{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:AMV43JZ7EYB64WUNCREZBQXBGH","short_pith_number":"pith:AMV43JZ7","canonical_record":{"source":{"id":"2404.11027","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-04-17T03:06:32Z","cross_cats_sorted":[],"title_canon_sha256":"01defa42e566ae1f80ed8228bc4207d6210f20e4819c87a1259a3ef28b254c58","abstract_canon_sha256":"e706848a146b8ade4f0c3fb9922c787e144e224ed0d453c2613d181778dbaf72"},"schema_version":"1.0"},"canonical_sha256":"032bcda73f2603ee5a8d144990c2e131d5a5cce4c8aa2ae5295243f99046c659","source":{"kind":"arxiv","id":"2404.11027","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.11027","created_at":"2026-07-05T08:09:02Z"},{"alias_kind":"arxiv_version","alias_value":"2404.11027v1","created_at":"2026-07-05T08:09:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.11027","created_at":"2026-07-05T08:09:02Z"},{"alias_kind":"pith_short_12","alias_value":"AMV43JZ7EYB6","created_at":"2026-07-05T08:09:02Z"},{"alias_kind":"pith_short_16","alias_value":"AMV43JZ7EYB64WUN","created_at":"2026-07-05T08:09:02Z"},{"alias_kind":"pith_short_8","alias_value":"AMV43JZ7","created_at":"2026-07-05T08:09:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:AMV43JZ7EYB64WUNCREZBQXBGH","target":"record","payload":{"canonical_record":{"source":{"id":"2404.11027","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-04-17T03:06:32Z","cross_cats_sorted":[],"title_canon_sha256":"01defa42e566ae1f80ed8228bc4207d6210f20e4819c87a1259a3ef28b254c58","abstract_canon_sha256":"e706848a146b8ade4f0c3fb9922c787e144e224ed0d453c2613d181778dbaf72"},"schema_version":"1.0"},"canonical_sha256":"032bcda73f2603ee5a8d144990c2e131d5a5cce4c8aa2ae5295243f99046c659","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:09:02.448207Z","signature_b64":"a2Mlyb5asGuQJ081BdoDFiCvk9JouVDaE6YeV1LmYUtto6m/OckJX79FlsJUi+Cb0EfJKrH/DkDDs91apu1TCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"032bcda73f2603ee5a8d144990c2e131d5a5cce4c8aa2ae5295243f99046c659","last_reissued_at":"2026-07-05T08:09:02.447797Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:09:02.447797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.11027","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:09:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VndrEShBYu2l52ou3sEj9WDqE5FTHhlCfZ3HRWPATeYZTQmDQRZPbOc6WyZK1/B05v+kp3SN2JHYLzibmu6uBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:25:19.070476Z"},"content_sha256":"47c95ae340f400940ae2ed7837ed338dc07360a938ad561b6e4694285df1131e","schema_version":"1.0","event_id":"sha256:47c95ae340f400940ae2ed7837ed338dc07360a938ad561b6e4694285df1131e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:AMV43JZ7EYB64WUNCREZBQXBGH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Empowering Large Language Models on Robotic Manipulation with Affordance Prompting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Changyin Sun, Chuheng Zhang, Guangran Cheng, Jiang Bian, Li Zhao, Wenzhe Cai","submitted_at":"2024-04-17T03:06:32Z","abstract_excerpt":"While large language models (LLMs) are successful in completing various language processing tasks, they easily fail to interact with the physical world by generating control sequences properly. We find that the main reason is that LLMs are not grounded in the physical world. Existing LLM-based approaches circumvent this problem by relying on additional pre-defined skills or pre-trained sub-policies, making it hard to adapt to new tasks. In contrast, we aim to address this problem and explore the possibility to prompt pre-trained LLMs to accomplish a series of robotic manipulation tasks in a tr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.11027","kind":"arxiv","version":1},"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/2404.11027/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":"2026-07-05T08:09:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qS9GdddGxki6/tovuC6SQTmACYxxHPCig2y/W9+OkR6IaiRnq/ZRl/xl0h7G1QA1jPMNymQIkthG4zhjDaZmDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:25:19.071011Z"},"content_sha256":"a417b64b13894f753769e522cd3d6d339cb512795a6231943a287b2742c40b1f","schema_version":"1.0","event_id":"sha256:a417b64b13894f753769e522cd3d6d339cb512795a6231943a287b2742c40b1f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AMV43JZ7EYB64WUNCREZBQXBGH/bundle.json","state_url":"https://pith.science/pith/AMV43JZ7EYB64WUNCREZBQXBGH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AMV43JZ7EYB64WUNCREZBQXBGH/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":"2026-08-08T15:25:19Z","links":{"resolver":"https://pith.science/pith/AMV43JZ7EYB64WUNCREZBQXBGH","bundle":"https://pith.science/pith/AMV43JZ7EYB64WUNCREZBQXBGH/bundle.json","state":"https://pith.science/pith/AMV43JZ7EYB64WUNCREZBQXBGH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AMV43JZ7EYB64WUNCREZBQXBGH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:AMV43JZ7EYB64WUNCREZBQXBGH","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":"e706848a146b8ade4f0c3fb9922c787e144e224ed0d453c2613d181778dbaf72","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-04-17T03:06:32Z","title_canon_sha256":"01defa42e566ae1f80ed8228bc4207d6210f20e4819c87a1259a3ef28b254c58"},"schema_version":"1.0","source":{"id":"2404.11027","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.11027","created_at":"2026-07-05T08:09:02Z"},{"alias_kind":"arxiv_version","alias_value":"2404.11027v1","created_at":"2026-07-05T08:09:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.11027","created_at":"2026-07-05T08:09:02Z"},{"alias_kind":"pith_short_12","alias_value":"AMV43JZ7EYB6","created_at":"2026-07-05T08:09:02Z"},{"alias_kind":"pith_short_16","alias_value":"AMV43JZ7EYB64WUN","created_at":"2026-07-05T08:09:02Z"},{"alias_kind":"pith_short_8","alias_value":"AMV43JZ7","created_at":"2026-07-05T08:09:02Z"}],"graph_snapshots":[{"event_id":"sha256:a417b64b13894f753769e522cd3d6d339cb512795a6231943a287b2742c40b1f","target":"graph","created_at":"2026-07-05T08:09:02Z","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/2404.11027/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While large language models (LLMs) are successful in completing various language processing tasks, they easily fail to interact with the physical world by generating control sequences properly. We find that the main reason is that LLMs are not grounded in the physical world. Existing LLM-based approaches circumvent this problem by relying on additional pre-defined skills or pre-trained sub-policies, making it hard to adapt to new tasks. In contrast, we aim to address this problem and explore the possibility to prompt pre-trained LLMs to accomplish a series of robotic manipulation tasks in a tr","authors_text":"Changyin Sun, Chuheng Zhang, Guangran Cheng, Jiang Bian, Li Zhao, Wenzhe Cai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-04-17T03:06:32Z","title":"Empowering Large Language Models on Robotic Manipulation with Affordance Prompting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.11027","kind":"arxiv","version":1},"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":"sha256:47c95ae340f400940ae2ed7837ed338dc07360a938ad561b6e4694285df1131e","target":"record","created_at":"2026-07-05T08:09:02Z","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":"e706848a146b8ade4f0c3fb9922c787e144e224ed0d453c2613d181778dbaf72","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-04-17T03:06:32Z","title_canon_sha256":"01defa42e566ae1f80ed8228bc4207d6210f20e4819c87a1259a3ef28b254c58"},"schema_version":"1.0","source":{"id":"2404.11027","kind":"arxiv","version":1}},"canonical_sha256":"032bcda73f2603ee5a8d144990c2e131d5a5cce4c8aa2ae5295243f99046c659","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"032bcda73f2603ee5a8d144990c2e131d5a5cce4c8aa2ae5295243f99046c659","first_computed_at":"2026-07-05T08:09:02.447797Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:09:02.447797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"a2Mlyb5asGuQJ081BdoDFiCvk9JouVDaE6YeV1LmYUtto6m/OckJX79FlsJUi+Cb0EfJKrH/DkDDs91apu1TCA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:09:02.448207Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.11027","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:47c95ae340f400940ae2ed7837ed338dc07360a938ad561b6e4694285df1131e","sha256:a417b64b13894f753769e522cd3d6d339cb512795a6231943a287b2742c40b1f"],"state_sha256":"01dd01e091b41799d2032421137fed9b6cda3b2c4e1c71df74ed4d67dcc8bae0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tr3QK64KaM+11dkKy8PIR2jPFaa4NeMWT4VbZO245rKSxVS6LHOf1RdgwVJ+Kv8Z0VIWXwMuboQ3nYGx/PphDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T15:25:19.076078Z","bundle_sha256":"4791ff27249b00e64541eb4db45892d8f12f59c33a50a1151a22f06797820a08"}}