{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:RCCXMIFCGJVM7NKZOTA5SPOEPB","short_pith_number":"pith:RCCXMIFC","canonical_record":{"source":{"id":"2407.08022","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2024-07-10T20:00:22Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"83442a07045423305575da7aabbaf9314d7d2e24d6a80d4d25730c7ebbbb39fc","abstract_canon_sha256":"d60d847c6473ce62849d5514751be255777dfe5713f72c1c35255fa7495498b4"},"schema_version":"1.0"},"canonical_sha256":"88857620a2326acfb55974c1d93dc47842ea7fb268a2de7b8d1fd9d7cceac730","source":{"kind":"arxiv","id":"2407.08022","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.08022","created_at":"2026-07-05T08:42:41Z"},{"alias_kind":"arxiv_version","alias_value":"2407.08022v1","created_at":"2026-07-05T08:42:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.08022","created_at":"2026-07-05T08:42:41Z"},{"alias_kind":"pith_short_12","alias_value":"RCCXMIFCGJVM","created_at":"2026-07-05T08:42:41Z"},{"alias_kind":"pith_short_16","alias_value":"RCCXMIFCGJVM7NKZ","created_at":"2026-07-05T08:42:41Z"},{"alias_kind":"pith_short_8","alias_value":"RCCXMIFC","created_at":"2026-07-05T08:42:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:RCCXMIFCGJVM7NKZOTA5SPOEPB","target":"record","payload":{"canonical_record":{"source":{"id":"2407.08022","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2024-07-10T20:00:22Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"83442a07045423305575da7aabbaf9314d7d2e24d6a80d4d25730c7ebbbb39fc","abstract_canon_sha256":"d60d847c6473ce62849d5514751be255777dfe5713f72c1c35255fa7495498b4"},"schema_version":"1.0"},"canonical_sha256":"88857620a2326acfb55974c1d93dc47842ea7fb268a2de7b8d1fd9d7cceac730","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:42:41.983582Z","signature_b64":"+c6b0REHvWtQ27kpbNpXotSF6ej00S8eJWQsIMcJuPsOaINTn9Fo1hx6Lybd9aXgNhc7p0V4AMTBNncXRWShBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88857620a2326acfb55974c1d93dc47842ea7fb268a2de7b8d1fd9d7cceac730","last_reissued_at":"2026-07-05T08:42:41.983187Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:42:41.983187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.08022","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":"2026-07-05T08:42:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KeUBrypgdtK/PXmt4Lw7Va4oe7zR9iRiRqieLG0T5jLVyWhWwj5uyBfQtK3so4Kj4cy0soaPzghOUWl4w/FwDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T21:26:37.384132Z"},"content_sha256":"4e2088035615bf035f955fdfbaf753916d89dc1ef632672376e34c668df67259","schema_version":"1.0","event_id":"sha256:4e2088035615bf035f955fdfbaf753916d89dc1ef632672376e34c668df67259"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:RCCXMIFCGJVM7NKZOTA5SPOEPB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Reinforcement Learning for Sequential Combinatorial Auctions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.GT","authors_text":"Aranyak Mehta, David C. Parkes, Di Wang, Manzil Zaheer, Sai Srivatsa Ravindranath, Zhe Feng","submitted_at":"2024-07-10T20:00:22Z","abstract_excerpt":"Revenue-optimal auction design is a challenging problem with significant theoretical and practical implications. Sequential auction mechanisms, known for their simplicity and strong strategyproofness guarantees, are often limited by theoretical results that are largely existential, except for certain restrictive settings. Although traditional reinforcement learning methods such as Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) are applicable in this domain, they struggle with computational demands and convergence issues when dealing with large and continuous action spaces. In l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.08022","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/2407.08022/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":"2026-07-05T08:42:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+RkLxzJWthkaWcFqw1Ku9nGt01WSr/wOhy0llfgrVdRk5DBxVeS1jRe7ljh9zXrXdq3y6GCi67phyDXw7gayBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T21:26:37.385367Z"},"content_sha256":"3f1f8a94da17924c609c58d4eaadbbeaac96f5d01e0051321d5a7feea1ce670d","schema_version":"1.0","event_id":"sha256:3f1f8a94da17924c609c58d4eaadbbeaac96f5d01e0051321d5a7feea1ce670d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RCCXMIFCGJVM7NKZOTA5SPOEPB/bundle.json","state_url":"https://pith.science/pith/RCCXMIFCGJVM7NKZOTA5SPOEPB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RCCXMIFCGJVM7NKZOTA5SPOEPB/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":"2026-08-04T21:26:37Z","links":{"resolver":"https://pith.science/pith/RCCXMIFCGJVM7NKZOTA5SPOEPB","bundle":"https://pith.science/pith/RCCXMIFCGJVM7NKZOTA5SPOEPB/bundle.json","state":"https://pith.science/pith/RCCXMIFCGJVM7NKZOTA5SPOEPB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RCCXMIFCGJVM7NKZOTA5SPOEPB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:RCCXMIFCGJVM7NKZOTA5SPOEPB","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":"d60d847c6473ce62849d5514751be255777dfe5713f72c1c35255fa7495498b4","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2024-07-10T20:00:22Z","title_canon_sha256":"83442a07045423305575da7aabbaf9314d7d2e24d6a80d4d25730c7ebbbb39fc"},"schema_version":"1.0","source":{"id":"2407.08022","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.08022","created_at":"2026-07-05T08:42:41Z"},{"alias_kind":"arxiv_version","alias_value":"2407.08022v1","created_at":"2026-07-05T08:42:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.08022","created_at":"2026-07-05T08:42:41Z"},{"alias_kind":"pith_short_12","alias_value":"RCCXMIFCGJVM","created_at":"2026-07-05T08:42:41Z"},{"alias_kind":"pith_short_16","alias_value":"RCCXMIFCGJVM7NKZ","created_at":"2026-07-05T08:42:41Z"},{"alias_kind":"pith_short_8","alias_value":"RCCXMIFC","created_at":"2026-07-05T08:42:41Z"}],"graph_snapshots":[{"event_id":"sha256:3f1f8a94da17924c609c58d4eaadbbeaac96f5d01e0051321d5a7feea1ce670d","target":"graph","created_at":"2026-07-05T08:42:41Z","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/2407.08022/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Revenue-optimal auction design is a challenging problem with significant theoretical and practical implications. Sequential auction mechanisms, known for their simplicity and strong strategyproofness guarantees, are often limited by theoretical results that are largely existential, except for certain restrictive settings. Although traditional reinforcement learning methods such as Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) are applicable in this domain, they struggle with computational demands and convergence issues when dealing with large and continuous action spaces. In l","authors_text":"Aranyak Mehta, David C. Parkes, Di Wang, Manzil Zaheer, Sai Srivatsa Ravindranath, Zhe Feng","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2024-07-10T20:00:22Z","title":"Deep Reinforcement Learning for Sequential Combinatorial Auctions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.08022","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":"sha256:4e2088035615bf035f955fdfbaf753916d89dc1ef632672376e34c668df67259","target":"record","created_at":"2026-07-05T08:42:41Z","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":"d60d847c6473ce62849d5514751be255777dfe5713f72c1c35255fa7495498b4","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2024-07-10T20:00:22Z","title_canon_sha256":"83442a07045423305575da7aabbaf9314d7d2e24d6a80d4d25730c7ebbbb39fc"},"schema_version":"1.0","source":{"id":"2407.08022","kind":"arxiv","version":1}},"canonical_sha256":"88857620a2326acfb55974c1d93dc47842ea7fb268a2de7b8d1fd9d7cceac730","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"88857620a2326acfb55974c1d93dc47842ea7fb268a2de7b8d1fd9d7cceac730","first_computed_at":"2026-07-05T08:42:41.983187Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:42:41.983187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+c6b0REHvWtQ27kpbNpXotSF6ej00S8eJWQsIMcJuPsOaINTn9Fo1hx6Lybd9aXgNhc7p0V4AMTBNncXRWShBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:42:41.983582Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.08022","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4e2088035615bf035f955fdfbaf753916d89dc1ef632672376e34c668df67259","sha256:3f1f8a94da17924c609c58d4eaadbbeaac96f5d01e0051321d5a7feea1ce670d"],"state_sha256":"4e9f9efae88d9332c6e39e022cd5eb7abb99a0a863c22f4547da68cda177860a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wFuTS3KmAT8J2MB1Nx/EImY8M99buABipHA173Ec3HF4VZGb9gUsCSSRio1Rq8EPB9BiWTTazhIGb5dMTOzwBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T21:26:37.391982Z","bundle_sha256":"c05b3fb65ad60d322362a7a05462f6ce82d236867c3ac424c16a524c864af994"}}