{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:WN3OJVYUNCQ4ZMFJU2O5JDVMNE","short_pith_number":"pith:WN3OJVYU","canonical_record":{"source":{"id":"2401.05821","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-11T10:38:22Z","cross_cats_sorted":["cs.SC"],"title_canon_sha256":"f925f3060eebb4cb53dcfbcb96b49696c7789c5e4f55348628d64d0623291756","abstract_canon_sha256":"bb06b236f4ea109c56564851b6f252044495c23862571f29a77cfb6fa21f4bf2"},"schema_version":"1.0"},"canonical_sha256":"b376e4d71468a1ccb0a9a69dd48eac690f9d8814f31621ba08325edff3502b27","source":{"kind":"arxiv","id":"2401.05821","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.05821","created_at":"2026-07-05T09:27:20Z"},{"alias_kind":"arxiv_version","alias_value":"2401.05821v4","created_at":"2026-07-05T09:27:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.05821","created_at":"2026-07-05T09:27:20Z"},{"alias_kind":"pith_short_12","alias_value":"WN3OJVYUNCQ4","created_at":"2026-07-05T09:27:20Z"},{"alias_kind":"pith_short_16","alias_value":"WN3OJVYUNCQ4ZMFJ","created_at":"2026-07-05T09:27:20Z"},{"alias_kind":"pith_short_8","alias_value":"WN3OJVYU","created_at":"2026-07-05T09:27:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:WN3OJVYUNCQ4ZMFJU2O5JDVMNE","target":"record","payload":{"canonical_record":{"source":{"id":"2401.05821","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-11T10:38:22Z","cross_cats_sorted":["cs.SC"],"title_canon_sha256":"f925f3060eebb4cb53dcfbcb96b49696c7789c5e4f55348628d64d0623291756","abstract_canon_sha256":"bb06b236f4ea109c56564851b6f252044495c23862571f29a77cfb6fa21f4bf2"},"schema_version":"1.0"},"canonical_sha256":"b376e4d71468a1ccb0a9a69dd48eac690f9d8814f31621ba08325edff3502b27","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:27:20.095268Z","signature_b64":"laujqbDYW19HSkSyhcAB6eL5nQsrd+K5Ugz2pHWWpj9IjukYUkaGsLMzBB06zsvUl9lW/T34QNPmTkZt5VZhBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b376e4d71468a1ccb0a9a69dd48eac690f9d8814f31621ba08325edff3502b27","last_reissued_at":"2026-07-05T09:27:20.094868Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:27:20.094868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.05821","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:27:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T6jdnG+krQ7Xno/1rZdS4BxH8Oaylr0jWwKges31djynF13RHRVHOAuqKbgypertLaCu84YL81v7ZPP1O5KGAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T11:13:27.123864Z"},"content_sha256":"06db462ecdf8048d58ed41af51b3624c105a5b69689c131435d0a0579778effc","schema_version":"1.0","event_id":"sha256:06db462ecdf8048d58ed41af51b3624c105a5b69689c131435d0a0579778effc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:WN3OJVYUNCQ4ZMFJU2O5JDVMNE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SC"],"primary_cat":"cs.LG","authors_text":"Kristian Kersting, Mark Rothermel, Quentin Delfosse, Sebastian Sztwiertnia, Wolfgang Stammer","submitted_at":"2024-01-11T10:38:22Z","abstract_excerpt":"Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal policies. Unfortunately, the black-box nature of deep neural networks impedes the inclusion of domain experts for inspecting the model and revising suboptimal policies. To this end, we introduce *Successive Concept Bottleneck Agents* (SCoBots), that integrate consecutive concept bottleneck (CB) layers. In contrast to current CB models, SCoBots do not just represent concepts as properties of individual objects, b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.05821","kind":"arxiv","version":4},"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/2401.05821/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":"2026-07-05T09:27:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wtrGPV/7+yw43rpRNMX+EemZMJILGMGvGNjtBHuofJGqAgno4DRsWkr3gt6CfN3ioC2PdqP1isFOCDC4yamFDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T11:13:27.148897Z"},"content_sha256":"ad9574ec99303629769fd47699c339be4bb9b54c1cbe7acba9dab5b5ac76050d","schema_version":"1.0","event_id":"sha256:ad9574ec99303629769fd47699c339be4bb9b54c1cbe7acba9dab5b5ac76050d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WN3OJVYUNCQ4ZMFJU2O5JDVMNE/bundle.json","state_url":"https://pith.science/pith/WN3OJVYUNCQ4ZMFJU2O5JDVMNE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WN3OJVYUNCQ4ZMFJU2O5JDVMNE/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":"2026-08-09T11:13:27Z","links":{"resolver":"https://pith.science/pith/WN3OJVYUNCQ4ZMFJU2O5JDVMNE","bundle":"https://pith.science/pith/WN3OJVYUNCQ4ZMFJU2O5JDVMNE/bundle.json","state":"https://pith.science/pith/WN3OJVYUNCQ4ZMFJU2O5JDVMNE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WN3OJVYUNCQ4ZMFJU2O5JDVMNE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WN3OJVYUNCQ4ZMFJU2O5JDVMNE","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":"bb06b236f4ea109c56564851b6f252044495c23862571f29a77cfb6fa21f4bf2","cross_cats_sorted":["cs.SC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-11T10:38:22Z","title_canon_sha256":"f925f3060eebb4cb53dcfbcb96b49696c7789c5e4f55348628d64d0623291756"},"schema_version":"1.0","source":{"id":"2401.05821","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.05821","created_at":"2026-07-05T09:27:20Z"},{"alias_kind":"arxiv_version","alias_value":"2401.05821v4","created_at":"2026-07-05T09:27:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.05821","created_at":"2026-07-05T09:27:20Z"},{"alias_kind":"pith_short_12","alias_value":"WN3OJVYUNCQ4","created_at":"2026-07-05T09:27:20Z"},{"alias_kind":"pith_short_16","alias_value":"WN3OJVYUNCQ4ZMFJ","created_at":"2026-07-05T09:27:20Z"},{"alias_kind":"pith_short_8","alias_value":"WN3OJVYU","created_at":"2026-07-05T09:27:20Z"}],"graph_snapshots":[{"event_id":"sha256:ad9574ec99303629769fd47699c339be4bb9b54c1cbe7acba9dab5b5ac76050d","target":"graph","created_at":"2026-07-05T09:27:20Z","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/2401.05821/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal policies. Unfortunately, the black-box nature of deep neural networks impedes the inclusion of domain experts for inspecting the model and revising suboptimal policies. To this end, we introduce *Successive Concept Bottleneck Agents* (SCoBots), that integrate consecutive concept bottleneck (CB) layers. In contrast to current CB models, SCoBots do not just represent concepts as properties of individual objects, b","authors_text":"Kristian Kersting, Mark Rothermel, Quentin Delfosse, Sebastian Sztwiertnia, Wolfgang Stammer","cross_cats":["cs.SC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-11T10:38:22Z","title":"Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.05821","kind":"arxiv","version":4},"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":"sha256:06db462ecdf8048d58ed41af51b3624c105a5b69689c131435d0a0579778effc","target":"record","created_at":"2026-07-05T09:27:20Z","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":"bb06b236f4ea109c56564851b6f252044495c23862571f29a77cfb6fa21f4bf2","cross_cats_sorted":["cs.SC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-11T10:38:22Z","title_canon_sha256":"f925f3060eebb4cb53dcfbcb96b49696c7789c5e4f55348628d64d0623291756"},"schema_version":"1.0","source":{"id":"2401.05821","kind":"arxiv","version":4}},"canonical_sha256":"b376e4d71468a1ccb0a9a69dd48eac690f9d8814f31621ba08325edff3502b27","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b376e4d71468a1ccb0a9a69dd48eac690f9d8814f31621ba08325edff3502b27","first_computed_at":"2026-07-05T09:27:20.094868Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:27:20.094868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"laujqbDYW19HSkSyhcAB6eL5nQsrd+K5Ugz2pHWWpj9IjukYUkaGsLMzBB06zsvUl9lW/T34QNPmTkZt5VZhBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:27:20.095268Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.05821","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:06db462ecdf8048d58ed41af51b3624c105a5b69689c131435d0a0579778effc","sha256:ad9574ec99303629769fd47699c339be4bb9b54c1cbe7acba9dab5b5ac76050d"],"state_sha256":"fe7c34d42443b88d91e5b7f60d6bcd2b0f187b6b46782a8d8d8748a6e8ca366a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q8Yt047smCqfYBUN1TFdQTiX3cO1QSrMTKtrF5saxdYQDR2rQu599LAgY6q0ahVe2CrEWxzyP71uT1FF8TrPBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T11:13:27.153994Z","bundle_sha256":"b4427a5258a2297007a35920e84e0ed39786073be392c2e91fe5811a05d2a428"}}