{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:J3CJVFMVLMSKPQTDPOP57IXKEU","short_pith_number":"pith:J3CJVFMV","schema_version":"1.0","canonical_sha256":"4ec49a95955b24a7c2637b9fdfa2ea251583d8a30ea1949781d334b1cd0015c9","source":{"kind":"arxiv","id":"2509.06768","version":1},"attestation_state":"computed","paper":{"title":"Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Aliasghar Arab, Devika Kodi, Kiruthiga Chandra Shekar, Oluwadamilola Sotomi","submitted_at":"2025-09-08T14:53:19Z","abstract_excerpt":"Autonomous robots operating in dynamic environments should identify and report anomalies. Embodying proactive mitigation improves safety and operational continuity. This paper presents a multimodal anomaly detection and mitigation system that integrates vision-language models and large language models to identify and report hazardous situations and conflicts in real-time. The proposed system enables robots to perceive, interpret, report, and if possible respond to urban and environmental anomalies through proactive detection mechanisms and automated mitigation actions. A key contribution in th"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2509.06768","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-09-08T14:53:19Z","cross_cats_sorted":[],"title_canon_sha256":"9bc435b2c362332dacf8ea96e9a803174836673129acc2e41bfe80d0dd1fc33c","abstract_canon_sha256":"8a1dde4bbac2d59a0044eb60ea4e2828c11bde9a187f2d12c54b84a3a4aa4e4d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:52.596782Z","signature_b64":"yQZnxDejQgkJ292DB9pkf1Sf/t1D0U1OOnfTlTTI1uZwJuLNR4ZrfmqURtMmJTMMtuEefcE2BwEdHtldpPcbBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ec49a95955b24a7c2637b9fdfa2ea251583d8a30ea1949781d334b1cd0015c9","last_reissued_at":"2026-07-05T12:06:52.596333Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:52.596333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Aliasghar Arab, Devika Kodi, Kiruthiga Chandra Shekar, Oluwadamilola Sotomi","submitted_at":"2025-09-08T14:53:19Z","abstract_excerpt":"Autonomous robots operating in dynamic environments should identify and report anomalies. Embodying proactive mitigation improves safety and operational continuity. This paper presents a multimodal anomaly detection and mitigation system that integrates vision-language models and large language models to identify and report hazardous situations and conflicts in real-time. The proposed system enables robots to perceive, interpret, report, and if possible respond to urban and environmental anomalies through proactive detection mechanisms and automated mitigation actions. A key contribution in th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.06768","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/2509.06768/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2509.06768","created_at":"2026-07-05T12:06:52.596392+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.06768v1","created_at":"2026-07-05T12:06:52.596392+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.06768","created_at":"2026-07-05T12:06:52.596392+00:00"},{"alias_kind":"pith_short_12","alias_value":"J3CJVFMVLMSK","created_at":"2026-07-05T12:06:52.596392+00:00"},{"alias_kind":"pith_short_16","alias_value":"J3CJVFMVLMSKPQTD","created_at":"2026-07-05T12:06:52.596392+00:00"},{"alias_kind":"pith_short_8","alias_value":"J3CJVFMV","created_at":"2026-07-05T12:06:52.596392+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J3CJVFMVLMSKPQTDPOP57IXKEU","json":"https://pith.science/pith/J3CJVFMVLMSKPQTDPOP57IXKEU.json","graph_json":"https://pith.science/api/pith-number/J3CJVFMVLMSKPQTDPOP57IXKEU/graph.json","events_json":"https://pith.science/api/pith-number/J3CJVFMVLMSKPQTDPOP57IXKEU/events.json","paper":"https://pith.science/paper/J3CJVFMV"},"agent_actions":{"view_html":"https://pith.science/pith/J3CJVFMVLMSKPQTDPOP57IXKEU","download_json":"https://pith.science/pith/J3CJVFMVLMSKPQTDPOP57IXKEU.json","view_paper":"https://pith.science/paper/J3CJVFMV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.06768&json=true","fetch_graph":"https://pith.science/api/pith-number/J3CJVFMVLMSKPQTDPOP57IXKEU/graph.json","fetch_events":"https://pith.science/api/pith-number/J3CJVFMVLMSKPQTDPOP57IXKEU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J3CJVFMVLMSKPQTDPOP57IXKEU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J3CJVFMVLMSKPQTDPOP57IXKEU/action/storage_attestation","attest_author":"https://pith.science/pith/J3CJVFMVLMSKPQTDPOP57IXKEU/action/author_attestation","sign_citation":"https://pith.science/pith/J3CJVFMVLMSKPQTDPOP57IXKEU/action/citation_signature","submit_replication":"https://pith.science/pith/J3CJVFMVLMSKPQTDPOP57IXKEU/action/replication_record"}},"created_at":"2026-07-05T12:06:52.596392+00:00","updated_at":"2026-07-05T12:06:52.596392+00:00"}