{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2AKZ7RH5IIVV7NLPY5ZMVVGRYS","short_pith_number":"pith:2AKZ7RH5","canonical_record":{"source":{"id":"2507.10284","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-07-14T13:51:28Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"424bd9e42905d21bc997467aec5d1df27fb98dada57a7ec9633cead932e0eced","abstract_canon_sha256":"50170631fe6326ffa54f65d1dbabfcef1b85172a87c07cf844341484a405283f"},"schema_version":"1.0"},"canonical_sha256":"d0159fc4fd422b5fb56fc772cad4d1c488a3a95787475ae75e3a77aee8dde040","source":{"kind":"arxiv","id":"2507.10284","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.10284","created_at":"2026-07-05T11:36:48Z"},{"alias_kind":"arxiv_version","alias_value":"2507.10284v1","created_at":"2026-07-05T11:36:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.10284","created_at":"2026-07-05T11:36:48Z"},{"alias_kind":"pith_short_12","alias_value":"2AKZ7RH5IIVV","created_at":"2026-07-05T11:36:48Z"},{"alias_kind":"pith_short_16","alias_value":"2AKZ7RH5IIVV7NLP","created_at":"2026-07-05T11:36:48Z"},{"alias_kind":"pith_short_8","alias_value":"2AKZ7RH5","created_at":"2026-07-05T11:36:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2AKZ7RH5IIVV7NLPY5ZMVVGRYS","target":"record","payload":{"canonical_record":{"source":{"id":"2507.10284","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-07-14T13:51:28Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"424bd9e42905d21bc997467aec5d1df27fb98dada57a7ec9633cead932e0eced","abstract_canon_sha256":"50170631fe6326ffa54f65d1dbabfcef1b85172a87c07cf844341484a405283f"},"schema_version":"1.0"},"canonical_sha256":"d0159fc4fd422b5fb56fc772cad4d1c488a3a95787475ae75e3a77aee8dde040","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:36:48.626223Z","signature_b64":"Ijs+aTK97MSgb0rT7LWBLNzOzJH4fAPS9KPSPpfs/M8oIgP+kI7hMKi+89QhcUS6sCZUDITCaNXAJChXTqYSCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0159fc4fd422b5fb56fc772cad4d1c488a3a95787475ae75e3a77aee8dde040","last_reissued_at":"2026-07-05T11:36:48.625655Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:36:48.625655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.10284","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":"2026-07-05T11:36:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HKlwE+JbYZ/ObbeV2vs+DbqUijv1mx/k71+RbqSzzsPR3g1iGaB9GYMe4jNhPL5xZiNfTc/MDbOWUc5qrsAaCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:22:11.219456Z"},"content_sha256":"51fa9dcdee554fdba20ed14625f44f591b978d73f1fdf97568cdea466bf4f5f8","schema_version":"1.0","event_id":"sha256:51fa9dcdee554fdba20ed14625f44f591b978d73f1fdf97568cdea466bf4f5f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2AKZ7RH5IIVV7NLPY5ZMVVGRYS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Prompt Informed Reinforcement Learning for Visual Coverage Path Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.RO","authors_text":"Venkat Margapuri","submitted_at":"2025-07-14T13:51:28Z","abstract_excerpt":"Visual coverage path planning with unmanned aerial vehicles (UAVs) requires agents to strategically coordinate UAV motion and camera control to maximize coverage, minimize redundancy, and maintain battery efficiency. Traditional reinforcement learning (RL) methods rely on environment-specific reward formulations that lack semantic adaptability. This study proposes Prompt-Informed Reinforcement Learning (PIRL), a novel approach that integrates the zero-shot reasoning ability and in-context learning capability of large language models with curiosity-driven RL. PIRL leverages semantic feedback fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.10284","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/2507.10284/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":"2026-07-05T11:36:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"97k/ZrSAqd3yjEoG8vXTQN2dQIeLq20MrLNkgv7BbRLExVnWNPaTsja4VZdWVM3graXcCsVnXyHvZX1pNq7gDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:22:11.219968Z"},"content_sha256":"df8f83c7d5ffa1787d0a74fd27e45a93703b837c3d83000568afc320995dbee2","schema_version":"1.0","event_id":"sha256:df8f83c7d5ffa1787d0a74fd27e45a93703b837c3d83000568afc320995dbee2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2AKZ7RH5IIVV7NLPY5ZMVVGRYS/bundle.json","state_url":"https://pith.science/pith/2AKZ7RH5IIVV7NLPY5ZMVVGRYS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2AKZ7RH5IIVV7NLPY5ZMVVGRYS/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":"2026-08-08T09:22:11Z","links":{"resolver":"https://pith.science/pith/2AKZ7RH5IIVV7NLPY5ZMVVGRYS","bundle":"https://pith.science/pith/2AKZ7RH5IIVV7NLPY5ZMVVGRYS/bundle.json","state":"https://pith.science/pith/2AKZ7RH5IIVV7NLPY5ZMVVGRYS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2AKZ7RH5IIVV7NLPY5ZMVVGRYS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2AKZ7RH5IIVV7NLPY5ZMVVGRYS","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":"50170631fe6326ffa54f65d1dbabfcef1b85172a87c07cf844341484a405283f","cross_cats_sorted":["cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-07-14T13:51:28Z","title_canon_sha256":"424bd9e42905d21bc997467aec5d1df27fb98dada57a7ec9633cead932e0eced"},"schema_version":"1.0","source":{"id":"2507.10284","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.10284","created_at":"2026-07-05T11:36:48Z"},{"alias_kind":"arxiv_version","alias_value":"2507.10284v1","created_at":"2026-07-05T11:36:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.10284","created_at":"2026-07-05T11:36:48Z"},{"alias_kind":"pith_short_12","alias_value":"2AKZ7RH5IIVV","created_at":"2026-07-05T11:36:48Z"},{"alias_kind":"pith_short_16","alias_value":"2AKZ7RH5IIVV7NLP","created_at":"2026-07-05T11:36:48Z"},{"alias_kind":"pith_short_8","alias_value":"2AKZ7RH5","created_at":"2026-07-05T11:36:48Z"}],"graph_snapshots":[{"event_id":"sha256:df8f83c7d5ffa1787d0a74fd27e45a93703b837c3d83000568afc320995dbee2","target":"graph","created_at":"2026-07-05T11:36:48Z","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/2507.10284/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Visual coverage path planning with unmanned aerial vehicles (UAVs) requires agents to strategically coordinate UAV motion and camera control to maximize coverage, minimize redundancy, and maintain battery efficiency. Traditional reinforcement learning (RL) methods rely on environment-specific reward formulations that lack semantic adaptability. This study proposes Prompt-Informed Reinforcement Learning (PIRL), a novel approach that integrates the zero-shot reasoning ability and in-context learning capability of large language models with curiosity-driven RL. PIRL leverages semantic feedback fr","authors_text":"Venkat Margapuri","cross_cats":["cs.MA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-07-14T13:51:28Z","title":"Prompt Informed Reinforcement Learning for Visual Coverage Path Planning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.10284","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":"sha256:51fa9dcdee554fdba20ed14625f44f591b978d73f1fdf97568cdea466bf4f5f8","target":"record","created_at":"2026-07-05T11:36:48Z","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":"50170631fe6326ffa54f65d1dbabfcef1b85172a87c07cf844341484a405283f","cross_cats_sorted":["cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-07-14T13:51:28Z","title_canon_sha256":"424bd9e42905d21bc997467aec5d1df27fb98dada57a7ec9633cead932e0eced"},"schema_version":"1.0","source":{"id":"2507.10284","kind":"arxiv","version":1}},"canonical_sha256":"d0159fc4fd422b5fb56fc772cad4d1c488a3a95787475ae75e3a77aee8dde040","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0159fc4fd422b5fb56fc772cad4d1c488a3a95787475ae75e3a77aee8dde040","first_computed_at":"2026-07-05T11:36:48.625655Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:36:48.625655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ijs+aTK97MSgb0rT7LWBLNzOzJH4fAPS9KPSPpfs/M8oIgP+kI7hMKi+89QhcUS6sCZUDITCaNXAJChXTqYSCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:36:48.626223Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.10284","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:51fa9dcdee554fdba20ed14625f44f591b978d73f1fdf97568cdea466bf4f5f8","sha256:df8f83c7d5ffa1787d0a74fd27e45a93703b837c3d83000568afc320995dbee2"],"state_sha256":"edd95f892aceeaacb21f152e3eabdb033375dfbfd91aee025cf8ee213f587680"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LApYqYKP0jT9vkhK5wJusFAiLBuFuCRT4NiYOoaGktRKvNJmKOZVktTys3hSmrvCH+o0S8BWrCM+gd483cfnDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T09:22:11.225477Z","bundle_sha256":"31832fa289f86e09d5bedbf68b5eb574df1b139dc9a21e42cd6f8b1e4f110417"}}