{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:Z2P3NGRKBOUXGRU3OR5TF47Z3P","short_pith_number":"pith:Z2P3NGRK","canonical_record":{"source":{"id":"2506.18679","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-23T14:22:49Z","cross_cats_sorted":[],"title_canon_sha256":"7ab1e918eff0b4249cbd24f8c081a50941994125fd4dd79450b1269d107ff3aa","abstract_canon_sha256":"d5990db5de56e1ffee2bfbf25eef15bfbeea5ab9dc9929155b8931799c2b160f"},"schema_version":"1.0"},"canonical_sha256":"ce9fb69a2a0ba973469b747b32f3f9dbe760ca0c7c32df39be1b77169089bce8","source":{"kind":"arxiv","id":"2506.18679","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.18679","created_at":"2026-07-05T11:37:24Z"},{"alias_kind":"arxiv_version","alias_value":"2506.18679v2","created_at":"2026-07-05T11:37:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18679","created_at":"2026-07-05T11:37:24Z"},{"alias_kind":"pith_short_12","alias_value":"Z2P3NGRKBOUX","created_at":"2026-07-05T11:37:24Z"},{"alias_kind":"pith_short_16","alias_value":"Z2P3NGRKBOUXGRU3","created_at":"2026-07-05T11:37:24Z"},{"alias_kind":"pith_short_8","alias_value":"Z2P3NGRK","created_at":"2026-07-05T11:37:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:Z2P3NGRKBOUXGRU3OR5TF47Z3P","target":"record","payload":{"canonical_record":{"source":{"id":"2506.18679","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-23T14:22:49Z","cross_cats_sorted":[],"title_canon_sha256":"7ab1e918eff0b4249cbd24f8c081a50941994125fd4dd79450b1269d107ff3aa","abstract_canon_sha256":"d5990db5de56e1ffee2bfbf25eef15bfbeea5ab9dc9929155b8931799c2b160f"},"schema_version":"1.0"},"canonical_sha256":"ce9fb69a2a0ba973469b747b32f3f9dbe760ca0c7c32df39be1b77169089bce8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:37:24.216463Z","signature_b64":"pWHIiE/0XftCNLPk5BFLuqV31osK6Y4Egj/uSDUmdwtW5tuekMAYrz682QdvrBI062w4Zl7NhMPKjkwaar0iDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce9fb69a2a0ba973469b747b32f3f9dbe760ca0c7c32df39be1b77169089bce8","last_reissued_at":"2026-07-05T11:37:24.215973Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:37:24.215973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.18679","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":"2026-07-05T11:37:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aP/I4KxJ8PLdPJApp4rLNBpfWscrZR0yk6mS4I4VViB4G/ba8sjHP0VJik9/PSc71Pm0XLrA6fp6kdWJzbKZAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T13:14:46.879990Z"},"content_sha256":"6d1c4bbe7e6d22cea814fbf950a7893769433a6d7f794c4ebedf4557fd390f05","schema_version":"1.0","event_id":"sha256:6d1c4bbe7e6d22cea814fbf950a7893769433a6d7f794c4ebedf4557fd390f05"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:Z2P3NGRKBOUXGRU3OR5TF47Z3P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Au Hoi Fan, Haowei Guo, Jinai Li, Puxin Yan, Ruicheng Zhang, Xiaofan Liu, Yu Sun, Zeyu Zhang","submitted_at":"2025-06-23T14:22:49Z","abstract_excerpt":"We introduce MARL-MambaContour, the first contour-based medical image segmentation framework based on Multi-Agent Reinforcement Learning (MARL). Our approach reframes segmentation as a multi-agent cooperation task focused on generate topologically consistent object-level contours, addressing the limitations of traditional pixel-based methods which could lack topological constraints and holistic structural awareness of anatomical regions. Each contour point is modeled as an autonomous agent that iteratively adjusts its position to align precisely with the target boundary, enabling adaptation to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18679","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/2506.18679/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":"2026-07-05T11:37:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ppgzpp9GiLUMlkRfkgKBhQWLcpafywLzDOWcPzA+H5aM/KN5a6+e59MMxgPE8BXgj1VOembH8NKjW3k4tsQ8DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T13:14:46.880559Z"},"content_sha256":"d45cea47cd65ebafca85a227596395ae3c8e00c86796e5cbd8d70ac4745787b9","schema_version":"1.0","event_id":"sha256:d45cea47cd65ebafca85a227596395ae3c8e00c86796e5cbd8d70ac4745787b9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z2P3NGRKBOUXGRU3OR5TF47Z3P/bundle.json","state_url":"https://pith.science/pith/Z2P3NGRKBOUXGRU3OR5TF47Z3P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z2P3NGRKBOUXGRU3OR5TF47Z3P/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":"2026-08-07T13:14:46Z","links":{"resolver":"https://pith.science/pith/Z2P3NGRKBOUXGRU3OR5TF47Z3P","bundle":"https://pith.science/pith/Z2P3NGRKBOUXGRU3OR5TF47Z3P/bundle.json","state":"https://pith.science/pith/Z2P3NGRKBOUXGRU3OR5TF47Z3P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z2P3NGRKBOUXGRU3OR5TF47Z3P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:Z2P3NGRKBOUXGRU3OR5TF47Z3P","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":"d5990db5de56e1ffee2bfbf25eef15bfbeea5ab9dc9929155b8931799c2b160f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-23T14:22:49Z","title_canon_sha256":"7ab1e918eff0b4249cbd24f8c081a50941994125fd4dd79450b1269d107ff3aa"},"schema_version":"1.0","source":{"id":"2506.18679","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.18679","created_at":"2026-07-05T11:37:24Z"},{"alias_kind":"arxiv_version","alias_value":"2506.18679v2","created_at":"2026-07-05T11:37:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18679","created_at":"2026-07-05T11:37:24Z"},{"alias_kind":"pith_short_12","alias_value":"Z2P3NGRKBOUX","created_at":"2026-07-05T11:37:24Z"},{"alias_kind":"pith_short_16","alias_value":"Z2P3NGRKBOUXGRU3","created_at":"2026-07-05T11:37:24Z"},{"alias_kind":"pith_short_8","alias_value":"Z2P3NGRK","created_at":"2026-07-05T11:37:24Z"}],"graph_snapshots":[{"event_id":"sha256:d45cea47cd65ebafca85a227596395ae3c8e00c86796e5cbd8d70ac4745787b9","target":"graph","created_at":"2026-07-05T11:37:24Z","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/2506.18679/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce MARL-MambaContour, the first contour-based medical image segmentation framework based on Multi-Agent Reinforcement Learning (MARL). Our approach reframes segmentation as a multi-agent cooperation task focused on generate topologically consistent object-level contours, addressing the limitations of traditional pixel-based methods which could lack topological constraints and holistic structural awareness of anatomical regions. Each contour point is modeled as an autonomous agent that iteratively adjusts its position to align precisely with the target boundary, enabling adaptation to","authors_text":"Au Hoi Fan, Haowei Guo, Jinai Li, Puxin Yan, Ruicheng Zhang, Xiaofan Liu, Yu Sun, Zeyu Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-23T14:22:49Z","title":"MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18679","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":"sha256:6d1c4bbe7e6d22cea814fbf950a7893769433a6d7f794c4ebedf4557fd390f05","target":"record","created_at":"2026-07-05T11:37:24Z","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":"d5990db5de56e1ffee2bfbf25eef15bfbeea5ab9dc9929155b8931799c2b160f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-23T14:22:49Z","title_canon_sha256":"7ab1e918eff0b4249cbd24f8c081a50941994125fd4dd79450b1269d107ff3aa"},"schema_version":"1.0","source":{"id":"2506.18679","kind":"arxiv","version":2}},"canonical_sha256":"ce9fb69a2a0ba973469b747b32f3f9dbe760ca0c7c32df39be1b77169089bce8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ce9fb69a2a0ba973469b747b32f3f9dbe760ca0c7c32df39be1b77169089bce8","first_computed_at":"2026-07-05T11:37:24.215973Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:37:24.215973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pWHIiE/0XftCNLPk5BFLuqV31osK6Y4Egj/uSDUmdwtW5tuekMAYrz682QdvrBI062w4Zl7NhMPKjkwaar0iDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:37:24.216463Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.18679","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6d1c4bbe7e6d22cea814fbf950a7893769433a6d7f794c4ebedf4557fd390f05","sha256:d45cea47cd65ebafca85a227596395ae3c8e00c86796e5cbd8d70ac4745787b9"],"state_sha256":"deb5c1eca6566f5c40d1ea8fee66cae14a84f8a7ed3b794b9f95e55e8c14d1ca"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+fMz7wJ8nPaQ4bxZeJF/qDf10cuJuohOybhFP+S+EXJe5Bisq1p1qCSQyPhfunQBptTK9K2I6guEAJ/D04nDCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T13:14:46.886615Z","bundle_sha256":"206918bcbe197b8e162899dc4e71dce35b3e5744b1a0248aeae66aea171dedef"}}