{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:YZWORFV72LK35PEX45PTC6V75T","short_pith_number":"pith:YZWORFV7","canonical_record":{"source":{"id":"2501.09849","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-16T21:33:47Z","cross_cats_sorted":[],"title_canon_sha256":"1191da07bfc60aa8e8443d0208f03ffc72672c62dbba396a0d211bd35aa439e1","abstract_canon_sha256":"30c49cf477cb429a73c2317112d2faa6b2557c2f35ff27e6c014d9a555bfc3ca"},"schema_version":"1.0"},"canonical_sha256":"c66ce896bfd2d5bebc97e75f317abfecdb163dbb8cf8fb2801bfe508696d5475","source":{"kind":"arxiv","id":"2501.09849","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.09849","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"arxiv_version","alias_value":"2501.09849v1","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09849","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"pith_short_12","alias_value":"YZWORFV72LK3","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"pith_short_16","alias_value":"YZWORFV72LK35PEX","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"pith_short_8","alias_value":"YZWORFV7","created_at":"2026-07-05T10:02:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:YZWORFV72LK35PEX45PTC6V75T","target":"record","payload":{"canonical_record":{"source":{"id":"2501.09849","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-16T21:33:47Z","cross_cats_sorted":[],"title_canon_sha256":"1191da07bfc60aa8e8443d0208f03ffc72672c62dbba396a0d211bd35aa439e1","abstract_canon_sha256":"30c49cf477cb429a73c2317112d2faa6b2557c2f35ff27e6c014d9a555bfc3ca"},"schema_version":"1.0"},"canonical_sha256":"c66ce896bfd2d5bebc97e75f317abfecdb163dbb8cf8fb2801bfe508696d5475","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:02:12.279185Z","signature_b64":"0jV7/Tk88EYui42Z+3HuNQYsNUU4cNksR3csNle7w+IKlRS7lrgmyNWzg8jXYOKcsb7XoEloa4pMcovRjyBGCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c66ce896bfd2d5bebc97e75f317abfecdb163dbb8cf8fb2801bfe508696d5475","last_reissued_at":"2026-07-05T10:02:12.278781Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:02:12.278781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.09849","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":"2026-07-05T10:02:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2d8ckSjtFTo7WxZACF4Ti4mJHX2ZrpSmAjvRw+HZ6V+UrwYyK9v8KXC2HeNQyFdbW6ySLAoUpiOn1hpstBo4Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T11:30:03.654255Z"},"content_sha256":"452b3908674d6f1dd834c779a5e5f59a953b6739153296ba70212f00286e685e","schema_version":"1.0","event_id":"sha256:452b3908674d6f1dd834c779a5e5f59a953b6739153296ba70212f00286e685e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:YZWORFV72LK35PEX45PTC6V75T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Coded Deep Learning: Framework and Algorithm","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"En-hui Yang, Shayan Mohajer Hamidi","submitted_at":"2025-01-16T21:33:47Z","abstract_excerpt":"The success of deep learning (DL) is often achieved with large models and high complexity during both training and post-training inferences, hindering training in resource-limited settings. To alleviate these issues, this paper introduces a new framework dubbed ``coded deep learning'' (CDL), which integrates information-theoretic coding concepts into the inner workings of DL, to significantly compress model weights and activations, reduce computational complexity at both training and post-training inference stages, and enable efficient model/data parallelism. Specifically, within CDL, (i) we f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09849","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/2501.09849/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":"2026-07-05T10:02:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3bzuoBY3M80WM49E6xLzPjHwOMOphFtRmrMezFjDNcfquSsNwLgF76rPr711W1ywH+S+oLSO7Mjk25nV1H6BCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T11:30:03.654648Z"},"content_sha256":"664737e19e55694e7a08b6d4fc2778be0c442d2ff36781194d7f244a2c756102","schema_version":"1.0","event_id":"sha256:664737e19e55694e7a08b6d4fc2778be0c442d2ff36781194d7f244a2c756102"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YZWORFV72LK35PEX45PTC6V75T/bundle.json","state_url":"https://pith.science/pith/YZWORFV72LK35PEX45PTC6V75T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YZWORFV72LK35PEX45PTC6V75T/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":"2026-07-06T11:30:03Z","links":{"resolver":"https://pith.science/pith/YZWORFV72LK35PEX45PTC6V75T","bundle":"https://pith.science/pith/YZWORFV72LK35PEX45PTC6V75T/bundle.json","state":"https://pith.science/pith/YZWORFV72LK35PEX45PTC6V75T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YZWORFV72LK35PEX45PTC6V75T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YZWORFV72LK35PEX45PTC6V75T","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":"30c49cf477cb429a73c2317112d2faa6b2557c2f35ff27e6c014d9a555bfc3ca","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-16T21:33:47Z","title_canon_sha256":"1191da07bfc60aa8e8443d0208f03ffc72672c62dbba396a0d211bd35aa439e1"},"schema_version":"1.0","source":{"id":"2501.09849","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.09849","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"arxiv_version","alias_value":"2501.09849v1","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09849","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"pith_short_12","alias_value":"YZWORFV72LK3","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"pith_short_16","alias_value":"YZWORFV72LK35PEX","created_at":"2026-07-05T10:02:12Z"},{"alias_kind":"pith_short_8","alias_value":"YZWORFV7","created_at":"2026-07-05T10:02:12Z"}],"graph_snapshots":[{"event_id":"sha256:664737e19e55694e7a08b6d4fc2778be0c442d2ff36781194d7f244a2c756102","target":"graph","created_at":"2026-07-05T10:02:12Z","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/2501.09849/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The success of deep learning (DL) is often achieved with large models and high complexity during both training and post-training inferences, hindering training in resource-limited settings. To alleviate these issues, this paper introduces a new framework dubbed ``coded deep learning'' (CDL), which integrates information-theoretic coding concepts into the inner workings of DL, to significantly compress model weights and activations, reduce computational complexity at both training and post-training inference stages, and enable efficient model/data parallelism. Specifically, within CDL, (i) we f","authors_text":"En-hui Yang, Shayan Mohajer Hamidi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-16T21:33:47Z","title":"Coded Deep Learning: Framework and Algorithm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09849","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":"sha256:452b3908674d6f1dd834c779a5e5f59a953b6739153296ba70212f00286e685e","target":"record","created_at":"2026-07-05T10:02:12Z","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":"30c49cf477cb429a73c2317112d2faa6b2557c2f35ff27e6c014d9a555bfc3ca","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-16T21:33:47Z","title_canon_sha256":"1191da07bfc60aa8e8443d0208f03ffc72672c62dbba396a0d211bd35aa439e1"},"schema_version":"1.0","source":{"id":"2501.09849","kind":"arxiv","version":1}},"canonical_sha256":"c66ce896bfd2d5bebc97e75f317abfecdb163dbb8cf8fb2801bfe508696d5475","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c66ce896bfd2d5bebc97e75f317abfecdb163dbb8cf8fb2801bfe508696d5475","first_computed_at":"2026-07-05T10:02:12.278781Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:02:12.278781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0jV7/Tk88EYui42Z+3HuNQYsNUU4cNksR3csNle7w+IKlRS7lrgmyNWzg8jXYOKcsb7XoEloa4pMcovRjyBGCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:02:12.279185Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.09849","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:452b3908674d6f1dd834c779a5e5f59a953b6739153296ba70212f00286e685e","sha256:664737e19e55694e7a08b6d4fc2778be0c442d2ff36781194d7f244a2c756102"],"state_sha256":"7178e2bfcc9e90b1995d19f511cb0ce021520be7285edb578b3285115b4f2115"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+KExSpLg5/WzN9u2B9b0Ee4Dm8x6LrXsc16GdY2eOmvomh/yXrPya0A8wyc1tPxpVHBJFqw4JoTr5snDc57iDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T11:30:03.656671Z","bundle_sha256":"2d0fb7752e52c786fd2c44a756f3c27712e424ec3f5b2f778566c4460742ccf7"}}