{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:VUWH5DO632VWXI7XELQVCT6XQK","short_pith_number":"pith:VUWH5DO6","canonical_record":{"source":{"id":"2501.01904","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-03T17:14:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"84ca0ef266a641e88842bd70425050200b3c534bf7ea27e5494e8a289fc720f2","abstract_canon_sha256":"c7a48a45d52693c0fd427f439b80cf8192218576ee4676d5b514a2190280bd17"},"schema_version":"1.0"},"canonical_sha256":"ad2c7e8ddedeab6ba3f722e1514fd782af1e4c59c506d0aec20fbc93d97bdbc8","source":{"kind":"arxiv","id":"2501.01904","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.01904","created_at":"2026-07-05T10:09:51Z"},{"alias_kind":"arxiv_version","alias_value":"2501.01904v2","created_at":"2026-07-05T10:09:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01904","created_at":"2026-07-05T10:09:51Z"},{"alias_kind":"pith_short_12","alias_value":"VUWH5DO632VW","created_at":"2026-07-05T10:09:51Z"},{"alias_kind":"pith_short_16","alias_value":"VUWH5DO632VWXI7X","created_at":"2026-07-05T10:09:51Z"},{"alias_kind":"pith_short_8","alias_value":"VUWH5DO6","created_at":"2026-07-05T10:09:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:VUWH5DO632VWXI7XELQVCT6XQK","target":"record","payload":{"canonical_record":{"source":{"id":"2501.01904","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-03T17:14:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"84ca0ef266a641e88842bd70425050200b3c534bf7ea27e5494e8a289fc720f2","abstract_canon_sha256":"c7a48a45d52693c0fd427f439b80cf8192218576ee4676d5b514a2190280bd17"},"schema_version":"1.0"},"canonical_sha256":"ad2c7e8ddedeab6ba3f722e1514fd782af1e4c59c506d0aec20fbc93d97bdbc8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:09:51.044408Z","signature_b64":"8RDtXkaEnBybbHYnlIQp6EHa8gDlsteG+mPgw9n4BP/8jVR5KBxGVpKMyqeGykjOfqnLNqyOZyTtwiTowsX7Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ad2c7e8ddedeab6ba3f722e1514fd782af1e4c59c506d0aec20fbc93d97bdbc8","last_reissued_at":"2026-07-05T10:09:51.043902Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:09:51.043902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.01904","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":"2026-07-05T10:09:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IWehhE8GE131z8zDDCgy9+jYRZXHjOMNPNzpbtabq/PZxDCdNxmfZ9A4hpLBGRknSw4ag+DxFxnVfsG9UJ05BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T21:14:29.488302Z"},"content_sha256":"29a2753ec341031f94beecd60d40e90d193e69d63f36ea4cbeee8fb65620ec5e","schema_version":"1.0","event_id":"sha256:29a2753ec341031f94beecd60d40e90d193e69d63f36ea4cbeee8fb65620ec5e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:VUWH5DO632VWXI7XELQVCT6XQK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Virgo: A Preliminary Exploration on Reproducing o1-like MLLM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bingning Wang, Ji-Rong Wen, Wayne Xin Zhao, Weipeng Chen, Yifan Du, Yifan Li, Yuqi Huo, Zheng Liu, Zhongyuan Wang, Zikang Liu","submitted_at":"2025-01-03T17:14:16Z","abstract_excerpt":"Recently, slow-thinking reasoning systems, built upon large language models (LLMs), have garnered widespread attention by scaling the thinking time during inference. There is also growing interest in adapting this capability to multimodal large language models (MLLMs). Given that MLLMs handle more complex data semantics across different modalities, it is intuitively more challenging to implement multimodal slow-thinking systems.\n  To address this issue, in this paper, we explore a straightforward approach by fine-tuning a capable MLLM with a small amount of textual long-form thought data, resu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01904","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/2501.01904/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":"2026-07-05T10:09:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nEb8pCpDldvZqnFCKGkarxq8CG0lRY6SowSENYr/CNap6vRe8SBfbY3gAEa/3mdq9iOpHAgZxXDdY8ySJPANCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T21:14:29.489228Z"},"content_sha256":"aed24608492c87e4d8820950253dc6c92a86b5206fa0b3ebbff79352dbdaea43","schema_version":"1.0","event_id":"sha256:aed24608492c87e4d8820950253dc6c92a86b5206fa0b3ebbff79352dbdaea43"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VUWH5DO632VWXI7XELQVCT6XQK/bundle.json","state_url":"https://pith.science/pith/VUWH5DO632VWXI7XELQVCT6XQK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VUWH5DO632VWXI7XELQVCT6XQK/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":"2026-08-17T21:14:29Z","links":{"resolver":"https://pith.science/pith/VUWH5DO632VWXI7XELQVCT6XQK","bundle":"https://pith.science/pith/VUWH5DO632VWXI7XELQVCT6XQK/bundle.json","state":"https://pith.science/pith/VUWH5DO632VWXI7XELQVCT6XQK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VUWH5DO632VWXI7XELQVCT6XQK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:VUWH5DO632VWXI7XELQVCT6XQK","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":"c7a48a45d52693c0fd427f439b80cf8192218576ee4676d5b514a2190280bd17","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-03T17:14:16Z","title_canon_sha256":"84ca0ef266a641e88842bd70425050200b3c534bf7ea27e5494e8a289fc720f2"},"schema_version":"1.0","source":{"id":"2501.01904","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.01904","created_at":"2026-07-05T10:09:51Z"},{"alias_kind":"arxiv_version","alias_value":"2501.01904v2","created_at":"2026-07-05T10:09:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01904","created_at":"2026-07-05T10:09:51Z"},{"alias_kind":"pith_short_12","alias_value":"VUWH5DO632VW","created_at":"2026-07-05T10:09:51Z"},{"alias_kind":"pith_short_16","alias_value":"VUWH5DO632VWXI7X","created_at":"2026-07-05T10:09:51Z"},{"alias_kind":"pith_short_8","alias_value":"VUWH5DO6","created_at":"2026-07-05T10:09:51Z"}],"graph_snapshots":[{"event_id":"sha256:aed24608492c87e4d8820950253dc6c92a86b5206fa0b3ebbff79352dbdaea43","target":"graph","created_at":"2026-07-05T10:09:51Z","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/2501.01904/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, slow-thinking reasoning systems, built upon large language models (LLMs), have garnered widespread attention by scaling the thinking time during inference. There is also growing interest in adapting this capability to multimodal large language models (MLLMs). Given that MLLMs handle more complex data semantics across different modalities, it is intuitively more challenging to implement multimodal slow-thinking systems.\n  To address this issue, in this paper, we explore a straightforward approach by fine-tuning a capable MLLM with a small amount of textual long-form thought data, resu","authors_text":"Bingning Wang, Ji-Rong Wen, Wayne Xin Zhao, Weipeng Chen, Yifan Du, Yifan Li, Yuqi Huo, Zheng Liu, Zhongyuan Wang, Zikang Liu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-03T17:14:16Z","title":"Virgo: A Preliminary Exploration on Reproducing o1-like MLLM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01904","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":"sha256:29a2753ec341031f94beecd60d40e90d193e69d63f36ea4cbeee8fb65620ec5e","target":"record","created_at":"2026-07-05T10:09:51Z","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":"c7a48a45d52693c0fd427f439b80cf8192218576ee4676d5b514a2190280bd17","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-03T17:14:16Z","title_canon_sha256":"84ca0ef266a641e88842bd70425050200b3c534bf7ea27e5494e8a289fc720f2"},"schema_version":"1.0","source":{"id":"2501.01904","kind":"arxiv","version":2}},"canonical_sha256":"ad2c7e8ddedeab6ba3f722e1514fd782af1e4c59c506d0aec20fbc93d97bdbc8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ad2c7e8ddedeab6ba3f722e1514fd782af1e4c59c506d0aec20fbc93d97bdbc8","first_computed_at":"2026-07-05T10:09:51.043902Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:09:51.043902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8RDtXkaEnBybbHYnlIQp6EHa8gDlsteG+mPgw9n4BP/8jVR5KBxGVpKMyqeGykjOfqnLNqyOZyTtwiTowsX7Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:09:51.044408Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.01904","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:29a2753ec341031f94beecd60d40e90d193e69d63f36ea4cbeee8fb65620ec5e","sha256:aed24608492c87e4d8820950253dc6c92a86b5206fa0b3ebbff79352dbdaea43"],"state_sha256":"f7a1729e4e844c839730fab33b662ff12cb05a993ac0b1270eece40edd05ccab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mpA4Uy+J6b8kqz7qoJ1Zg48UveH1y3jggP5f9MrLYrb5jWDWFkv7EG8eC1UVNs+qXXtGx5IiQ9qp7zq+vRsWAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T21:14:29.492943Z","bundle_sha256":"75d484ecf0cf2fe61477d88adcf4f0119674072775b4ae83cca51e5032692dbe"}}