ab89ec64f7e5c36233025afeb4d984d2be57ea56206d5c","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-14T05:48:09Z","title_canon_sha256":"ff860669f8c2cd21f21843643602fffb88c6b60a2f2b2b56414fc85b0eabd5a8"},"schema_version":"1.0","source":{"id":"2507.09948","kind":"arxiv","version":2}},"canonical_sha256":"005883e0161c15468993e30f7ff14891c7485ed296e9103fe7ee119081aa6149","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"005883e0161c15468993e30f7ff14891c7485ed296e9103fe7ee119081aa6149","first_computed_at":"2026-07-05T11:40:01.083839Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:40:01.083839Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ScIevAtcMiVpFGn55YNJ40TPG/msNoWFC7wDhKgYpPMsz6E2W3qxFztR8Ry5SO0eAmSSswAh8WI0wzHUWFHRCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:40:01.084393Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.09948","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f2d0286996522819987da8863859afb89831e1c54c961d2e7e18199e85111f7e","sha256:0ceba075b5dfaf3157213938375f718fc52f17beb3377a0c58297ca05441c731"],"state_sha256":"b87b87608f34c0baea38ba17741704a23ef428dd510576a4b71ba12d05143284"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mpvXGA4fK5yKSkA6B4+9LroQ3HYW/tfXUb5yN7NX2Gy5zpc2jr8fwHW+MdmBQnfLjBx/uwuABWRe0p6I45bNCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T13:54:19.659379Z","bundle_sha256":"e04d4ca211a2ac04745419dd41c405c149e9ec9f8bce114c5313f1867bdae4b0"}}