{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:YAKOOMKQMAF3P42NKNURR3QGDB","short_pith_number":"pith:YAKOOMKQ","canonical_record":{"source":{"id":"2306.07542","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-06-13T05:22:30Z","cross_cats_sorted":[],"title_canon_sha256":"fdd6517907e122894bb9f55f888c31dfbfd5569a6ea887f98306dbcc817bb6e7","abstract_canon_sha256":"55735cb78f5c28734948c51d2498ea05df572e45d4bc46187bd949abdd41dd0e"},"schema_version":"1.0"},"canonical_sha256":"c014e73150600bb7f34d536918ee06187a788ebee4a861eec5feea81f832e8a2","source":{"kind":"arxiv","id":"2306.07542","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.07542","created_at":"2026-07-05T06:20:06Z"},{"alias_kind":"arxiv_version","alias_value":"2306.07542v1","created_at":"2026-07-05T06:20:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.07542","created_at":"2026-07-05T06:20:06Z"},{"alias_kind":"pith_short_12","alias_value":"YAKOOMKQMAF3","created_at":"2026-07-05T06:20:06Z"},{"alias_kind":"pith_short_16","alias_value":"YAKOOMKQMAF3P42N","created_at":"2026-07-05T06:20:06Z"},{"alias_kind":"pith_short_8","alias_value":"YAKOOMKQ","created_at":"2026-07-05T06:20:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:YAKOOMKQMAF3P42NKNURR3QGDB","target":"record","payload":{"canonical_record":{"source":{"id":"2306.07542","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-06-13T05:22:30Z","cross_cats_sorted":[],"title_canon_sha256":"fdd6517907e122894bb9f55f888c31dfbfd5569a6ea887f98306dbcc817bb6e7","abstract_canon_sha256":"55735cb78f5c28734948c51d2498ea05df572e45d4bc46187bd949abdd41dd0e"},"schema_version":"1.0"},"canonical_sha256":"c014e73150600bb7f34d536918ee06187a788ebee4a861eec5feea81f832e8a2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:20:06.046234Z","signature_b64":"PbhXyewSJ3iqBcNJJa6DVJ/tptrqGvORRjjgsw8PIsujVoMdpeVfXhZJ/PLC2RXIZhsj8KtkrxJG5446S5MNAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c014e73150600bb7f34d536918ee06187a788ebee4a861eec5feea81f832e8a2","last_reissued_at":"2026-07-05T06:20:06.045832Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:20:06.045832Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.07542","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":"2026-07-05T06:20:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CrAw/b2LST3rxBpDs6+1eerx9PkmKLIHJwILCSFzpOjecSvV2lIW92TCtwTvZHb+NlWfNgImaveIaNShQ9qXDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T07:39:07.940778Z"},"content_sha256":"a3e2105be73b44d6550c10d7a461235579f0e1e80387a6935c259b275acc3dc7","schema_version":"1.0","event_id":"sha256:a3e2105be73b44d6550c10d7a461235579f0e1e80387a6935c259b275acc3dc7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:YAKOOMKQMAF3P42NKNURR3QGDB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chuheng Zhang, Jiang Bian, Lei Song, Li Zhao, Wei Jiang, Xianliang Yang, Zhihao Liu","submitted_at":"2023-06-13T05:22:30Z","abstract_excerpt":"Multi-agent reinforcement learning (MARL) models multiple agents that interact and learn within a shared environment. This paradigm is applicable to various industrial scenarios such as autonomous driving, quantitative trading, and inventory management. However, applying MARL to these real-world scenarios is impeded by many challenges such as scaling up, complex agent interactions, and non-stationary dynamics. To incentivize the research of MARL on these challenges, we develop MABIM (Multi-Agent Benchmark for Inventory Management) which is a multi-echelon, multi-commodity inventory management "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.07542","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/2306.07542/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":"2026-07-05T06:20:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cOLrEXoSR6OBxDpIsosOhlPytIGP924sPuN+rH+2QvtAw8mzfYaJEn5r0bQMfCMkez9rAOFjs/KGyiWvn8TUBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T07:39:07.941735Z"},"content_sha256":"7764505a8c6d1b63a7c5e5d6351915ed6ad2f912fdb3f827f731d234e95746b6","schema_version":"1.0","event_id":"sha256:7764505a8c6d1b63a7c5e5d6351915ed6ad2f912fdb3f827f731d234e95746b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YAKOOMKQMAF3P42NKNURR3QGDB/bundle.json","state_url":"https://pith.science/pith/YAKOOMKQMAF3P42NKNURR3QGDB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YAKOOMKQMAF3P42NKNURR3QGDB/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":"2026-08-12T07:39:07Z","links":{"resolver":"https://pith.science/pith/YAKOOMKQMAF3P42NKNURR3QGDB","bundle":"https://pith.science/pith/YAKOOMKQMAF3P42NKNURR3QGDB/bundle.json","state":"https://pith.science/pith/YAKOOMKQMAF3P42NKNURR3QGDB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YAKOOMKQMAF3P42NKNURR3QGDB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YAKOOMKQMAF3P42NKNURR3QGDB","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":"55735cb78f5c28734948c51d2498ea05df572e45d4bc46187bd949abdd41dd0e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-06-13T05:22:30Z","title_canon_sha256":"fdd6517907e122894bb9f55f888c31dfbfd5569a6ea887f98306dbcc817bb6e7"},"schema_version":"1.0","source":{"id":"2306.07542","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.07542","created_at":"2026-07-05T06:20:06Z"},{"alias_kind":"arxiv_version","alias_value":"2306.07542v1","created_at":"2026-07-05T06:20:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.07542","created_at":"2026-07-05T06:20:06Z"},{"alias_kind":"pith_short_12","alias_value":"YAKOOMKQMAF3","created_at":"2026-07-05T06:20:06Z"},{"alias_kind":"pith_short_16","alias_value":"YAKOOMKQMAF3P42N","created_at":"2026-07-05T06:20:06Z"},{"alias_kind":"pith_short_8","alias_value":"YAKOOMKQ","created_at":"2026-07-05T06:20:06Z"}],"graph_snapshots":[{"event_id":"sha256:7764505a8c6d1b63a7c5e5d6351915ed6ad2f912fdb3f827f731d234e95746b6","target":"graph","created_at":"2026-07-05T06:20:06Z","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/2306.07542/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-agent reinforcement learning (MARL) models multiple agents that interact and learn within a shared environment. This paradigm is applicable to various industrial scenarios such as autonomous driving, quantitative trading, and inventory management. However, applying MARL to these real-world scenarios is impeded by many challenges such as scaling up, complex agent interactions, and non-stationary dynamics. To incentivize the research of MARL on these challenges, we develop MABIM (Multi-Agent Benchmark for Inventory Management) which is a multi-echelon, multi-commodity inventory management ","authors_text":"Chuheng Zhang, Jiang Bian, Lei Song, Li Zhao, Wei Jiang, Xianliang Yang, Zhihao Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-06-13T05:22:30Z","title":"A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.07542","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:a3e2105be73b44d6550c10d7a461235579f0e1e80387a6935c259b275acc3dc7","target":"record","created_at":"2026-07-05T06:20:06Z","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":"55735cb78f5c28734948c51d2498ea05df572e45d4bc46187bd949abdd41dd0e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-06-13T05:22:30Z","title_canon_sha256":"fdd6517907e122894bb9f55f888c31dfbfd5569a6ea887f98306dbcc817bb6e7"},"schema_version":"1.0","source":{"id":"2306.07542","kind":"arxiv","version":1}},"canonical_sha256":"c014e73150600bb7f34d536918ee06187a788ebee4a861eec5feea81f832e8a2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c014e73150600bb7f34d536918ee06187a788ebee4a861eec5feea81f832e8a2","first_computed_at":"2026-07-05T06:20:06.045832Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:20:06.045832Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PbhXyewSJ3iqBcNJJa6DVJ/tptrqGvORRjjgsw8PIsujVoMdpeVfXhZJ/PLC2RXIZhsj8KtkrxJG5446S5MNAg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:20:06.046234Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.07542","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a3e2105be73b44d6550c10d7a461235579f0e1e80387a6935c259b275acc3dc7","sha256:7764505a8c6d1b63a7c5e5d6351915ed6ad2f912fdb3f827f731d234e95746b6"],"state_sha256":"836f99705892beecf7f72ffcec43aa2b46aac9d3adf09442a810036ee05363b5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GF5iLg2E4MY+f7gHrY49taQiTLugr9f+RFUNGpGPl11KFT1IxxmWv63lA73y0uu+HZOib8BzyhmPvLSigRJuCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T07:39:07.947150Z","bundle_sha256":"4613ab7615eb66e8b8ae2620fe805a0d069608df0b7a4803a0fc07029a9b5748"}}