{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:D5HJLOWA4BESQBFAEMZDKI3262","short_pith_number":"pith:D5HJLOWA","canonical_record":{"source":{"id":"2504.01324","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-02T03:18:24Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"76dcd2611cfbe7ddaff6aab49f57bde8b846035438a17927a7a7a8be19301cbb","abstract_canon_sha256":"6cb8ce9a5784ea7cec87340921c586478c496ff7e5df436d0be6320b164fe601"},"schema_version":"1.0"},"canonical_sha256":"1f4e95bac0e0492804a0233235237af68e4e36e5bc59201f5ee90d13c6c0d897","source":{"kind":"arxiv","id":"2504.01324","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.01324","created_at":"2026-07-05T10:43:12Z"},{"alias_kind":"arxiv_version","alias_value":"2504.01324v1","created_at":"2026-07-05T10:43:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.01324","created_at":"2026-07-05T10:43:12Z"},{"alias_kind":"pith_short_12","alias_value":"D5HJLOWA4BES","created_at":"2026-07-05T10:43:12Z"},{"alias_kind":"pith_short_16","alias_value":"D5HJLOWA4BESQBFA","created_at":"2026-07-05T10:43:12Z"},{"alias_kind":"pith_short_8","alias_value":"D5HJLOWA","created_at":"2026-07-05T10:43:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:D5HJLOWA4BESQBFAEMZDKI3262","target":"record","payload":{"canonical_record":{"source":{"id":"2504.01324","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-02T03:18:24Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"76dcd2611cfbe7ddaff6aab49f57bde8b846035438a17927a7a7a8be19301cbb","abstract_canon_sha256":"6cb8ce9a5784ea7cec87340921c586478c496ff7e5df436d0be6320b164fe601"},"schema_version":"1.0"},"canonical_sha256":"1f4e95bac0e0492804a0233235237af68e4e36e5bc59201f5ee90d13c6c0d897","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:43:12.994436Z","signature_b64":"FVsAbRenIKQ27C0wDFeiRX3pK0cCB3ctoZBiFTBBIoRASKNDUCWDaHzgA5svSYZsWVrdzKho49Ka7a9bldwkDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f4e95bac0e0492804a0233235237af68e4e36e5bc59201f5ee90d13c6c0d897","last_reissued_at":"2026-07-05T10:43:12.993959Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:43:12.993959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.01324","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":"2026-07-05T10:43:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"50PmQ7w+q+MQsOH7yYj9UaiI0iUXjwoAGourNtSrKJ5bl0Q3QwtOSOO034WvDCBlwG4EWRb5DTWoLHfqQGgKBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T04:47:07.068034Z"},"content_sha256":"ac44bf31fd05008759e01010346ba724699185b5eb41f7640bf044f406da08b6","schema_version":"1.0","event_id":"sha256:ac44bf31fd05008759e01010346ba724699185b5eb41f7640bf044f406da08b6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:D5HJLOWA4BESQBFAEMZDKI3262","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On Data Synthesis and Post-training for Visual Abstract Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CV","authors_text":"Gang Zhang, Jiangjiang Liu, Ke Zhu, Qunyi Xie, Shanshan Liu, Yu Wang","submitted_at":"2025-04-02T03:18:24Z","abstract_excerpt":"This paper is a pioneering work attempting to address abstract visual reasoning (AVR) problems for large vision-language models (VLMs). We make a common LLaVA-NeXT 7B model capable of perceiving and reasoning about specific AVR problems, surpassing both open-sourced (e.g., Qwen-2-VL-72B) and closed-sourced powerful VLMs (e.g., GPT-4o) with significant margin. This is a great breakthrough since almost all previous VLMs fail or show nearly random performance on representative AVR benchmarks. Our key success is our innovative data synthesis and post-training process, aiming to fully relieve the t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.01324","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/2504.01324/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":"2026-07-05T10:43:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4li7fKnkhF3SmDHPeOSSG0azvEVZ6/H1Re+ui3GZNMmls+MIzRzyA8Y+Qpm4A41r5RCU7LJrGp21BPsISHxBCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T04:47:07.068565Z"},"content_sha256":"d8e9bac2ab932709b21411ae19434fbf57952b73513dc8c15fc9a56c99fdf254","schema_version":"1.0","event_id":"sha256:d8e9bac2ab932709b21411ae19434fbf57952b73513dc8c15fc9a56c99fdf254"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D5HJLOWA4BESQBFAEMZDKI3262/bundle.json","state_url":"https://pith.science/pith/D5HJLOWA4BESQBFAEMZDKI3262/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D5HJLOWA4BESQBFAEMZDKI3262/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":"2026-08-07T04:47:07Z","links":{"resolver":"https://pith.science/pith/D5HJLOWA4BESQBFAEMZDKI3262","bundle":"https://pith.science/pith/D5HJLOWA4BESQBFAEMZDKI3262/bundle.json","state":"https://pith.science/pith/D5HJLOWA4BESQBFAEMZDKI3262/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D5HJLOWA4BESQBFAEMZDKI3262/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:D5HJLOWA4BESQBFAEMZDKI3262","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":"6cb8ce9a5784ea7cec87340921c586478c496ff7e5df436d0be6320b164fe601","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-02T03:18:24Z","title_canon_sha256":"76dcd2611cfbe7ddaff6aab49f57bde8b846035438a17927a7a7a8be19301cbb"},"schema_version":"1.0","source":{"id":"2504.01324","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.01324","created_at":"2026-07-05T10:43:12Z"},{"alias_kind":"arxiv_version","alias_value":"2504.01324v1","created_at":"2026-07-05T10:43:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.01324","created_at":"2026-07-05T10:43:12Z"},{"alias_kind":"pith_short_12","alias_value":"D5HJLOWA4BES","created_at":"2026-07-05T10:43:12Z"},{"alias_kind":"pith_short_16","alias_value":"D5HJLOWA4BESQBFA","created_at":"2026-07-05T10:43:12Z"},{"alias_kind":"pith_short_8","alias_value":"D5HJLOWA","created_at":"2026-07-05T10:43:12Z"}],"graph_snapshots":[{"event_id":"sha256:d8e9bac2ab932709b21411ae19434fbf57952b73513dc8c15fc9a56c99fdf254","target":"graph","created_at":"2026-07-05T10:43:12Z","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/2504.01324/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper is a pioneering work attempting to address abstract visual reasoning (AVR) problems for large vision-language models (VLMs). We make a common LLaVA-NeXT 7B model capable of perceiving and reasoning about specific AVR problems, surpassing both open-sourced (e.g., Qwen-2-VL-72B) and closed-sourced powerful VLMs (e.g., GPT-4o) with significant margin. This is a great breakthrough since almost all previous VLMs fail or show nearly random performance on representative AVR benchmarks. Our key success is our innovative data synthesis and post-training process, aiming to fully relieve the t","authors_text":"Gang Zhang, Jiangjiang Liu, Ke Zhu, Qunyi Xie, Shanshan Liu, Yu Wang","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-02T03:18:24Z","title":"On Data Synthesis and Post-training for Visual Abstract Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.01324","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:ac44bf31fd05008759e01010346ba724699185b5eb41f7640bf044f406da08b6","target":"record","created_at":"2026-07-05T10:43:12Z","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":"6cb8ce9a5784ea7cec87340921c586478c496ff7e5df436d0be6320b164fe601","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-02T03:18:24Z","title_canon_sha256":"76dcd2611cfbe7ddaff6aab49f57bde8b846035438a17927a7a7a8be19301cbb"},"schema_version":"1.0","source":{"id":"2504.01324","kind":"arxiv","version":1}},"canonical_sha256":"1f4e95bac0e0492804a0233235237af68e4e36e5bc59201f5ee90d13c6c0d897","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1f4e95bac0e0492804a0233235237af68e4e36e5bc59201f5ee90d13c6c0d897","first_computed_at":"2026-07-05T10:43:12.993959Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:43:12.993959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FVsAbRenIKQ27C0wDFeiRX3pK0cCB3ctoZBiFTBBIoRASKNDUCWDaHzgA5svSYZsWVrdzKho49Ka7a9bldwkDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:43:12.994436Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.01324","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ac44bf31fd05008759e01010346ba724699185b5eb41f7640bf044f406da08b6","sha256:d8e9bac2ab932709b21411ae19434fbf57952b73513dc8c15fc9a56c99fdf254"],"state_sha256":"f41cf75367b47e1babd7a55eed69a3029f814a9b1ed20c36aa3e7a0cfb549ea1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aSPyvwp2J+C45PS2K31OkueoZ/3pkrDumBd4tpSYR8Fr9q4S9dXyLH5GPDpnwjvy8swSOKKwXZfTCVsuQhoDCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T04:47:07.072391Z","bundle_sha256":"3620e436647d4e4c1076f520b7724ebae21f5e20baf247361ae452a0439873d4"}}