{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JNSKVTNMY67OS2VB6ATGXWORZB","short_pith_number":"pith:JNSKVTNM","schema_version":"1.0","canonical_sha256":"4b64aacdacc7bee96aa1f0266bd9d1c86a173612096066334e70e5f199ac3100","source":{"kind":"arxiv","id":"2412.15427","version":1},"attestation_state":"computed","paper":{"title":"AdaCred: Adaptive Causal Decision Transformers with Feature Crediting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Hemant Kumawat, Saibal Mukhopadhyay","submitted_at":"2024-12-19T22:22:37Z","abstract_excerpt":"Reinforcement learning (RL) can be formulated as a sequence modeling problem, where models predict future actions based on historical state-action-reward sequences. Current approaches typically require long trajectory sequences to model the environment in offline RL settings. However, these models tend to over-rely on memorizing long-term representations, which impairs their ability to effectively attribute importance to trajectories and learned representations based on task-specific relevance. In this work, we introduce AdaCred, a novel approach that represents trajectories as causal graphs b"},"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":"2412.15427","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-19T22:22:37Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"c9e6046f9e18eac1481973edb61a49f018625381a4397654c56707670336edd4","abstract_canon_sha256":"483905ac8e198ded67e2e29be2db6d549a7ebc9b1f40c2b058ecd03322bad98e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:52:23.097700Z","signature_b64":"DHjXjZhKVZT55ucFTUh4OE4R/q0ofOaPmnbiVmCDU9qmmHCT0sK1AnAos2OTrxN6aPHM1cWQ2jux3KKTC+6QCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b64aacdacc7bee96aa1f0266bd9d1c86a173612096066334e70e5f199ac3100","last_reissued_at":"2026-07-05T09:52:23.097167Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:52:23.097167Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdaCred: Adaptive Causal Decision Transformers with Feature Crediting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Hemant Kumawat, Saibal Mukhopadhyay","submitted_at":"2024-12-19T22:22:37Z","abstract_excerpt":"Reinforcement learning (RL) can be formulated as a sequence modeling problem, where models predict future actions based on historical state-action-reward sequences. Current approaches typically require long trajectory sequences to model the environment in offline RL settings. However, these models tend to over-rely on memorizing long-term representations, which impairs their ability to effectively attribute importance to trajectories and learned representations based on task-specific relevance. In this work, we introduce AdaCred, a novel approach that represents trajectories as causal graphs b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.15427","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/2412.15427/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":"2412.15427","created_at":"2026-07-05T09:52:23.097224+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.15427v1","created_at":"2026-07-05T09:52:23.097224+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.15427","created_at":"2026-07-05T09:52:23.097224+00:00"},{"alias_kind":"pith_short_12","alias_value":"JNSKVTNMY67O","created_at":"2026-07-05T09:52:23.097224+00:00"},{"alias_kind":"pith_short_16","alias_value":"JNSKVTNMY67OS2VB","created_at":"2026-07-05T09:52:23.097224+00:00"},{"alias_kind":"pith_short_8","alias_value":"JNSKVTNM","created_at":"2026-07-05T09:52:23.097224+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/JNSKVTNMY67OS2VB6ATGXWORZB","json":"https://pith.science/pith/JNSKVTNMY67OS2VB6ATGXWORZB.json","graph_json":"https://pith.science/api/pith-number/JNSKVTNMY67OS2VB6ATGXWORZB/graph.json","events_json":"https://pith.science/api/pith-number/JNSKVTNMY67OS2VB6ATGXWORZB/events.json","paper":"https://pith.science/paper/JNSKVTNM"},"agent_actions":{"view_html":"https://pith.science/pith/JNSKVTNMY67OS2VB6ATGXWORZB","download_json":"https://pith.science/pith/JNSKVTNMY67OS2VB6ATGXWORZB.json","view_paper":"https://pith.science/paper/JNSKVTNM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.15427&json=true","fetch_graph":"https://pith.science/api/pith-number/JNSKVTNMY67OS2VB6ATGXWORZB/graph.json","fetch_events":"https://pith.science/api/pith-number/JNSKVTNMY67OS2VB6ATGXWORZB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JNSKVTNMY67OS2VB6ATGXWORZB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JNSKVTNMY67OS2VB6ATGXWORZB/action/storage_attestation","attest_author":"https://pith.science/pith/JNSKVTNMY67OS2VB6ATGXWORZB/action/author_attestation","sign_citation":"https://pith.science/pith/JNSKVTNMY67OS2VB6ATGXWORZB/action/citation_signature","submit_replication":"https://pith.science/pith/JNSKVTNMY67OS2VB6ATGXWORZB/action/replication_record"}},"created_at":"2026-07-05T09:52:23.097224+00:00","updated_at":"2026-07-05T09:52:23.097224+00:00"}