{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ELOBNZBE2FNQFSBIGL2OWWBB22","short_pith_number":"pith:ELOBNZBE","canonical_record":{"source":{"id":"2606.28943","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-27T14:30:43Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"68307b8ecefe465bb45b225c4d3ba18787a3c2a1306d7afc05c6c5918fdaec98","abstract_canon_sha256":"e66b9a6fda4825cb08dfa0141d84af0340fe020434d97a422388af38b079aef4"},"schema_version":"1.0"},"canonical_sha256":"22dc16e424d15b02c82832f4eb5821d68711e66ac7b37c41594805b2be63f7b1","source":{"kind":"arxiv","id":"2606.28943","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.28943","created_at":"2026-06-30T01:17:46Z"},{"alias_kind":"arxiv_version","alias_value":"2606.28943v1","created_at":"2026-06-30T01:17:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.28943","created_at":"2026-06-30T01:17:46Z"},{"alias_kind":"pith_short_12","alias_value":"ELOBNZBE2FNQ","created_at":"2026-06-30T01:17:46Z"},{"alias_kind":"pith_short_16","alias_value":"ELOBNZBE2FNQFSBI","created_at":"2026-06-30T01:17:46Z"},{"alias_kind":"pith_short_8","alias_value":"ELOBNZBE","created_at":"2026-06-30T01:17:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ELOBNZBE2FNQFSBIGL2OWWBB22","target":"record","payload":{"canonical_record":{"source":{"id":"2606.28943","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-27T14:30:43Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"68307b8ecefe465bb45b225c4d3ba18787a3c2a1306d7afc05c6c5918fdaec98","abstract_canon_sha256":"e66b9a6fda4825cb08dfa0141d84af0340fe020434d97a422388af38b079aef4"},"schema_version":"1.0"},"canonical_sha256":"22dc16e424d15b02c82832f4eb5821d68711e66ac7b37c41594805b2be63f7b1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T01:17:46.628390Z","signature_b64":"O6YuzZAjWIyVrO3ojY8kJx482wXu5GojEuIw6AnyccEsjUlOIK6Ed1dxN55GPP3r+J4Nre4YhuvrwVC2Nk5RAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22dc16e424d15b02c82832f4eb5821d68711e66ac7b37c41594805b2be63f7b1","last_reissued_at":"2026-06-30T01:17:46.627704Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T01:17:46.627704Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.28943","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":"2026-06-30T01:17:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V6izPz5kAM9q0R6HS/703zrMmQ3RHz8foAD7BmukXv0XPIrnuMt23SFVcSk1es2RVTgG+C8DUA/zJIMmxCnPDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T17:54:51.128593Z"},"content_sha256":"cba03c2a22ce5d8d14bd9356f089629a6df0cf25973f785f783a5c754a899dc5","schema_version":"1.0","event_id":"sha256:cba03c2a22ce5d8d14bd9356f089629a6df0cf25973f785f783a5c754a899dc5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ELOBNZBE2FNQFSBIGL2OWWBB22","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Haoran Wang, Junhan Li, Minghao Chen, Yuxin Zhang","submitted_at":"2026-06-27T14:30:43Z","abstract_excerpt":"Learning to bid in repeated multi-unit auctions with bandit feedback poses a fundamental challenge. Existing methods often rely on rigid explore-then-exploit schedules, assume stationary adversaries, and optimize solely for bidder utility, thereby limiting adaptability and strategic robustness. To address these limitations, we introduce the A3M framework, which integrates adaptive deep reinforcement learning (DRL), explicit adversarial reasoning, and principled multi-objective reward design for online auction strategy optimization. A3M employs an actor-critic DRL backbone to dynamically balanc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.28943","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/2606.28943/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":"2026-06-30T01:17:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8KC42SoNo1cxXmlW4bCH4cfQkg3CQL/1KGNlilu7CL0IT22hJ3EtEFKi2hoh6XZ+L3YnUMMQb/5swUSNBCHGCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T17:54:51.128970Z"},"content_sha256":"ee2346a7e15d62b0c0eaebb73fcf959df62dcd08a3ce8c8095d9fda1cd10f356","schema_version":"1.0","event_id":"sha256:ee2346a7e15d62b0c0eaebb73fcf959df62dcd08a3ce8c8095d9fda1cd10f356"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ELOBNZBE2FNQFSBIGL2OWWBB22/bundle.json","state_url":"https://pith.science/pith/ELOBNZBE2FNQFSBIGL2OWWBB22/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ELOBNZBE2FNQFSBIGL2OWWBB22/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":"2026-06-30T17:54:51Z","links":{"resolver":"https://pith.science/pith/ELOBNZBE2FNQFSBIGL2OWWBB22","bundle":"https://pith.science/pith/ELOBNZBE2FNQFSBIGL2OWWBB22/bundle.json","state":"https://pith.science/pith/ELOBNZBE2FNQFSBIGL2OWWBB22/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ELOBNZBE2FNQFSBIGL2OWWBB22/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ELOBNZBE2FNQFSBIGL2OWWBB22","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":"e66b9a6fda4825cb08dfa0141d84af0340fe020434d97a422388af38b079aef4","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-27T14:30:43Z","title_canon_sha256":"68307b8ecefe465bb45b225c4d3ba18787a3c2a1306d7afc05c6c5918fdaec98"},"schema_version":"1.0","source":{"id":"2606.28943","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.28943","created_at":"2026-06-30T01:17:46Z"},{"alias_kind":"arxiv_version","alias_value":"2606.28943v1","created_at":"2026-06-30T01:17:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.28943","created_at":"2026-06-30T01:17:46Z"},{"alias_kind":"pith_short_12","alias_value":"ELOBNZBE2FNQ","created_at":"2026-06-30T01:17:46Z"},{"alias_kind":"pith_short_16","alias_value":"ELOBNZBE2FNQFSBI","created_at":"2026-06-30T01:17:46Z"},{"alias_kind":"pith_short_8","alias_value":"ELOBNZBE","created_at":"2026-06-30T01:17:46Z"}],"graph_snapshots":[{"event_id":"sha256:ee2346a7e15d62b0c0eaebb73fcf959df62dcd08a3ce8c8095d9fda1cd10f356","target":"graph","created_at":"2026-06-30T01:17:46Z","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/2606.28943/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning to bid in repeated multi-unit auctions with bandit feedback poses a fundamental challenge. Existing methods often rely on rigid explore-then-exploit schedules, assume stationary adversaries, and optimize solely for bidder utility, thereby limiting adaptability and strategic robustness. To address these limitations, we introduce the A3M framework, which integrates adaptive deep reinforcement learning (DRL), explicit adversarial reasoning, and principled multi-objective reward design for online auction strategy optimization. A3M employs an actor-critic DRL backbone to dynamically balanc","authors_text":"Haoran Wang, Junhan Li, Minghao Chen, Yuxin Zhang","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-27T14:30:43Z","title":"A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.28943","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:cba03c2a22ce5d8d14bd9356f089629a6df0cf25973f785f783a5c754a899dc5","target":"record","created_at":"2026-06-30T01:17:46Z","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":"e66b9a6fda4825cb08dfa0141d84af0340fe020434d97a422388af38b079aef4","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-27T14:30:43Z","title_canon_sha256":"68307b8ecefe465bb45b225c4d3ba18787a3c2a1306d7afc05c6c5918fdaec98"},"schema_version":"1.0","source":{"id":"2606.28943","kind":"arxiv","version":1}},"canonical_sha256":"22dc16e424d15b02c82832f4eb5821d68711e66ac7b37c41594805b2be63f7b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"22dc16e424d15b02c82832f4eb5821d68711e66ac7b37c41594805b2be63f7b1","first_computed_at":"2026-06-30T01:17:46.627704Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T01:17:46.627704Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"O6YuzZAjWIyVrO3ojY8kJx482wXu5GojEuIw6AnyccEsjUlOIK6Ed1dxN55GPP3r+J4Nre4YhuvrwVC2Nk5RAA==","signature_status":"signed_v1","signed_at":"2026-06-30T01:17:46.628390Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.28943","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cba03c2a22ce5d8d14bd9356f089629a6df0cf25973f785f783a5c754a899dc5","sha256:ee2346a7e15d62b0c0eaebb73fcf959df62dcd08a3ce8c8095d9fda1cd10f356"],"state_sha256":"fed1772b5db1706660979c4c05b26c5ea00db0b760c1be0af1d81809b419abef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZPloDO5Dx0bdwoDj7TU0JQ3wtcJSxHWmkt946ULnhjuNkErzyP+7I1o0uB7ojP1uBRmOKRuhWwXG5fG62/mJCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T17:54:51.130991Z","bundle_sha256":"f6c9ca475d408a5be22ff75c9f53cbeaaee8bad50648cbd3e2238fa35d3f2892"}}