{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:JURFBPZVF2RJ6D56IPDIQNS4NL","short_pith_number":"pith:JURFBPZV","canonical_record":{"source":{"id":"2502.15803","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-19T06:14:14Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"ed02cb062eddf6c81882bab4a8f17a61daabddd161c2ce6c87769d9f10dc8601","abstract_canon_sha256":"1338ddc48f1439a8bc8fcab1a95c78171e2fb33a06081fd3b65afecc78c266ac"},"schema_version":"1.0"},"canonical_sha256":"4d2250bf352ea29f0fbe43c688365c6afad94abe17d8416b465f719aafcf69c8","source":{"kind":"arxiv","id":"2502.15803","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.15803","created_at":"2026-07-05T10:20:19Z"},{"alias_kind":"arxiv_version","alias_value":"2502.15803v1","created_at":"2026-07-05T10:20:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.15803","created_at":"2026-07-05T10:20:19Z"},{"alias_kind":"pith_short_12","alias_value":"JURFBPZVF2RJ","created_at":"2026-07-05T10:20:19Z"},{"alias_kind":"pith_short_16","alias_value":"JURFBPZVF2RJ6D56","created_at":"2026-07-05T10:20:19Z"},{"alias_kind":"pith_short_8","alias_value":"JURFBPZV","created_at":"2026-07-05T10:20:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:JURFBPZVF2RJ6D56IPDIQNS4NL","target":"record","payload":{"canonical_record":{"source":{"id":"2502.15803","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-19T06:14:14Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"ed02cb062eddf6c81882bab4a8f17a61daabddd161c2ce6c87769d9f10dc8601","abstract_canon_sha256":"1338ddc48f1439a8bc8fcab1a95c78171e2fb33a06081fd3b65afecc78c266ac"},"schema_version":"1.0"},"canonical_sha256":"4d2250bf352ea29f0fbe43c688365c6afad94abe17d8416b465f719aafcf69c8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:20:19.684356Z","signature_b64":"kP266MEb2CmAbdPZwctWvY9Y2MC3ctnc+dm9sAsYE89GArNYkV9Uf92aThTOSzf744PT4qMcaaTul49lPzD8BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d2250bf352ea29f0fbe43c688365c6afad94abe17d8416b465f719aafcf69c8","last_reissued_at":"2026-07-05T10:20:19.683742Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:20:19.683742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.15803","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":"2026-07-05T10:20:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zr7MW8itcOCxPYty7eIcZ13dIlzp7rCyMW+yMwabWtZEkSVYBkRaxemRVBKm+obRHmFRLL/BfDZXILkqBNtrAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T22:40:35.054535Z"},"content_sha256":"63b07605eab206f3ad7a1b6479a4643287781d2cd023a459c05342abd6c3a095","schema_version":"1.0","event_id":"sha256:63b07605eab206f3ad7a1b6479a4643287781d2cd023a459c05342abd6c3a095"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:JURFBPZVF2RJ6D56IPDIQNS4NL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Megrez-Omni Technical Report","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Boxun Li, Congyi Liu, Dong Zhou, Guohao Dai, Guowei Niu, Haiyang Xu, Shengen Yan, Tao Yuan, Weilin Liu, Yadong Li, Yueqing Zhuang, Yu Wang, Zheyue Tan, Zhiyuan Li, Zhuyu Yao","submitted_at":"2025-02-19T06:14:14Z","abstract_excerpt":"In this work, we present the Megrez models, comprising a language model (Megrez-3B-Instruct) and a multimodal model (Megrez-3B-Omni). These models are designed to deliver fast inference, compactness, and robust edge-side intelligence through a software-hardware co-design approach. Megrez-3B-Instruct offers several advantages, including high accuracy, high speed, ease of use, and a wide range of applications. Building on Megrez-3B-Instruct, Megrez-3B-Omni is an on-device multimodal understanding LLM that supports image, text, and audio analysis. It achieves state-of-the-art accuracy across all "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.15803","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/2502.15803/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":"2026-07-05T10:20:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Bq885DqykdV7TDBsik5xehGCyH6YrqEfbbp/kYK/apQS4YEsF1AMbUakumLgguYKvoqPlBQyASvsKaVxgmQ7Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T22:40:35.054912Z"},"content_sha256":"af567e54466c3bffe5d6dcc06f496dd0274191aaa643d2df5b0e5b8c9ee3eb60","schema_version":"1.0","event_id":"sha256:af567e54466c3bffe5d6dcc06f496dd0274191aaa643d2df5b0e5b8c9ee3eb60"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JURFBPZVF2RJ6D56IPDIQNS4NL/bundle.json","state_url":"https://pith.science/pith/JURFBPZVF2RJ6D56IPDIQNS4NL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JURFBPZVF2RJ6D56IPDIQNS4NL/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":"2026-07-21T22:40:35Z","links":{"resolver":"https://pith.science/pith/JURFBPZVF2RJ6D56IPDIQNS4NL","bundle":"https://pith.science/pith/JURFBPZVF2RJ6D56IPDIQNS4NL/bundle.json","state":"https://pith.science/pith/JURFBPZVF2RJ6D56IPDIQNS4NL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JURFBPZVF2RJ6D56IPDIQNS4NL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:JURFBPZVF2RJ6D56IPDIQNS4NL","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":"1338ddc48f1439a8bc8fcab1a95c78171e2fb33a06081fd3b65afecc78c266ac","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-19T06:14:14Z","title_canon_sha256":"ed02cb062eddf6c81882bab4a8f17a61daabddd161c2ce6c87769d9f10dc8601"},"schema_version":"1.0","source":{"id":"2502.15803","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.15803","created_at":"2026-07-05T10:20:19Z"},{"alias_kind":"arxiv_version","alias_value":"2502.15803v1","created_at":"2026-07-05T10:20:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.15803","created_at":"2026-07-05T10:20:19Z"},{"alias_kind":"pith_short_12","alias_value":"JURFBPZVF2RJ","created_at":"2026-07-05T10:20:19Z"},{"alias_kind":"pith_short_16","alias_value":"JURFBPZVF2RJ6D56","created_at":"2026-07-05T10:20:19Z"},{"alias_kind":"pith_short_8","alias_value":"JURFBPZV","created_at":"2026-07-05T10:20:19Z"}],"graph_snapshots":[{"event_id":"sha256:af567e54466c3bffe5d6dcc06f496dd0274191aaa643d2df5b0e5b8c9ee3eb60","target":"graph","created_at":"2026-07-05T10:20:19Z","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/2502.15803/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we present the Megrez models, comprising a language model (Megrez-3B-Instruct) and a multimodal model (Megrez-3B-Omni). These models are designed to deliver fast inference, compactness, and robust edge-side intelligence through a software-hardware co-design approach. Megrez-3B-Instruct offers several advantages, including high accuracy, high speed, ease of use, and a wide range of applications. Building on Megrez-3B-Instruct, Megrez-3B-Omni is an on-device multimodal understanding LLM that supports image, text, and audio analysis. It achieves state-of-the-art accuracy across all ","authors_text":"Boxun Li, Congyi Liu, Dong Zhou, Guohao Dai, Guowei Niu, Haiyang Xu, Shengen Yan, Tao Yuan, Weilin Liu, Yadong Li, Yueqing Zhuang, Yu Wang, Zheyue Tan, Zhiyuan Li, Zhuyu Yao","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-19T06:14:14Z","title":"Megrez-Omni Technical Report"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.15803","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":"sha256:63b07605eab206f3ad7a1b6479a4643287781d2cd023a459c05342abd6c3a095","target":"record","created_at":"2026-07-05T10:20:19Z","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":"1338ddc48f1439a8bc8fcab1a95c78171e2fb33a06081fd3b65afecc78c266ac","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-19T06:14:14Z","title_canon_sha256":"ed02cb062eddf6c81882bab4a8f17a61daabddd161c2ce6c87769d9f10dc8601"},"schema_version":"1.0","source":{"id":"2502.15803","kind":"arxiv","version":1}},"canonical_sha256":"4d2250bf352ea29f0fbe43c688365c6afad94abe17d8416b465f719aafcf69c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4d2250bf352ea29f0fbe43c688365c6afad94abe17d8416b465f719aafcf69c8","first_computed_at":"2026-07-05T10:20:19.683742Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:20:19.683742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kP266MEb2CmAbdPZwctWvY9Y2MC3ctnc+dm9sAsYE89GArNYkV9Uf92aThTOSzf744PT4qMcaaTul49lPzD8BA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:20:19.684356Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.15803","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:63b07605eab206f3ad7a1b6479a4643287781d2cd023a459c05342abd6c3a095","sha256:af567e54466c3bffe5d6dcc06f496dd0274191aaa643d2df5b0e5b8c9ee3eb60"],"state_sha256":"d2e2c443deccf3d11f5642b1f3a032eca1db4be2f745a3a5a48f983fef6a03ff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WKzBF7hLBVV5zc5n6W6uirTsOSbSqVuUomi10uY7thuJv6tCKxp4LIotMJB2TDEe4AJ1M4iVsibZ49hS1RS1Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-21T22:40:35.057009Z","bundle_sha256":"545759f48b9b58d7bda083dbdc9861e3c12ee07619b53793b3dbfe4193a1e685"}}