{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NAJLLYJCPNISP6H6OKVRLVOZTG","short_pith_number":"pith:NAJLLYJC","canonical_record":{"source":{"id":"2402.16354","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T07:19:23Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"b47e0fa590c90300c1614c182a64b68697596622c0e8b7103f360c9e29073c45","abstract_canon_sha256":"a1c25dec6efe2577500baef602ed6d73a05435cd89881874950c34a3e12cef96"},"schema_version":"1.0"},"canonical_sha256":"6812b5e1227b5127f8fe72ab15d5d9999e6a36b86a627a79cbeaaf57ef90ecbe","source":{"kind":"arxiv","id":"2402.16354","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.16354","created_at":"2026-07-05T08:23:18Z"},{"alias_kind":"arxiv_version","alias_value":"2402.16354v2","created_at":"2026-07-05T08:23:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.16354","created_at":"2026-07-05T08:23:18Z"},{"alias_kind":"pith_short_12","alias_value":"NAJLLYJCPNIS","created_at":"2026-07-05T08:23:18Z"},{"alias_kind":"pith_short_16","alias_value":"NAJLLYJCPNISP6H6","created_at":"2026-07-05T08:23:18Z"},{"alias_kind":"pith_short_8","alias_value":"NAJLLYJC","created_at":"2026-07-05T08:23:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NAJLLYJCPNISP6H6OKVRLVOZTG","target":"record","payload":{"canonical_record":{"source":{"id":"2402.16354","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T07:19:23Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"b47e0fa590c90300c1614c182a64b68697596622c0e8b7103f360c9e29073c45","abstract_canon_sha256":"a1c25dec6efe2577500baef602ed6d73a05435cd89881874950c34a3e12cef96"},"schema_version":"1.0"},"canonical_sha256":"6812b5e1227b5127f8fe72ab15d5d9999e6a36b86a627a79cbeaaf57ef90ecbe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:18.687869Z","signature_b64":"OMOIDEJfLgYqjtoqLX9D2qORnzkH5qrHiL/ZC07MWuPTvvhj1OSu+qJDiNtITbItGqRTX7VkKYlMI1o50QSPAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6812b5e1227b5127f8fe72ab15d5d9999e6a36b86a627a79cbeaaf57ef90ecbe","last_reissued_at":"2026-07-05T08:23:18.687370Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:18.687370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.16354","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:23:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m1/Y6t0qcBt0pRC9FbWAtvx95HkuWRQQ443JgpPSohd8BgrYWhLYai/A5p0/pq7RhDTIPu573in/dCcQHJwgCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T17:39:19.676469Z"},"content_sha256":"b48595198ef09956fa2267e86def96fb407539ebaf14a93c04c7fbe7024baf06","schema_version":"1.0","event_id":"sha256:b48595198ef09956fa2267e86def96fb407539ebaf14a93c04c7fbe7024baf06"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NAJLLYJCPNISP6H6OKVRLVOZTG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Language-guided Skill Learning with Temporal Variational Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Elias Stengel-Eskin, George Konidaris, Haotian Fu, Marc-Alexandre C\\^ot\\'e, Nicolas Le Roux, Pratyusha Sharma, Xingdi Yuan","submitted_at":"2024-02-26T07:19:23Z","abstract_excerpt":"We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose an initial segmentation of the trajectories. Following that, a hierarchical variational inference framework incorporates the LLM-generated segmentation information to discover reusable skills by merging trajectory segments. To further control the trade-off between compression and reusability, we introduce a novel auxiliary objective based on the Minimum Description Length principle that helps guide this skill discovery process. Our results demonstrate tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.16354","kind":"arxiv","version":2},"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/2402.16354/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":"2026-07-05T08:23:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EksfKE47GgfFcT13dwgR9l0Tai4Ybi1Q/eArBv/lX7dS/fQJ7GMJa1inWiG9+4g80jujhvUzYDhuLll0g7QTBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T17:39:19.676956Z"},"content_sha256":"fe2c10c60df52566ca469c87070568360de2543908a8383fe519cbc1d06fd788","schema_version":"1.0","event_id":"sha256:fe2c10c60df52566ca469c87070568360de2543908a8383fe519cbc1d06fd788"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NAJLLYJCPNISP6H6OKVRLVOZTG/bundle.json","state_url":"https://pith.science/pith/NAJLLYJCPNISP6H6OKVRLVOZTG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NAJLLYJCPNISP6H6OKVRLVOZTG/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":"2026-07-31T17:39:19Z","links":{"resolver":"https://pith.science/pith/NAJLLYJCPNISP6H6OKVRLVOZTG","bundle":"https://pith.science/pith/NAJLLYJCPNISP6H6OKVRLVOZTG/bundle.json","state":"https://pith.science/pith/NAJLLYJCPNISP6H6OKVRLVOZTG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NAJLLYJCPNISP6H6OKVRLVOZTG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NAJLLYJCPNISP6H6OKVRLVOZTG","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":"a1c25dec6efe2577500baef602ed6d73a05435cd89881874950c34a3e12cef96","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T07:19:23Z","title_canon_sha256":"b47e0fa590c90300c1614c182a64b68697596622c0e8b7103f360c9e29073c45"},"schema_version":"1.0","source":{"id":"2402.16354","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.16354","created_at":"2026-07-05T08:23:18Z"},{"alias_kind":"arxiv_version","alias_value":"2402.16354v2","created_at":"2026-07-05T08:23:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.16354","created_at":"2026-07-05T08:23:18Z"},{"alias_kind":"pith_short_12","alias_value":"NAJLLYJCPNIS","created_at":"2026-07-05T08:23:18Z"},{"alias_kind":"pith_short_16","alias_value":"NAJLLYJCPNISP6H6","created_at":"2026-07-05T08:23:18Z"},{"alias_kind":"pith_short_8","alias_value":"NAJLLYJC","created_at":"2026-07-05T08:23:18Z"}],"graph_snapshots":[{"event_id":"sha256:fe2c10c60df52566ca469c87070568360de2543908a8383fe519cbc1d06fd788","target":"graph","created_at":"2026-07-05T08:23:18Z","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/2402.16354/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose an initial segmentation of the trajectories. Following that, a hierarchical variational inference framework incorporates the LLM-generated segmentation information to discover reusable skills by merging trajectory segments. To further control the trade-off between compression and reusability, we introduce a novel auxiliary objective based on the Minimum Description Length principle that helps guide this skill discovery process. Our results demonstrate tha","authors_text":"Elias Stengel-Eskin, George Konidaris, Haotian Fu, Marc-Alexandre C\\^ot\\'e, Nicolas Le Roux, Pratyusha Sharma, Xingdi Yuan","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T07:19:23Z","title":"Language-guided Skill Learning with Temporal Variational Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.16354","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:b48595198ef09956fa2267e86def96fb407539ebaf14a93c04c7fbe7024baf06","target":"record","created_at":"2026-07-05T08:23:18Z","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":"a1c25dec6efe2577500baef602ed6d73a05435cd89881874950c34a3e12cef96","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T07:19:23Z","title_canon_sha256":"b47e0fa590c90300c1614c182a64b68697596622c0e8b7103f360c9e29073c45"},"schema_version":"1.0","source":{"id":"2402.16354","kind":"arxiv","version":2}},"canonical_sha256":"6812b5e1227b5127f8fe72ab15d5d9999e6a36b86a627a79cbeaaf57ef90ecbe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6812b5e1227b5127f8fe72ab15d5d9999e6a36b86a627a79cbeaaf57ef90ecbe","first_computed_at":"2026-07-05T08:23:18.687370Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:23:18.687370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OMOIDEJfLgYqjtoqLX9D2qORnzkH5qrHiL/ZC07MWuPTvvhj1OSu+qJDiNtITbItGqRTX7VkKYlMI1o50QSPAA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:23:18.687869Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.16354","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b48595198ef09956fa2267e86def96fb407539ebaf14a93c04c7fbe7024baf06","sha256:fe2c10c60df52566ca469c87070568360de2543908a8383fe519cbc1d06fd788"],"state_sha256":"b0165ef523d36234d30c7729c2bfc76a9b02032c37710a9d4f9a4bd9fca64255"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2JrM75tYcPUZejoCorSac5A9ObZX9NvnvWYFNW7YuyAxyYoF/SNW9IZVb/jxUianrlXApULd7pgB/+MUNGy4Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T17:39:19.682293Z","bundle_sha256":"5ebdc3dbd431fd57894ffd7635918c3a7dd3c92ba9c646f4a1936e9a18026d95"}}