{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BTI4BVXFARTYBY2VUYCW7BXBTD","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":"5659e1fbb42c957edff57793439687ae0dd7cbec326c69bdbd43cd3478148462","cross_cats_sorted":["cs.AI","cs.PL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-08-26T14:32:49Z","title_canon_sha256":"89a4f848233da73c319cfd56ef17b5337f43a4bc93b774156fa684d8187ef2fd"},"schema_version":"1.0","source":{"id":"2508.19074","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.19074","created_at":"2026-07-05T11:59:36Z"},{"alias_kind":"arxiv_version","alias_value":"2508.19074v1","created_at":"2026-07-05T11:59:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.19074","created_at":"2026-07-05T11:59:36Z"},{"alias_kind":"pith_short_12","alias_value":"BTI4BVXFARTY","created_at":"2026-07-05T11:59:36Z"},{"alias_kind":"pith_short_16","alias_value":"BTI4BVXFARTYBY2V","created_at":"2026-07-05T11:59:36Z"},{"alias_kind":"pith_short_8","alias_value":"BTI4BVXF","created_at":"2026-07-05T11:59:36Z"}],"graph_snapshots":[{"event_id":"sha256:34777c32f0a93ca666acf353ea839164a4a1a8c2218d45367045e6bd99b3b559","target":"graph","created_at":"2026-07-05T11:59:36Z","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/2508.19074/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The Large Language Models (LLM) are increasingly being deployed in robotics to generate robot control programs for specific user tasks, enabling embodied intelligence. Existing methods primarily focus on LLM training and prompt design that utilize LLMs to generate executable programs directly from user tasks in natural language. However, due to the inconsistency of the LLMs and the high complexity of the tasks, such best-effort approaches often lead to tremendous programming errors in the generated code, which significantly undermines the effectiveness especially when the light-weight LLMs are","authors_text":"Junyi Li, ShiXing Wan, Shuai Zhao, YongTian Cheng, ZhanShang Nie, Zhendong Chen","cross_cats":["cs.AI","cs.PL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-08-26T14:32:49Z","title":"An LLM-powered Natural-to-Robotic Language Translation Framework with Correctness Guarantees"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.19074","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:d3612125b315e5cf6ab172d9659cd8e4b48e239925326c1327622bf2b9ec43fa","target":"record","created_at":"2026-07-05T11:59:36Z","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":"5659e1fbb42c957edff57793439687ae0dd7cbec326c69bdbd43cd3478148462","cross_cats_sorted":["cs.AI","cs.PL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-08-26T14:32:49Z","title_canon_sha256":"89a4f848233da73c319cfd56ef17b5337f43a4bc93b774156fa684d8187ef2fd"},"schema_version":"1.0","source":{"id":"2508.19074","kind":"arxiv","version":1}},"canonical_sha256":"0cd1c0d6e5046780e355a6056f86e198cbe56c833929dedddf480a218a960607","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0cd1c0d6e5046780e355a6056f86e198cbe56c833929dedddf480a218a960607","first_computed_at":"2026-07-05T11:59:36.339900Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:59:36.339900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JwCGoVe392W8cHKX0pcYsy9UXVh0htVUCsmj8zzwujgWcyN/4Whqvx50obWOjWD1n1Ftf8C+NH0ZUJn5wJAWAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:59:36.340394Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.19074","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d3612125b315e5cf6ab172d9659cd8e4b48e239925326c1327622bf2b9ec43fa","sha256:34777c32f0a93ca666acf353ea839164a4a1a8c2218d45367045e6bd99b3b559"],"state_sha256":"b5ef4745253b709ddf0897cbe07bf942b4cfc02d2c4c6c113f96dd044ed3ab08"}