{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EPLDDZDB324X4XL2JS3PS4SFU3","short_pith_number":"pith:EPLDDZDB","schema_version":"1.0","canonical_sha256":"23d631e461deb97e5d7a4cb6f97245a6f75635a2bf14f843199cc3ab470cdc93","source":{"kind":"arxiv","id":"2607.07498","version":1},"attestation_state":"computed","paper":{"title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alessandro Sestini, Amir Baghi, Boris Skuin, Florian Fuchs, Jessy Gosselin-Grant, Joakim Bergdahl, Linus Gissl\\'en, Michele Petteni","submitted_at":"2026-07-08T14:57:39Z","abstract_excerpt":"Testing is a major effort for the gaming industry, requiring a significant part of development budget and people power. We present a case study on a development version of the ice hockey game EA SPORTS NHL 26, for which human playtesters test the goalie AI for behavioral exploits. To reduce the effort of re-testing the goalie AI after every game or behavior modification in the development phase, we propose Reward-Adaptive Iterative Discovery (RAID), a novel approach to automatically find exploits using an iterative Reinforcement Learning (RL) approach that trains a population of goal scoring a"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.07498","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-08T14:57:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"66a3cb14359285103d05b710eb3709f3cc13232060caa78493303e634d79eb6e","abstract_canon_sha256":"4834090cf52ac3b8fa2b2096777735aa20e7e12402e68a015728258a772a567b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-09T01:20:27.281860Z","signature_b64":"YOUN6VBtLcimzUvAhmwdtWt96v4azSHg12vRHxP1cNNuZMy1h2Ienc4NvvPYOhxfVcR/LXCrHotL/OrvbbU1Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23d631e461deb97e5d7a4cb6f97245a6f75635a2bf14f843199cc3ab470cdc93","last_reissued_at":"2026-07-09T01:20:27.281446Z","signature_status":"signed_v1","first_computed_at":"2026-07-09T01:20:27.281446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alessandro Sestini, Amir Baghi, Boris Skuin, Florian Fuchs, Jessy Gosselin-Grant, Joakim Bergdahl, Linus Gissl\\'en, Michele Petteni","submitted_at":"2026-07-08T14:57:39Z","abstract_excerpt":"Testing is a major effort for the gaming industry, requiring a significant part of development budget and people power. We present a case study on a development version of the ice hockey game EA SPORTS NHL 26, for which human playtesters test the goalie AI for behavioral exploits. To reduce the effort of re-testing the goalie AI after every game or behavior modification in the development phase, we propose Reward-Adaptive Iterative Discovery (RAID), a novel approach to automatically find exploits using an iterative Reinforcement Learning (RL) approach that trains a population of goal scoring a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.07498","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/2607.07498/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.07498","created_at":"2026-07-09T01:20:27.281503+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.07498v1","created_at":"2026-07-09T01:20:27.281503+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.07498","created_at":"2026-07-09T01:20:27.281503+00:00"},{"alias_kind":"pith_short_12","alias_value":"EPLDDZDB324X","created_at":"2026-07-09T01:20:27.281503+00:00"},{"alias_kind":"pith_short_16","alias_value":"EPLDDZDB324X4XL2","created_at":"2026-07-09T01:20:27.281503+00:00"},{"alias_kind":"pith_short_8","alias_value":"EPLDDZDB","created_at":"2026-07-09T01:20:27.281503+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EPLDDZDB324X4XL2JS3PS4SFU3","json":"https://pith.science/pith/EPLDDZDB324X4XL2JS3PS4SFU3.json","graph_json":"https://pith.science/api/pith-number/EPLDDZDB324X4XL2JS3PS4SFU3/graph.json","events_json":"https://pith.science/api/pith-number/EPLDDZDB324X4XL2JS3PS4SFU3/events.json","paper":"https://pith.science/paper/EPLDDZDB"},"agent_actions":{"view_html":"https://pith.science/pith/EPLDDZDB324X4XL2JS3PS4SFU3","download_json":"https://pith.science/pith/EPLDDZDB324X4XL2JS3PS4SFU3.json","view_paper":"https://pith.science/paper/EPLDDZDB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.07498&json=true","fetch_graph":"https://pith.science/api/pith-number/EPLDDZDB324X4XL2JS3PS4SFU3/graph.json","fetch_events":"https://pith.science/api/pith-number/EPLDDZDB324X4XL2JS3PS4SFU3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EPLDDZDB324X4XL2JS3PS4SFU3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EPLDDZDB324X4XL2JS3PS4SFU3/action/storage_attestation","attest_author":"https://pith.science/pith/EPLDDZDB324X4XL2JS3PS4SFU3/action/author_attestation","sign_citation":"https://pith.science/pith/EPLDDZDB324X4XL2JS3PS4SFU3/action/citation_signature","submit_replication":"https://pith.science/pith/EPLDDZDB324X4XL2JS3PS4SFU3/action/replication_record"}},"created_at":"2026-07-09T01:20:27.281503+00:00","updated_at":"2026-07-09T01:20:27.281503+00:00"}