{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:B65QQBDKM4IS3OJCNCEUA5ORKU","short_pith_number":"pith:B65QQBDK","canonical_record":{"source":{"id":"2407.16312","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2024-07-23T09:05:06Z","cross_cats_sorted":["cs.AI","cs.GT"],"title_canon_sha256":"2116183261b27bebdd46f95a3e96071a2b892eb5705d5502d6c5f115f7efd3a4","abstract_canon_sha256":"c8fcc10c1dcdbb64ccb3fd84d74e06f2620110161f911c6622ee022e4e4ed0c4"},"schema_version":"1.0"},"canonical_sha256":"0fbb08046a67112db92268894075d1552976cb72f4eeaf6f11dd86978e3f67bd","source":{"kind":"arxiv","id":"2407.16312","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.16312","created_at":"2026-07-05T09:26:29Z"},{"alias_kind":"arxiv_version","alias_value":"2407.16312v2","created_at":"2026-07-05T09:26:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.16312","created_at":"2026-07-05T09:26:29Z"},{"alias_kind":"pith_short_12","alias_value":"B65QQBDKM4IS","created_at":"2026-07-05T09:26:29Z"},{"alias_kind":"pith_short_16","alias_value":"B65QQBDKM4IS3OJC","created_at":"2026-07-05T09:26:29Z"},{"alias_kind":"pith_short_8","alias_value":"B65QQBDK","created_at":"2026-07-05T09:26:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:B65QQBDKM4IS3OJCNCEUA5ORKU","target":"record","payload":{"canonical_record":{"source":{"id":"2407.16312","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2024-07-23T09:05:06Z","cross_cats_sorted":["cs.AI","cs.GT"],"title_canon_sha256":"2116183261b27bebdd46f95a3e96071a2b892eb5705d5502d6c5f115f7efd3a4","abstract_canon_sha256":"c8fcc10c1dcdbb64ccb3fd84d74e06f2620110161f911c6622ee022e4e4ed0c4"},"schema_version":"1.0"},"canonical_sha256":"0fbb08046a67112db92268894075d1552976cb72f4eeaf6f11dd86978e3f67bd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:26:29.233116Z","signature_b64":"CNcu9ievh9wiRwrElHJhVmtLSsWO7ZrPWT+YmMA4uIwgFEOo5pecpkSW5FeWjYX4liE7AMDj9vVVCzSzXTBlAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0fbb08046a67112db92268894075d1552976cb72f4eeaf6f11dd86978e3f67bd","last_reissued_at":"2026-07-05T09:26:29.232613Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:26:29.232613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.16312","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":"2026-07-05T09:26:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fQW6wrp3z7MWSmBLY3WP6/Ecz0Vc6cWYPO/Q/fIGZztC5G+pcdpzvXpI+NVGI8Atvc36NlJ3ClMMvYRa62+SAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T06:15:02.284549Z"},"content_sha256":"343032171107ec9b862b932ba8d20efd2bf642c6cf57e23f19d436fa963d0726","schema_version":"1.0","event_id":"sha256:343032171107ec9b862b932ba8d20efd2bf642c6cf57e23f19d436fa963d0726"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:B65QQBDKM4IS3OJCNCEUA5ORKU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GT"],"primary_cat":"cs.MA","authors_text":"Ann Now\\'e, Diederik M. Roijers, El-Ghazali Talbi, Florian Felten, Gao Peng, Gr\\'egoire Danoy, Hendrik Baier, Hicham Azmani, Jordan K. Terry, Patrick Mannion, Roxana R\\u{a}dulescu, Umut Ucak, Willem R\\\"opke","submitted_at":"2024-07-23T09:05:06Z","abstract_excerpt":"Many challenging tasks such as managing traffic systems, electricity grids, or supply chains involve complex decision-making processes that must balance multiple conflicting objectives and coordinate the actions of various independent decision-makers (DMs). One perspective for formalising and addressing such tasks is multi-objective multi-agent reinforcement learning (MOMARL). MOMARL broadens reinforcement learning (RL) to problems with multiple agents each needing to consider multiple objectives in their learning process. In reinforcement learning research, benchmarks are crucial in facilitat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.16312","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/2407.16312/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":"2026-07-05T09:26:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HU/1cS4518edQU+c+mCXr+xwLBskRZWpkiUM3bjUFsa7is28QGcLFcZL2Pvp3aQqFnGpumy++OrRibY4RL3NAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T06:15:02.284932Z"},"content_sha256":"0d7b6c7b8c9e93da4561f87d0179f442b85f0735931ca37ec6db768daef75eab","schema_version":"1.0","event_id":"sha256:0d7b6c7b8c9e93da4561f87d0179f442b85f0735931ca37ec6db768daef75eab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B65QQBDKM4IS3OJCNCEUA5ORKU/bundle.json","state_url":"https://pith.science/pith/B65QQBDKM4IS3OJCNCEUA5ORKU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B65QQBDKM4IS3OJCNCEUA5ORKU/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":"2026-07-07T06:15:02Z","links":{"resolver":"https://pith.science/pith/B65QQBDKM4IS3OJCNCEUA5ORKU","bundle":"https://pith.science/pith/B65QQBDKM4IS3OJCNCEUA5ORKU/bundle.json","state":"https://pith.science/pith/B65QQBDKM4IS3OJCNCEUA5ORKU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B65QQBDKM4IS3OJCNCEUA5ORKU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:B65QQBDKM4IS3OJCNCEUA5ORKU","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":"c8fcc10c1dcdbb64ccb3fd84d74e06f2620110161f911c6622ee022e4e4ed0c4","cross_cats_sorted":["cs.AI","cs.GT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2024-07-23T09:05:06Z","title_canon_sha256":"2116183261b27bebdd46f95a3e96071a2b892eb5705d5502d6c5f115f7efd3a4"},"schema_version":"1.0","source":{"id":"2407.16312","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.16312","created_at":"2026-07-05T09:26:29Z"},{"alias_kind":"arxiv_version","alias_value":"2407.16312v2","created_at":"2026-07-05T09:26:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.16312","created_at":"2026-07-05T09:26:29Z"},{"alias_kind":"pith_short_12","alias_value":"B65QQBDKM4IS","created_at":"2026-07-05T09:26:29Z"},{"alias_kind":"pith_short_16","alias_value":"B65QQBDKM4IS3OJC","created_at":"2026-07-05T09:26:29Z"},{"alias_kind":"pith_short_8","alias_value":"B65QQBDK","created_at":"2026-07-05T09:26:29Z"}],"graph_snapshots":[{"event_id":"sha256:0d7b6c7b8c9e93da4561f87d0179f442b85f0735931ca37ec6db768daef75eab","target":"graph","created_at":"2026-07-05T09:26:29Z","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/2407.16312/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many challenging tasks such as managing traffic systems, electricity grids, or supply chains involve complex decision-making processes that must balance multiple conflicting objectives and coordinate the actions of various independent decision-makers (DMs). One perspective for formalising and addressing such tasks is multi-objective multi-agent reinforcement learning (MOMARL). MOMARL broadens reinforcement learning (RL) to problems with multiple agents each needing to consider multiple objectives in their learning process. In reinforcement learning research, benchmarks are crucial in facilitat","authors_text":"Ann Now\\'e, Diederik M. Roijers, El-Ghazali Talbi, Florian Felten, Gao Peng, Gr\\'egoire Danoy, Hendrik Baier, Hicham Azmani, Jordan K. Terry, Patrick Mannion, Roxana R\\u{a}dulescu, Umut Ucak, Willem R\\\"opke","cross_cats":["cs.AI","cs.GT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2024-07-23T09:05:06Z","title":"MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.16312","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":"sha256:343032171107ec9b862b932ba8d20efd2bf642c6cf57e23f19d436fa963d0726","target":"record","created_at":"2026-07-05T09:26:29Z","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":"c8fcc10c1dcdbb64ccb3fd84d74e06f2620110161f911c6622ee022e4e4ed0c4","cross_cats_sorted":["cs.AI","cs.GT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2024-07-23T09:05:06Z","title_canon_sha256":"2116183261b27bebdd46f95a3e96071a2b892eb5705d5502d6c5f115f7efd3a4"},"schema_version":"1.0","source":{"id":"2407.16312","kind":"arxiv","version":2}},"canonical_sha256":"0fbb08046a67112db92268894075d1552976cb72f4eeaf6f11dd86978e3f67bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0fbb08046a67112db92268894075d1552976cb72f4eeaf6f11dd86978e3f67bd","first_computed_at":"2026-07-05T09:26:29.232613Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:26:29.232613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CNcu9ievh9wiRwrElHJhVmtLSsWO7ZrPWT+YmMA4uIwgFEOo5pecpkSW5FeWjYX4liE7AMDj9vVVCzSzXTBlAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:26:29.233116Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.16312","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:343032171107ec9b862b932ba8d20efd2bf642c6cf57e23f19d436fa963d0726","sha256:0d7b6c7b8c9e93da4561f87d0179f442b85f0735931ca37ec6db768daef75eab"],"state_sha256":"d0b1a76ec6594f5c2f04eab4e8ad71609dd532084db6ef25796043c9c5c01092"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"90heevflxnhgHBPzxoF1JkjkZYLiukC/p1vG31oEzgSRkkHDlYpci0eeTrefQafBw/brzUKqV6DthzUvwTGoDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T06:15:02.286988Z","bundle_sha256":"c336bd76a31d0a7041cdde6b00e0a5c72f81f99cc23b9492fea6a820af424ada"}}