{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:5P23W4EUFSUDJEW5VYFOFHS7O6","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":"21b3c4506d15bb3758a24f62fd46526f1f400a6324a4fb2324abb19b8d568833","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-11T07:33:35Z","title_canon_sha256":"40335a05dbd3c6dc13fc16922880c081849d26994e36458023e88d37f3dd8763"},"schema_version":"1.0","source":{"id":"2509.09208","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09208","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09208v1","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09208","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"pith_short_12","alias_value":"5P23W4EUFSUD","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"pith_short_16","alias_value":"5P23W4EUFSUDJEW5","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"pith_short_8","alias_value":"5P23W4EU","created_at":"2026-07-05T12:09:31Z"}],"graph_snapshots":[{"event_id":"sha256:bf36e0d4b50518a825740710d6a88413e1a0ba8d82c3ff41b69b90402f2b6d9c","target":"graph","created_at":"2026-07-05T12:09:31Z","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/2509.09208/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Constrained Reinforcement Learning (RL) aims to maximize the return while adhering to predefined constraint limits, which represent domain-specific safety requirements. In continuous control settings, where learning agents govern system actions, balancing the trade-off between reward maximization and constraint satisfaction remains a significant challenge. Policy optimization methods often exhibit instability near constraint boundaries, resulting in suboptimal training performance. To address this issue, we introduce a novel approach that integrates an adaptive incentive mechanism in addition ","authors_text":"Pallab Dasgupta, Somnath Hazra, Soumyajit Dey","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-11T07:33:35Z","title":"Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09208","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":"sha256:55726ca35d7a033243b827e27936f2592328a965fd3c3c1673da76d826da0408","target":"record","created_at":"2026-07-05T12:09:31Z","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":"21b3c4506d15bb3758a24f62fd46526f1f400a6324a4fb2324abb19b8d568833","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-11T07:33:35Z","title_canon_sha256":"40335a05dbd3c6dc13fc16922880c081849d26994e36458023e88d37f3dd8763"},"schema_version":"1.0","source":{"id":"2509.09208","kind":"arxiv","version":1}},"canonical_sha256":"ebf5bb70942ca83492ddae0ae29e5f778e6f6e0de69f69eca9baddd627f9fe9f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ebf5bb70942ca83492ddae0ae29e5f778e6f6e0de69f69eca9baddd627f9fe9f","first_computed_at":"2026-07-05T12:09:31.599342Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:09:31.599342Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vX5AVKYqIMj+rM/yhWcL6aT/768e32JTQc89tQX7jNPmxx1QfK5QvO4fBsTyKhECt210ytcKrqxJ3qCMS50RAg==","signature_status":"signed_v1","signed_at":"2026-07-05T12:09:31.599953Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.09208","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:55726ca35d7a033243b827e27936f2592328a965fd3c3c1673da76d826da0408","sha256:bf36e0d4b50518a825740710d6a88413e1a0ba8d82c3ff41b69b90402f2b6d9c"],"state_sha256":"e0c753eb0207344e5e52bae0731d16d44776a26781ae2a0233d14eb6ad0def60"}