{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:JUJFFJHQYWBFGRXEVFXJMAR73N","short_pith_number":"pith:JUJFFJHQ","canonical_record":{"source":{"id":"2509.00280","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-29T23:45:09Z","cross_cats_sorted":["cs.DC","cs.PF"],"title_canon_sha256":"9dcad6d924954b74d34ffc1190849f06ca4e8a6ef412eda413137b02e6b66fde","abstract_canon_sha256":"2c660f2c1db814b7347bf845470e44ce3a4c3486d5d743d437ebaed695e88468"},"schema_version":"1.0"},"canonical_sha256":"4d1252a4f0c5825346e4a96e96023fdb68d77060f075b2cdb9f12ba7a4f3c4a4","source":{"kind":"arxiv","id":"2509.00280","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.00280","created_at":"2026-07-05T12:02:12Z"},{"alias_kind":"arxiv_version","alias_value":"2509.00280v1","created_at":"2026-07-05T12:02:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.00280","created_at":"2026-07-05T12:02:12Z"},{"alias_kind":"pith_short_12","alias_value":"JUJFFJHQYWBF","created_at":"2026-07-05T12:02:12Z"},{"alias_kind":"pith_short_16","alias_value":"JUJFFJHQYWBFGRXE","created_at":"2026-07-05T12:02:12Z"},{"alias_kind":"pith_short_8","alias_value":"JUJFFJHQ","created_at":"2026-07-05T12:02:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:JUJFFJHQYWBFGRXEVFXJMAR73N","target":"record","payload":{"canonical_record":{"source":{"id":"2509.00280","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-29T23:45:09Z","cross_cats_sorted":["cs.DC","cs.PF"],"title_canon_sha256":"9dcad6d924954b74d34ffc1190849f06ca4e8a6ef412eda413137b02e6b66fde","abstract_canon_sha256":"2c660f2c1db814b7347bf845470e44ce3a4c3486d5d743d437ebaed695e88468"},"schema_version":"1.0"},"canonical_sha256":"4d1252a4f0c5825346e4a96e96023fdb68d77060f075b2cdb9f12ba7a4f3c4a4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:12.753143Z","signature_b64":"pNEy6BlI1LAeWzG7zMovn67st03kU2ffnkwaobhEY2eTxyPhpaLfW1ri+5S9Fq9Cp7I7ExfOLUFwc42D9AwJCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d1252a4f0c5825346e4a96e96023fdb68d77060f075b2cdb9f12ba7a4f3c4a4","last_reissued_at":"2026-07-05T12:02:12.752655Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:12.752655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.00280","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T12:02:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M6St5kV2dI59Nm4OgdBvvVqHuEIrPGch0itwAhn0EQWlBhA6nsM3JLa89p55fdZLdgcVdorqd+R4ob/BghWEDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:50:32.410774Z"},"content_sha256":"187e4d2fd37c163822095cfeb1c0b5df0247b710a0429789a63d19a3291a4211","schema_version":"1.0","event_id":"sha256:187e4d2fd37c163822095cfeb1c0b5df0247b710a0429789a63d19a3291a4211"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:JUJFFJHQYWBFGRXEVFXJMAR73N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ReLATE: Learning Efficient Sparse Encoding for High-Performance Tensor Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.PF"],"primary_cat":"cs.LG","authors_text":"Ahmed E. Helal, Fabio Checconi, Fabrizio Petrini, Jan Laukemann, Jee Choi, Jesmin Jahan Tithi, Yongseok Soh","submitted_at":"2025-08-29T23:45:09Z","abstract_excerpt":"Tensor decomposition (TD) is essential for analyzing high-dimensional sparse data, yet its irregular computations and memory-access patterns pose major performance challenges on modern parallel processors. Prior works rely on expert-designed sparse tensor formats that fail to adapt to irregular tensor shapes and/or highly variable data distributions. We present the reinforcement-learned adaptive tensor encoding (ReLATE) framework, a novel learning-augmented method that automatically constructs efficient sparse tensor representations without labeled training samples. ReLATE employs an autonomou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.00280","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/2509.00280/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":"2026-07-05T12:02:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xeEdrpEKWddElPBtpx3gYEmUySF3GHH5QcyhiDe1HnlSBt6vIyJiOVkjMnMoEtBxEvIyxslpiRzTRRufeN2UAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:50:32.411293Z"},"content_sha256":"ae095a18c8d14b1fb66bbb3f469dfce1e49f43da5214a19982d81c6c00de1fd4","schema_version":"1.0","event_id":"sha256:ae095a18c8d14b1fb66bbb3f469dfce1e49f43da5214a19982d81c6c00de1fd4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JUJFFJHQYWBFGRXEVFXJMAR73N/bundle.json","state_url":"https://pith.science/pith/JUJFFJHQYWBFGRXEVFXJMAR73N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JUJFFJHQYWBFGRXEVFXJMAR73N/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":"2026-08-06T20:50:32Z","links":{"resolver":"https://pith.science/pith/JUJFFJHQYWBFGRXEVFXJMAR73N","bundle":"https://pith.science/pith/JUJFFJHQYWBFGRXEVFXJMAR73N/bundle.json","state":"https://pith.science/pith/JUJFFJHQYWBFGRXEVFXJMAR73N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JUJFFJHQYWBFGRXEVFXJMAR73N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:JUJFFJHQYWBFGRXEVFXJMAR73N","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":"2c660f2c1db814b7347bf845470e44ce3a4c3486d5d743d437ebaed695e88468","cross_cats_sorted":["cs.DC","cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-29T23:45:09Z","title_canon_sha256":"9dcad6d924954b74d34ffc1190849f06ca4e8a6ef412eda413137b02e6b66fde"},"schema_version":"1.0","source":{"id":"2509.00280","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.00280","created_at":"2026-07-05T12:02:12Z"},{"alias_kind":"arxiv_version","alias_value":"2509.00280v1","created_at":"2026-07-05T12:02:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.00280","created_at":"2026-07-05T12:02:12Z"},{"alias_kind":"pith_short_12","alias_value":"JUJFFJHQYWBF","created_at":"2026-07-05T12:02:12Z"},{"alias_kind":"pith_short_16","alias_value":"JUJFFJHQYWBFGRXE","created_at":"2026-07-05T12:02:12Z"},{"alias_kind":"pith_short_8","alias_value":"JUJFFJHQ","created_at":"2026-07-05T12:02:12Z"}],"graph_snapshots":[{"event_id":"sha256:ae095a18c8d14b1fb66bbb3f469dfce1e49f43da5214a19982d81c6c00de1fd4","target":"graph","created_at":"2026-07-05T12:02:12Z","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/2509.00280/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Tensor decomposition (TD) is essential for analyzing high-dimensional sparse data, yet its irregular computations and memory-access patterns pose major performance challenges on modern parallel processors. Prior works rely on expert-designed sparse tensor formats that fail to adapt to irregular tensor shapes and/or highly variable data distributions. We present the reinforcement-learned adaptive tensor encoding (ReLATE) framework, a novel learning-augmented method that automatically constructs efficient sparse tensor representations without labeled training samples. ReLATE employs an autonomou","authors_text":"Ahmed E. Helal, Fabio Checconi, Fabrizio Petrini, Jan Laukemann, Jee Choi, Jesmin Jahan Tithi, Yongseok Soh","cross_cats":["cs.DC","cs.PF"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-29T23:45:09Z","title":"ReLATE: Learning Efficient Sparse Encoding for High-Performance Tensor Decomposition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.00280","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":"sha256:187e4d2fd37c163822095cfeb1c0b5df0247b710a0429789a63d19a3291a4211","target":"record","created_at":"2026-07-05T12:02:12Z","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":"2c660f2c1db814b7347bf845470e44ce3a4c3486d5d743d437ebaed695e88468","cross_cats_sorted":["cs.DC","cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-29T23:45:09Z","title_canon_sha256":"9dcad6d924954b74d34ffc1190849f06ca4e8a6ef412eda413137b02e6b66fde"},"schema_version":"1.0","source":{"id":"2509.00280","kind":"arxiv","version":1}},"canonical_sha256":"4d1252a4f0c5825346e4a96e96023fdb68d77060f075b2cdb9f12ba7a4f3c4a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4d1252a4f0c5825346e4a96e96023fdb68d77060f075b2cdb9f12ba7a4f3c4a4","first_computed_at":"2026-07-05T12:02:12.752655Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:02:12.752655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pNEy6BlI1LAeWzG7zMovn67st03kU2ffnkwaobhEY2eTxyPhpaLfW1ri+5S9Fq9Cp7I7ExfOLUFwc42D9AwJCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T12:02:12.753143Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.00280","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:187e4d2fd37c163822095cfeb1c0b5df0247b710a0429789a63d19a3291a4211","sha256:ae095a18c8d14b1fb66bbb3f469dfce1e49f43da5214a19982d81c6c00de1fd4"],"state_sha256":"e62de0f52b76b534d50162b684721b9d256588584b73be25f95c2e3ec2ee9f52"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9IJutwzPy1Q48COgjsOz+Cu3nfP/gYN049u8m18/eFmZiJOl6c1sOpmFuTo+DBkG6Lr5iFqDvKfS7sxcpi1WBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T20:50:32.415566Z","bundle_sha256":"38e69ee4cf9372687bdb75b2b6f1e74ea37803df4ef6e4000ccb7ceae40cc5c0"}}