{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:MIMYYXI7PO3EIKCHTHPPKWA3VR","short_pith_number":"pith:MIMYYXI7","canonical_record":{"source":{"id":"2506.21230","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-26T13:20:55Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"de4b00448fbefa4235c8a948524865e782b612d3ba1d72a367ceed892e4c6b06","abstract_canon_sha256":"24e3627f1edc311da5f9552ada59d2335d6ea4bc015616e67e75c648e4d7df08"},"schema_version":"1.0"},"canonical_sha256":"62198c5d1f7bb644284799def5581bac5de069cdad7b14675557cd2391d2fda6","source":{"kind":"arxiv","id":"2506.21230","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.21230","created_at":"2026-07-05T11:30:45Z"},{"alias_kind":"arxiv_version","alias_value":"2506.21230v2","created_at":"2026-07-05T11:30:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21230","created_at":"2026-07-05T11:30:45Z"},{"alias_kind":"pith_short_12","alias_value":"MIMYYXI7PO3E","created_at":"2026-07-05T11:30:45Z"},{"alias_kind":"pith_short_16","alias_value":"MIMYYXI7PO3EIKCH","created_at":"2026-07-05T11:30:45Z"},{"alias_kind":"pith_short_8","alias_value":"MIMYYXI7","created_at":"2026-07-05T11:30:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:MIMYYXI7PO3EIKCHTHPPKWA3VR","target":"record","payload":{"canonical_record":{"source":{"id":"2506.21230","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-26T13:20:55Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"de4b00448fbefa4235c8a948524865e782b612d3ba1d72a367ceed892e4c6b06","abstract_canon_sha256":"24e3627f1edc311da5f9552ada59d2335d6ea4bc015616e67e75c648e4d7df08"},"schema_version":"1.0"},"canonical_sha256":"62198c5d1f7bb644284799def5581bac5de069cdad7b14675557cd2391d2fda6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:30:45.383633Z","signature_b64":"rA8YHhKaQ3CKciWEw++ks0auED/Iw9U7WheBqKJ3gOXIsvAzseoOv0QtCi0SOpDcDA2Pr6smLUl0YxyTwVGpDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62198c5d1f7bb644284799def5581bac5de069cdad7b14675557cd2391d2fda6","last_reissued_at":"2026-07-05T11:30:45.383138Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:30:45.383138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.21230","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":"2026-07-05T11:30:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zefS0Z1Tj2E1OKya25H5w5OjEJvLL902UFulfIoGHBpBdFdQ10pf1qmPBDPGByPMBPFgQgmH6DMJOQwyIxrZDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T22:53:31.147448Z"},"content_sha256":"a40e34c5799483980c9a271102edb1bada640cc3f7a9eea7e7c8512cfbc19294","schema_version":"1.0","event_id":"sha256:a40e34c5799483980c9a271102edb1bada640cc3f7a9eea7e7c8512cfbc19294"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:MIMYYXI7PO3EIKCHTHPPKWA3VR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"World-aware Planning Narratives Enhance Large Vision-Language Model Planner","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.AI","authors_text":"Jingjing Gong, Junhao Shi, Qipeng Guo, Siyin Wang, Xipeng Qiu, Zhaoye Fei","submitted_at":"2025-06-26T13:20:55Z","abstract_excerpt":"Large Vision-Language Models (LVLMs) show promise for embodied planning tasks but struggle with complex scenarios involving unfamiliar environments and multi-step goals. Current approaches rely on environment-agnostic imitation learning that disconnects instructions from environmental contexts, causing models to struggle with context-sensitive instructions and rely on supplementary cues rather than visual reasoning during long-horizon interactions. In this work, we propose World-Aware Planning Narrative Enhancement (WAP), a framework that infuses LVLMs with comprehensive environmental understa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21230","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/2506.21230/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":"2026-07-05T11:30:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pv38E9mBaiKPBgbzsMJS5UMS71YsLMbKkJMOr0F1U+eRcOc6tXfcDzUvAo3uOjQpAnsAft4lkSrSPzBO4u5RBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T22:53:31.148403Z"},"content_sha256":"29ef52d3533b6df29f1511667b1067152e3b5832ced88213c5cadf543ffd1f09","schema_version":"1.0","event_id":"sha256:29ef52d3533b6df29f1511667b1067152e3b5832ced88213c5cadf543ffd1f09"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MIMYYXI7PO3EIKCHTHPPKWA3VR/bundle.json","state_url":"https://pith.science/pith/MIMYYXI7PO3EIKCHTHPPKWA3VR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MIMYYXI7PO3EIKCHTHPPKWA3VR/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":"2026-08-05T22:53:31Z","links":{"resolver":"https://pith.science/pith/MIMYYXI7PO3EIKCHTHPPKWA3VR","bundle":"https://pith.science/pith/MIMYYXI7PO3EIKCHTHPPKWA3VR/bundle.json","state":"https://pith.science/pith/MIMYYXI7PO3EIKCHTHPPKWA3VR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MIMYYXI7PO3EIKCHTHPPKWA3VR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MIMYYXI7PO3EIKCHTHPPKWA3VR","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":"24e3627f1edc311da5f9552ada59d2335d6ea4bc015616e67e75c648e4d7df08","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-26T13:20:55Z","title_canon_sha256":"de4b00448fbefa4235c8a948524865e782b612d3ba1d72a367ceed892e4c6b06"},"schema_version":"1.0","source":{"id":"2506.21230","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.21230","created_at":"2026-07-05T11:30:45Z"},{"alias_kind":"arxiv_version","alias_value":"2506.21230v2","created_at":"2026-07-05T11:30:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21230","created_at":"2026-07-05T11:30:45Z"},{"alias_kind":"pith_short_12","alias_value":"MIMYYXI7PO3E","created_at":"2026-07-05T11:30:45Z"},{"alias_kind":"pith_short_16","alias_value":"MIMYYXI7PO3EIKCH","created_at":"2026-07-05T11:30:45Z"},{"alias_kind":"pith_short_8","alias_value":"MIMYYXI7","created_at":"2026-07-05T11:30:45Z"}],"graph_snapshots":[{"event_id":"sha256:29ef52d3533b6df29f1511667b1067152e3b5832ced88213c5cadf543ffd1f09","target":"graph","created_at":"2026-07-05T11:30:45Z","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/2506.21230/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Vision-Language Models (LVLMs) show promise for embodied planning tasks but struggle with complex scenarios involving unfamiliar environments and multi-step goals. Current approaches rely on environment-agnostic imitation learning that disconnects instructions from environmental contexts, causing models to struggle with context-sensitive instructions and rely on supplementary cues rather than visual reasoning during long-horizon interactions. In this work, we propose World-Aware Planning Narrative Enhancement (WAP), a framework that infuses LVLMs with comprehensive environmental understa","authors_text":"Jingjing Gong, Junhao Shi, Qipeng Guo, Siyin Wang, Xipeng Qiu, Zhaoye Fei","cross_cats":["cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-26T13:20:55Z","title":"World-aware Planning Narratives Enhance Large Vision-Language Model Planner"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21230","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:a40e34c5799483980c9a271102edb1bada640cc3f7a9eea7e7c8512cfbc19294","target":"record","created_at":"2026-07-05T11:30:45Z","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":"24e3627f1edc311da5f9552ada59d2335d6ea4bc015616e67e75c648e4d7df08","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-26T13:20:55Z","title_canon_sha256":"de4b00448fbefa4235c8a948524865e782b612d3ba1d72a367ceed892e4c6b06"},"schema_version":"1.0","source":{"id":"2506.21230","kind":"arxiv","version":2}},"canonical_sha256":"62198c5d1f7bb644284799def5581bac5de069cdad7b14675557cd2391d2fda6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"62198c5d1f7bb644284799def5581bac5de069cdad7b14675557cd2391d2fda6","first_computed_at":"2026-07-05T11:30:45.383138Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:30:45.383138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rA8YHhKaQ3CKciWEw++ks0auED/Iw9U7WheBqKJ3gOXIsvAzseoOv0QtCi0SOpDcDA2Pr6smLUl0YxyTwVGpDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:30:45.383633Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.21230","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a40e34c5799483980c9a271102edb1bada640cc3f7a9eea7e7c8512cfbc19294","sha256:29ef52d3533b6df29f1511667b1067152e3b5832ced88213c5cadf543ffd1f09"],"state_sha256":"34769d1c7b2a7dfed76b2871c0b4259f88b6d63ceb5c555bf3d4f1bf01802e7a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iIONKUD2oFMNBeGvDWtkIcDD0y1zmgQJwA/PpL38RrrI8XfAQruTNPTyUs3+04s/xdeHEcLYFCHBUq0n9B/7Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T22:53:31.155944Z","bundle_sha256":"85f8969ec5be34282ffdb862a2ccc078391b42570bdb391b7fa45f88ada63447"}}