{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:L4SFKGG3EASLRLAD33QKXUMXWK","short_pith_number":"pith:L4SFKGG3","canonical_record":{"source":{"id":"2601.21841","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-29T15:18:58Z","cross_cats_sorted":[],"title_canon_sha256":"4fefdc44f109870b15e0798d24759c08ba738f99a3cd75fee13bbfe06dc38de9","abstract_canon_sha256":"0d258ee91353402ba8b6ed53f2c59d5f3b6312b707bc02d07239c14f15d73c4b"},"schema_version":"1.0"},"canonical_sha256":"5f245518db2024b8ac03dee0abd197b284560f6af38ae4651d738559d4e68832","source":{"kind":"arxiv","id":"2601.21841","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.21841","created_at":"2026-05-20T00:04:23Z"},{"alias_kind":"arxiv_version","alias_value":"2601.21841v3","created_at":"2026-05-20T00:04:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.21841","created_at":"2026-05-20T00:04:23Z"},{"alias_kind":"pith_short_12","alias_value":"L4SFKGG3EASL","created_at":"2026-05-20T00:04:23Z"},{"alias_kind":"pith_short_16","alias_value":"L4SFKGG3EASLRLAD","created_at":"2026-05-20T00:04:23Z"},{"alias_kind":"pith_short_8","alias_value":"L4SFKGG3","created_at":"2026-05-20T00:04:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:L4SFKGG3EASLRLAD33QKXUMXWK","target":"record","payload":{"canonical_record":{"source":{"id":"2601.21841","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-29T15:18:58Z","cross_cats_sorted":[],"title_canon_sha256":"4fefdc44f109870b15e0798d24759c08ba738f99a3cd75fee13bbfe06dc38de9","abstract_canon_sha256":"0d258ee91353402ba8b6ed53f2c59d5f3b6312b707bc02d07239c14f15d73c4b"},"schema_version":"1.0"},"canonical_sha256":"5f245518db2024b8ac03dee0abd197b284560f6af38ae4651d738559d4e68832","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:23.764614Z","signature_b64":"woxjCl7LQbixj7FKhrmJ54YSM+kkpzCiqq8RmsR0v6j1rUuUWgbqLIxpQDmqF1X5BO2zKU71MmMfbaUGHS5KBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f245518db2024b8ac03dee0abd197b284560f6af38ae4651d738559d4e68832","last_reissued_at":"2026-05-20T00:04:23.763779Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:23.763779Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.21841","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:04:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RTTAIHniyyipKr0RJHtMJ5NM3Y/L8k4LudM21QZqnjjTHLH9P07EMbt5rd2mzGY8ylOInnCs6ENk+2+c+ErDBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T02:27:43.278336Z"},"content_sha256":"9746897c6a1c799d1b575354160fd5ebb2d75aa4e01659bab476dcd4b27fb2af","schema_version":"1.0","event_id":"sha256:9746897c6a1c799d1b575354160fd5ebb2d75aa4e01659bab476dcd4b27fb2af"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:L4SFKGG3EASLRLAD33QKXUMXWK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Embodied Task Planning via Graph-Informed Action Generation with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Masood Mortazavi, Ning Yan, Xiang Li","submitted_at":"2026-01-29T15:18:58Z","abstract_excerpt":"While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning capabilities, their deployment as embodied agents still faces fundamental challenges in long-horizon planning. Unlike open-ended text generation, embodied agents must decompose high-level intents into actionable sub-goals while adhering to the constraints of a dynamic environment. Standard LLM planners frequently fail to maintain strategy coherence over extended horizons due to context window limitations or hallucinate state transitions that violate environment constraints. We propose GiG, a planning framework that"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.21841","kind":"arxiv","version":3},"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/2601.21841/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":"2026-05-20T00:04:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C8Wv5KmnOjkhij1ZHNMWy5a45yFDdmjGMbW3XOuNQSBb6zpBBGuE5Lk56KvsYuX6OxMf5AYzv9OFS1TfJi2TDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T02:27:43.278722Z"},"content_sha256":"1e690b400796108e4f51691221f65c31cff5aec75f930e762c279dc9ec659bee","schema_version":"1.0","event_id":"sha256:1e690b400796108e4f51691221f65c31cff5aec75f930e762c279dc9ec659bee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L4SFKGG3EASLRLAD33QKXUMXWK/bundle.json","state_url":"https://pith.science/pith/L4SFKGG3EASLRLAD33QKXUMXWK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L4SFKGG3EASLRLAD33QKXUMXWK/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":"2026-05-28T02:27:43Z","links":{"resolver":"https://pith.science/pith/L4SFKGG3EASLRLAD33QKXUMXWK","bundle":"https://pith.science/pith/L4SFKGG3EASLRLAD33QKXUMXWK/bundle.json","state":"https://pith.science/pith/L4SFKGG3EASLRLAD33QKXUMXWK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L4SFKGG3EASLRLAD33QKXUMXWK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:L4SFKGG3EASLRLAD33QKXUMXWK","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":"0d258ee91353402ba8b6ed53f2c59d5f3b6312b707bc02d07239c14f15d73c4b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-29T15:18:58Z","title_canon_sha256":"4fefdc44f109870b15e0798d24759c08ba738f99a3cd75fee13bbfe06dc38de9"},"schema_version":"1.0","source":{"id":"2601.21841","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.21841","created_at":"2026-05-20T00:04:23Z"},{"alias_kind":"arxiv_version","alias_value":"2601.21841v3","created_at":"2026-05-20T00:04:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.21841","created_at":"2026-05-20T00:04:23Z"},{"alias_kind":"pith_short_12","alias_value":"L4SFKGG3EASL","created_at":"2026-05-20T00:04:23Z"},{"alias_kind":"pith_short_16","alias_value":"L4SFKGG3EASLRLAD","created_at":"2026-05-20T00:04:23Z"},{"alias_kind":"pith_short_8","alias_value":"L4SFKGG3","created_at":"2026-05-20T00:04:23Z"}],"graph_snapshots":[{"event_id":"sha256:1e690b400796108e4f51691221f65c31cff5aec75f930e762c279dc9ec659bee","target":"graph","created_at":"2026-05-20T00:04:23Z","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/2601.21841/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning capabilities, their deployment as embodied agents still faces fundamental challenges in long-horizon planning. Unlike open-ended text generation, embodied agents must decompose high-level intents into actionable sub-goals while adhering to the constraints of a dynamic environment. Standard LLM planners frequently fail to maintain strategy coherence over extended horizons due to context window limitations or hallucinate state transitions that violate environment constraints. We propose GiG, a planning framework that","authors_text":"Masood Mortazavi, Ning Yan, Xiang Li","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-29T15:18:58Z","title":"Embodied Task Planning via Graph-Informed Action Generation with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.21841","kind":"arxiv","version":3},"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":"sha256:9746897c6a1c799d1b575354160fd5ebb2d75aa4e01659bab476dcd4b27fb2af","target":"record","created_at":"2026-05-20T00:04:23Z","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":"0d258ee91353402ba8b6ed53f2c59d5f3b6312b707bc02d07239c14f15d73c4b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-29T15:18:58Z","title_canon_sha256":"4fefdc44f109870b15e0798d24759c08ba738f99a3cd75fee13bbfe06dc38de9"},"schema_version":"1.0","source":{"id":"2601.21841","kind":"arxiv","version":3}},"canonical_sha256":"5f245518db2024b8ac03dee0abd197b284560f6af38ae4651d738559d4e68832","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5f245518db2024b8ac03dee0abd197b284560f6af38ae4651d738559d4e68832","first_computed_at":"2026-05-20T00:04:23.763779Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:04:23.763779Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"woxjCl7LQbixj7FKhrmJ54YSM+kkpzCiqq8RmsR0v6j1rUuUWgbqLIxpQDmqF1X5BO2zKU71MmMfbaUGHS5KBA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:04:23.764614Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.21841","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9746897c6a1c799d1b575354160fd5ebb2d75aa4e01659bab476dcd4b27fb2af","sha256:1e690b400796108e4f51691221f65c31cff5aec75f930e762c279dc9ec659bee"],"state_sha256":"db8c07304682d39191f7c24943645cd0a6fdc70efbc3a33c2a9a5daabaa9679a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"63QZ9uBoV3hcFo3RQiAskwPmFC0xuWZ6BRIEmyhKrulNNW0uX730MvC8AWprGx2RYPKvXuTvew5qAqSZkNMdBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T02:27:43.280807Z","bundle_sha256":"f34e849285a51f4d1c604b8ba4746532d5116f9f6b8dbf2cbcd7276cffd1732a"}}