{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:G6QO6VEETYLN6J5MJI7D7MXZP3","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":"b8d52909b9382b567198f7f2cdb19cf4df1a7778470bcf76a8b7804157e02b43","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-10-06T21:30:38Z","title_canon_sha256":"9cc067091c4ba8c4142e1a5a0043abde024c9b3f89be51e4f66048c3a36ee9a8"},"schema_version":"1.0","source":{"id":"2410.04631","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.04631","created_at":"2026-07-05T10:41:15Z"},{"alias_kind":"arxiv_version","alias_value":"2410.04631v2","created_at":"2026-07-05T10:41:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.04631","created_at":"2026-07-05T10:41:15Z"},{"alias_kind":"pith_short_12","alias_value":"G6QO6VEETYLN","created_at":"2026-07-05T10:41:15Z"},{"alias_kind":"pith_short_16","alias_value":"G6QO6VEETYLN6J5M","created_at":"2026-07-05T10:41:15Z"},{"alias_kind":"pith_short_8","alias_value":"G6QO6VEE","created_at":"2026-07-05T10:41:15Z"}],"graph_snapshots":[{"event_id":"sha256:c91bdf0c229b9e89dc9557f4522efb9043fd9b83dbe9b74fbce7bcd4b9eecde7","target":"graph","created_at":"2026-07-05T10:41:15Z","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/2410.04631/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Linear temporal logic (LTL) has recently been adopted as a powerful formalism for specifying complex, temporally extended tasks in multi-task reinforcement learning (RL). However, learning policies that efficiently satisfy arbitrary specifications not observed during training remains a challenging problem. Existing approaches suffer from several shortcomings: they are often only applicable to finite-horizon fragments of LTL, are restricted to suboptimal solutions, and do not adequately handle safety constraints. In this work, we propose a novel learning approach to address these concerns. Our ","authors_text":"Alessandro Abate, Mathias Jackermeier","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-10-06T21:30:38Z","title":"DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.04631","kind":"arxiv","version":2},"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":"sha256:9a553f5674e4e5c0a7d2509a221286f8902fbff7d7f5496de418abcab1380bea","target":"record","created_at":"2026-07-05T10:41:15Z","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":"b8d52909b9382b567198f7f2cdb19cf4df1a7778470bcf76a8b7804157e02b43","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-10-06T21:30:38Z","title_canon_sha256":"9cc067091c4ba8c4142e1a5a0043abde024c9b3f89be51e4f66048c3a36ee9a8"},"schema_version":"1.0","source":{"id":"2410.04631","kind":"arxiv","version":2}},"canonical_sha256":"37a0ef54849e16df27ac4a3e3fb2f97ef6fc5f72584a270f731da36281310b1b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37a0ef54849e16df27ac4a3e3fb2f97ef6fc5f72584a270f731da36281310b1b","first_computed_at":"2026-07-05T10:41:15.794776Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:41:15.794776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4ap1VUX/u1/+REkovcKMr1/h8eBwIFkbiEV+Sn3mj82xLza3rlXzNZnBF0Us6VFwhXb/IqqaNG4YgbZBKKtxAg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:41:15.795247Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.04631","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a553f5674e4e5c0a7d2509a221286f8902fbff7d7f5496de418abcab1380bea","sha256:c91bdf0c229b9e89dc9557f4522efb9043fd9b83dbe9b74fbce7bcd4b9eecde7"],"state_sha256":"54df1927ad68f2b22ffa7b1733fcc36bdfbc79b77578ddc8436abee7b1ad6fea"}