{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:D2UMAEVDIKWHSGUOCUCHEGWKNN","short_pith_number":"pith:D2UMAEVD","canonical_record":{"source":{"id":"2310.08582","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-12T17:59:50Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"a6808070142535f2bfe8e943af0a9f728a588418e3d1d4b453bfda01a0ccbe6c","abstract_canon_sha256":"57245fb45342a292cfd298d9c2cba1d3b7e998fa04801f195ecd12b7247c9a39"},"schema_version":"1.0"},"canonical_sha256":"1ea8c012a342ac791a8e1504721aca6b68897110cf4d2814e6252e3067b022e9","source":{"kind":"arxiv","id":"2310.08582","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.08582","created_at":"2026-07-05T08:47:47Z"},{"alias_kind":"arxiv_version","alias_value":"2310.08582v2","created_at":"2026-07-05T08:47:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08582","created_at":"2026-07-05T08:47:47Z"},{"alias_kind":"pith_short_12","alias_value":"D2UMAEVDIKWH","created_at":"2026-07-05T08:47:47Z"},{"alias_kind":"pith_short_16","alias_value":"D2UMAEVDIKWHSGUO","created_at":"2026-07-05T08:47:47Z"},{"alias_kind":"pith_short_8","alias_value":"D2UMAEVD","created_at":"2026-07-05T08:47:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:D2UMAEVDIKWHSGUOCUCHEGWKNN","target":"record","payload":{"canonical_record":{"source":{"id":"2310.08582","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-12T17:59:50Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"a6808070142535f2bfe8e943af0a9f728a588418e3d1d4b453bfda01a0ccbe6c","abstract_canon_sha256":"57245fb45342a292cfd298d9c2cba1d3b7e998fa04801f195ecd12b7247c9a39"},"schema_version":"1.0"},"canonical_sha256":"1ea8c012a342ac791a8e1504721aca6b68897110cf4d2814e6252e3067b022e9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:47:47.699432Z","signature_b64":"hrOeksaM+NqDvloJ/Vg/TuuAWqCKi8QemLi/2Dt2yEBZ3hlNqag7o3j21isN+5xmgf3vntv2qKFJGcup3xm3BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ea8c012a342ac791a8e1504721aca6b68897110cf4d2814e6252e3067b022e9","last_reissued_at":"2026-07-05T08:47:47.698939Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:47:47.698939Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.08582","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":"2026-07-05T08:47:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ir+rpwk16mGtHJG1k8E2HagUHIvclQK/n4Tah8SC9EJ/CkxnfxKhUslevptoQzg/h+UBkFqMV/JpA/2v4i4EDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:08:32.194896Z"},"content_sha256":"642d9c9216473e0fc051d901c928db92954e601f9668ca6be6de4764ac5ab12c","schema_version":"1.0","event_id":"sha256:642d9c9216473e0fc051d901c928db92954e601f9668ca6be6de4764ac5ab12c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:D2UMAEVDIKWHSGUOCUCHEGWKNN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tree-Planner: Efficient Close-loop Task Planning with Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CL","authors_text":"Bin Wang, Mengkang Hu, Mingyu Ding, Ping Luo, Qiguang Chen, Shiguang Wu, Wenqi Shao, Xinmiao Yu, Yao Mu, Yu Qiao","submitted_at":"2023-10-12T17:59:50Z","abstract_excerpt":"This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on real-time observations. Recently, prompting Large Language Models (LLMs) to generate actions iteratively has become a prevalent paradigm due to its superior performance and user-friendliness. However, this paradigm is plagued by two inefficiencies: high token consumption and redundant error correction, both of which hinder its scalability for large-scale testing and applications. To address these issues, we propose T"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08582","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/2310.08582/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":"2026-07-05T08:47:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NVB8RkTcGD5CjeeJxY3sanLwjtPRLs1wBPfJ6l/JzuMBBL85O/IWV0tJhjH3QNEblGKuULQD1nb9xix9xGt7Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:08:32.195262Z"},"content_sha256":"2bde77c18789cc33b41e1c6e635219dfd65ea74777418c2019fd08de070ed699","schema_version":"1.0","event_id":"sha256:2bde77c18789cc33b41e1c6e635219dfd65ea74777418c2019fd08de070ed699"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D2UMAEVDIKWHSGUOCUCHEGWKNN/bundle.json","state_url":"https://pith.science/pith/D2UMAEVDIKWHSGUOCUCHEGWKNN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D2UMAEVDIKWHSGUOCUCHEGWKNN/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":"2026-07-07T08:08:32Z","links":{"resolver":"https://pith.science/pith/D2UMAEVDIKWHSGUOCUCHEGWKNN","bundle":"https://pith.science/pith/D2UMAEVDIKWHSGUOCUCHEGWKNN/bundle.json","state":"https://pith.science/pith/D2UMAEVDIKWHSGUOCUCHEGWKNN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D2UMAEVDIKWHSGUOCUCHEGWKNN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:D2UMAEVDIKWHSGUOCUCHEGWKNN","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":"57245fb45342a292cfd298d9c2cba1d3b7e998fa04801f195ecd12b7247c9a39","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-12T17:59:50Z","title_canon_sha256":"a6808070142535f2bfe8e943af0a9f728a588418e3d1d4b453bfda01a0ccbe6c"},"schema_version":"1.0","source":{"id":"2310.08582","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.08582","created_at":"2026-07-05T08:47:47Z"},{"alias_kind":"arxiv_version","alias_value":"2310.08582v2","created_at":"2026-07-05T08:47:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08582","created_at":"2026-07-05T08:47:47Z"},{"alias_kind":"pith_short_12","alias_value":"D2UMAEVDIKWH","created_at":"2026-07-05T08:47:47Z"},{"alias_kind":"pith_short_16","alias_value":"D2UMAEVDIKWHSGUO","created_at":"2026-07-05T08:47:47Z"},{"alias_kind":"pith_short_8","alias_value":"D2UMAEVD","created_at":"2026-07-05T08:47:47Z"}],"graph_snapshots":[{"event_id":"sha256:2bde77c18789cc33b41e1c6e635219dfd65ea74777418c2019fd08de070ed699","target":"graph","created_at":"2026-07-05T08:47:47Z","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/2310.08582/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on real-time observations. Recently, prompting Large Language Models (LLMs) to generate actions iteratively has become a prevalent paradigm due to its superior performance and user-friendliness. However, this paradigm is plagued by two inefficiencies: high token consumption and redundant error correction, both of which hinder its scalability for large-scale testing and applications. To address these issues, we propose T","authors_text":"Bin Wang, Mengkang Hu, Mingyu Ding, Ping Luo, Qiguang Chen, Shiguang Wu, Wenqi Shao, Xinmiao Yu, Yao Mu, Yu Qiao","cross_cats":["cs.AI","cs.LG","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-12T17:59:50Z","title":"Tree-Planner: Efficient Close-loop Task Planning with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08582","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":"sha256:642d9c9216473e0fc051d901c928db92954e601f9668ca6be6de4764ac5ab12c","target":"record","created_at":"2026-07-05T08:47:47Z","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":"57245fb45342a292cfd298d9c2cba1d3b7e998fa04801f195ecd12b7247c9a39","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-12T17:59:50Z","title_canon_sha256":"a6808070142535f2bfe8e943af0a9f728a588418e3d1d4b453bfda01a0ccbe6c"},"schema_version":"1.0","source":{"id":"2310.08582","kind":"arxiv","version":2}},"canonical_sha256":"1ea8c012a342ac791a8e1504721aca6b68897110cf4d2814e6252e3067b022e9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1ea8c012a342ac791a8e1504721aca6b68897110cf4d2814e6252e3067b022e9","first_computed_at":"2026-07-05T08:47:47.698939Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:47:47.698939Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hrOeksaM+NqDvloJ/Vg/TuuAWqCKi8QemLi/2Dt2yEBZ3hlNqag7o3j21isN+5xmgf3vntv2qKFJGcup3xm3BA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:47:47.699432Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.08582","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:642d9c9216473e0fc051d901c928db92954e601f9668ca6be6de4764ac5ab12c","sha256:2bde77c18789cc33b41e1c6e635219dfd65ea74777418c2019fd08de070ed699"],"state_sha256":"4c35962cdcc70b408d42d77963cacd7b4ac77854071b598861525a42a2818ef6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LmW2BcvTr8oDpuf8tDLlbdiR+jDwg0wjTgGGrr8iuibxR9KLGrC93UkuwadAENSiMjsMCbIBWknb9G1xwstkDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T08:08:32.197399Z","bundle_sha256":"2ec90413801e998018cf1b73f5131b0d11ea76b67887b3a58e0915bd233af333"}}