{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:YB4DSTEFXFGOLYIBMUILBWOBFL","short_pith_number":"pith:YB4DSTEF","canonical_record":{"source":{"id":"2608.12515","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-08-12T18:47:43Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"8a61629e6814e8fd225658dc2653dd8da7d90df1b01d89ae0a35f3733397993c","abstract_canon_sha256":"dbf86d5eadff5f04bf5b42ec814ecc463c96e598f8aa0881a4334cfeac45ad0a"},"schema_version":"1.0"},"canonical_sha256":"c078394c85b94ce5e1016510b0d9c12ae961777173c69b1d9a3e724d617e6912","source":{"kind":"arxiv","id":"2608.12515","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.12515","created_at":"2026-08-14T00:44:16Z"},{"alias_kind":"arxiv_version","alias_value":"2608.12515v1","created_at":"2026-08-14T00:44:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.12515","created_at":"2026-08-14T00:44:16Z"},{"alias_kind":"pith_short_12","alias_value":"YB4DSTEFXFGO","created_at":"2026-08-14T00:44:16Z"},{"alias_kind":"pith_short_16","alias_value":"YB4DSTEFXFGOLYIB","created_at":"2026-08-14T00:44:16Z"},{"alias_kind":"pith_short_8","alias_value":"YB4DSTEF","created_at":"2026-08-14T00:44:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:YB4DSTEFXFGOLYIBMUILBWOBFL","target":"record","payload":{"canonical_record":{"source":{"id":"2608.12515","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-08-12T18:47:43Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"8a61629e6814e8fd225658dc2653dd8da7d90df1b01d89ae0a35f3733397993c","abstract_canon_sha256":"dbf86d5eadff5f04bf5b42ec814ecc463c96e598f8aa0881a4334cfeac45ad0a"},"schema_version":"1.0"},"canonical_sha256":"c078394c85b94ce5e1016510b0d9c12ae961777173c69b1d9a3e724d617e6912","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-14T00:44:16.709384Z","signature_b64":"33omiNukJB8i5cmS0+NNFFig7e0C4sWJ3nOohEVmn8tHIH/fY6Z1k0n5+rJ1nHHcy8PY6Wq3mZO9RSeTxy5zBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c078394c85b94ce5e1016510b0d9c12ae961777173c69b1d9a3e724d617e6912","last_reissued_at":"2026-08-14T00:44:16.692732Z","signature_status":"signed_v1","first_computed_at":"2026-08-14T00:44:16.692732Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.12515","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":"2026-08-14T00:44:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ip6alFoiGpNAQlypd/5KIrhr4qdjZ2p+yO1VcbEmIx+8cPUM67yI0nBTsfqpPx/hOMVnnAe/S++G/0jSIMGwBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T05:33:49.542840Z"},"content_sha256":"e83f1707d67e400d1c6405454233740dcb85571814e930cb74b0c793c05380ba","schema_version":"1.0","event_id":"sha256:e83f1707d67e400d1c6405454233740dcb85571814e930cb74b0c793c05380ba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:YB4DSTEFXFGOLYIBMUILBWOBFL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Can Vision-Language Models Assess Proxemic Risk from Egocentric Robot Images?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Dmytro Kuzmenko, Vladyslava Rudas","submitted_at":"2026-08-12T18:47:43Z","abstract_excerpt":"Assessing proxemic danger from a robot's egocentric perspective is critical for safe embodied navigation in human environments and requires both visual and contextual reasoning. We evaluate three opensource vision-language models (VLMs) (\\textit{InternVL}, \\textit{Qwen-VL}, and \\textit{SmolVLM}) on the classification of egocentric robot images into four danger levels, comparing three prompting strategies and two rounds of QLoRA fine-tuning against a stratified random baseline. Without fine-tuning, all models perform near the baseline, while fine-tuning yields only modest overall improvements. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.12515","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/2608.12515/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":"2026-08-14T00:44:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bFp/l+uJauDxKWqGcwzyp5u3ksHMKND0O9VnaOiYGmx+vIdhHpa1T3K/zTUHITZpLp4fmGFeUD0v8Jm+yVjjCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T05:33:49.543369Z"},"content_sha256":"8127cdc9344f226899d2353c34d6aad1aac675d342edcb2205a1efbe757d43ba","schema_version":"1.0","event_id":"sha256:8127cdc9344f226899d2353c34d6aad1aac675d342edcb2205a1efbe757d43ba"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:YB4DSTEFXFGOLYIBMUILBWOBFL","target":"integrity","payload":{"note":"Identifier '10.1007/s12369-019-00560-92' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Patompak, P., Jeong, S., Nilkhamhang, I., Chong, N.Y.: Learning proxemics for personalized human–robot social interaction. International Journal of Social Robotics12, 267–280 (2020).https://doi.org/10.1007/s12369-019-00560-92","arxiv_id":"2608.12515","detector":"doi_compliance","evidence":{"doi":"10.1007/s12369-019-00560-92","arxiv_id":null,"ref_index":15,"raw_excerpt":"Patompak, P., Jeong, S., Nilkhamhang, I., Chong, N.Y.: Learning proxemics for personalized human–robot social interaction. International Journal of Social Robotics12, 267–280 (2020).https://doi.org/10.1007/s12369-019-00560-92","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":15,"audited_at":"2026-08-16T00:19:28.431395Z","event_type":"pith.integrity.v1","detected_doi":"10.1007/s12369-019-00560-92","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"8197d6bad61fc0f68da4ca1cf83700aa4b9d073a57958c73830246a668568a85","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":20124,"payload_sha256":"b0822752c0b33944da5c701f99206b804bf040f81541f9f72d76852ddbb14b18","signature_b64":"2wfYUDNrH88tC6Jy2alZkTUOfr26Wy5i2zxM5LfhYg5vkAKIu5omFneP/YHqJOVDUZaZNgTOjo/flnJ2ZaZRCQ==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-16T00:23:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SQ6Zs2gWnSHy+T75LP68wm8Tq/1dSuuifh8kQKcSKsHWblJ/0YBMklFNV1g2VURWSfHEMcPnGWJsmAagDHdwBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T05:33:49.588325Z"},"content_sha256":"dd1ce5ee04ddf8385103c15cd8a274a423619109a1b9653e722b47af183690fe","schema_version":"1.0","event_id":"sha256:dd1ce5ee04ddf8385103c15cd8a274a423619109a1b9653e722b47af183690fe"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:YB4DSTEFXFGOLYIBMUILBWOBFL","target":"integrity","payload":{"note":"Identifier '10.3390/mi120201932' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Daza, M., Barrios Aranibar, D., Diaz Amado, J.A., et al.: An approach of social navigation based on proxemics for crowded environments of humans and robots. Micromachines12(2), 193 (2021).https://doi.org/10.3390/mi120201932","arxiv_id":"2608.12515","detector":"doi_compliance","evidence":{"doi":"10.3390/mi120201932","arxiv_id":null,"ref_index":4,"raw_excerpt":"Daza, M., Barrios Aranibar, D., Diaz Amado, J.A., et al.: An approach of social navigation based on proxemics for crowded environments of humans and robots. Micromachines12(2), 193 (2021).https://doi.org/10.3390/mi120201932","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":4,"audited_at":"2026-08-16T00:19:28.431395Z","event_type":"pith.integrity.v1","detected_doi":"10.3390/mi120201932","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"32cfe88786d64550a80f52d3abeb7b196bf3c57e6917512f155eec6e6b239861","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":20123,"payload_sha256":"de8b05cc7010f9c82033873801d4a6ca560e9ed3ffe2ddefe45aeaad715b6fe4","signature_b64":"KqDf93xvUo7FrXSxd/xmayu7QnFzIh6FW+xC9MfzG0dLP3mUTP37b2H1031loG8InMUxH8KMCGCJWqTNwpp9Cw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-16T00:23:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a7Jqb5JvHHHd2yVtwwVVY5cDbT82eP+6Kwq2tAGpk8W0VnJmRa9Bpukqpue/pC+og5ug3HDNJ61dqydd96M0CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T05:33:49.588733Z"},"content_sha256":"a07de59f1d44c7bc6342f539c5c48d74649e113108ef4894f4b713bcc10cd97a","schema_version":"1.0","event_id":"sha256:a07de59f1d44c7bc6342f539c5c48d74649e113108ef4894f4b713bcc10cd97a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YB4DSTEFXFGOLYIBMUILBWOBFL/bundle.json","state_url":"https://pith.science/pith/YB4DSTEFXFGOLYIBMUILBWOBFL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YB4DSTEFXFGOLYIBMUILBWOBFL/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":"2026-08-17T05:33:49Z","links":{"resolver":"https://pith.science/pith/YB4DSTEFXFGOLYIBMUILBWOBFL","bundle":"https://pith.science/pith/YB4DSTEFXFGOLYIBMUILBWOBFL/bundle.json","state":"https://pith.science/pith/YB4DSTEFXFGOLYIBMUILBWOBFL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YB4DSTEFXFGOLYIBMUILBWOBFL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:YB4DSTEFXFGOLYIBMUILBWOBFL","merge_version":"pith-open-graph-merge-v1","event_count":4,"valid_event_count":4,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"dbf86d5eadff5f04bf5b42ec814ecc463c96e598f8aa0881a4334cfeac45ad0a","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-08-12T18:47:43Z","title_canon_sha256":"8a61629e6814e8fd225658dc2653dd8da7d90df1b01d89ae0a35f3733397993c"},"schema_version":"1.0","source":{"id":"2608.12515","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.12515","created_at":"2026-08-14T00:44:16Z"},{"alias_kind":"arxiv_version","alias_value":"2608.12515v1","created_at":"2026-08-14T00:44:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.12515","created_at":"2026-08-14T00:44:16Z"},{"alias_kind":"pith_short_12","alias_value":"YB4DSTEFXFGO","created_at":"2026-08-14T00:44:16Z"},{"alias_kind":"pith_short_16","alias_value":"YB4DSTEFXFGOLYIB","created_at":"2026-08-14T00:44:16Z"},{"alias_kind":"pith_short_8","alias_value":"YB4DSTEF","created_at":"2026-08-14T00:44:16Z"}],"graph_snapshots":[{"event_id":"sha256:8127cdc9344f226899d2353c34d6aad1aac675d342edcb2205a1efbe757d43ba","target":"graph","created_at":"2026-08-14T00:44:16Z","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/2608.12515/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Assessing proxemic danger from a robot's egocentric perspective is critical for safe embodied navigation in human environments and requires both visual and contextual reasoning. We evaluate three opensource vision-language models (VLMs) (\\textit{InternVL}, \\textit{Qwen-VL}, and \\textit{SmolVLM}) on the classification of egocentric robot images into four danger levels, comparing three prompting strategies and two rounds of QLoRA fine-tuning against a stratified random baseline. Without fine-tuning, all models perform near the baseline, while fine-tuning yields only modest overall improvements. ","authors_text":"Dmytro Kuzmenko, Vladyslava Rudas","cross_cats":["cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-08-12T18:47:43Z","title":"Can Vision-Language Models Assess Proxemic Risk from Egocentric Robot Images?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.12515","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:e83f1707d67e400d1c6405454233740dcb85571814e930cb74b0c793c05380ba","target":"record","created_at":"2026-08-14T00:44:16Z","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":"dbf86d5eadff5f04bf5b42ec814ecc463c96e598f8aa0881a4334cfeac45ad0a","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-08-12T18:47:43Z","title_canon_sha256":"8a61629e6814e8fd225658dc2653dd8da7d90df1b01d89ae0a35f3733397993c"},"schema_version":"1.0","source":{"id":"2608.12515","kind":"arxiv","version":1}},"canonical_sha256":"c078394c85b94ce5e1016510b0d9c12ae961777173c69b1d9a3e724d617e6912","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c078394c85b94ce5e1016510b0d9c12ae961777173c69b1d9a3e724d617e6912","first_computed_at":"2026-08-14T00:44:16.692732Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-14T00:44:16.692732Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"33omiNukJB8i5cmS0+NNFFig7e0C4sWJ3nOohEVmn8tHIH/fY6Z1k0n5+rJ1nHHcy8PY6Wq3mZO9RSeTxy5zBg==","signature_status":"signed_v1","signed_at":"2026-08-14T00:44:16.709384Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.12515","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:a07de59f1d44c7bc6342f539c5c48d74649e113108ef4894f4b713bcc10cd97a","sha256:dd1ce5ee04ddf8385103c15cd8a274a423619109a1b9653e722b47af183690fe"]}],"invalid_events":[],"applied_event_ids":["sha256:e83f1707d67e400d1c6405454233740dcb85571814e930cb74b0c793c05380ba","sha256:8127cdc9344f226899d2353c34d6aad1aac675d342edcb2205a1efbe757d43ba"],"state_sha256":"8bb9bad9df40c221b557d8e26018b7d9b6498c827c1561bb58d8f8cb40046a44"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"COtuuOvNj5jIPCr1YMexbyJUqA9sc6xYXuyTZJgW+3FIup7VT3ukeoNqmFjIiyw7gGpdsbjD1Qy4klxt9OwAAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T05:33:49.591388Z","bundle_sha256":"41b16d7a07a586856c313fa13913f40b5b02e426eaf628efa435080e87d4d84d"}}