{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:O76FD3Q3JCZJU2MQ6W7I7YPPO2","short_pith_number":"pith:O76FD3Q3","canonical_record":{"source":{"id":"2605.29951","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-28T13:58:15Z","cross_cats_sorted":["cs.CL","cs.LG","cs.MM"],"title_canon_sha256":"c9b774c58aa52396a13c06fdec6f8716184f92a9c9b2927786b8886e0652d529","abstract_canon_sha256":"9781b0f8de28bcd53b28abedd6ec549627ca0290d9603a430c4a6d8527cb0c41"},"schema_version":"1.0"},"canonical_sha256":"77fc51ee1b48b29a6990f5be8fe1ef76890e2c1ea8ad3728dbfe6fc7508d563e","source":{"kind":"arxiv","id":"2605.29951","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.29951","created_at":"2026-05-29T02:06:03Z"},{"alias_kind":"arxiv_version","alias_value":"2605.29951v1","created_at":"2026-05-29T02:06:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29951","created_at":"2026-05-29T02:06:03Z"},{"alias_kind":"pith_short_12","alias_value":"O76FD3Q3JCZJ","created_at":"2026-05-29T02:06:03Z"},{"alias_kind":"pith_short_16","alias_value":"O76FD3Q3JCZJU2MQ","created_at":"2026-05-29T02:06:03Z"},{"alias_kind":"pith_short_8","alias_value":"O76FD3Q3","created_at":"2026-05-29T02:06:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:O76FD3Q3JCZJU2MQ6W7I7YPPO2","target":"record","payload":{"canonical_record":{"source":{"id":"2605.29951","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-28T13:58:15Z","cross_cats_sorted":["cs.CL","cs.LG","cs.MM"],"title_canon_sha256":"c9b774c58aa52396a13c06fdec6f8716184f92a9c9b2927786b8886e0652d529","abstract_canon_sha256":"9781b0f8de28bcd53b28abedd6ec549627ca0290d9603a430c4a6d8527cb0c41"},"schema_version":"1.0"},"canonical_sha256":"77fc51ee1b48b29a6990f5be8fe1ef76890e2c1ea8ad3728dbfe6fc7508d563e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T02:06:03.401644Z","signature_b64":"cgI6Sel5q0ygb7KWyXjOylp7rxi0u4/vdL6snIBGW29hinFeqOBvv8Y+RQ4FfulY00+EZR90gWxoCuoH6x5pBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77fc51ee1b48b29a6990f5be8fe1ef76890e2c1ea8ad3728dbfe6fc7508d563e","last_reissued_at":"2026-05-29T02:06:03.400820Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T02:06:03.400820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.29951","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":"2026-05-29T02:06:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y+rFR8Ow6ROPzEUe9znfWdNEfalo/X9n912I3rpWzqbxvD/nMuPvBfuIm8upafBHA11fz/g0CRy4N8DyAdupDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T06:48:38.980974Z"},"content_sha256":"947a08345a0e9ee99bb0c1fef43f6f3f520f117c9171d4d73f5d7ee7da57138f","schema_version":"1.0","event_id":"sha256:947a08345a0e9ee99bb0c1fef43f6f3f520f117c9171d4d73f5d7ee7da57138f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:O76FD3Q3JCZJU2MQ6W7I7YPPO2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MuPHI: Learning Implicit Multimodal Harm Reasoning via Semantically Grounded Reward Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","cs.MM"],"primary_cat":"cs.AI","authors_text":"Anisha Saha, Sophia Wiedmann, Teodora Kamova, Timothy Hospedales, Varsha Suresh, Vera Demberg","submitted_at":"2026-05-28T13:58:15Z","abstract_excerpt":"Understanding how harm emerges from interaction between otherwise benign image-text pairs requires intent-aware cross-modal reasoning beyond surface-level features. Existing vision-language models (VLMs) excel at literal reasoning over perceptual cues but often fail to derive harmful semantics that rely on implicit, context-dependent reasoning. To evaluate VLMs on compositional harm detection and reasoning, we introduce Multimodal Pragmatic Harm Interpretation (MuPHI), a dataset containing image-text pairs where harm is encoded in subtle multimodal cues. MuPHI spans diverse harm categories and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29951","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/2605.29951/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":"2026-05-29T02:06:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UuB2IDuV0zOC9g65J8XM+ohouTyEVTQEJO6IO9WCPFppU6SZ6g0sUPVM+Cr9ykASLUT+IP4AcJlYozBZpzjcDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T06:48:38.981415Z"},"content_sha256":"9bbfa1ca8680a80a16a9ec89c1f12fa8adbf5e21789957e9a6454827d31fd373","schema_version":"1.0","event_id":"sha256:9bbfa1ca8680a80a16a9ec89c1f12fa8adbf5e21789957e9a6454827d31fd373"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O76FD3Q3JCZJU2MQ6W7I7YPPO2/bundle.json","state_url":"https://pith.science/pith/O76FD3Q3JCZJU2MQ6W7I7YPPO2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O76FD3Q3JCZJU2MQ6W7I7YPPO2/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":"2026-06-09T06:48:38Z","links":{"resolver":"https://pith.science/pith/O76FD3Q3JCZJU2MQ6W7I7YPPO2","bundle":"https://pith.science/pith/O76FD3Q3JCZJU2MQ6W7I7YPPO2/bundle.json","state":"https://pith.science/pith/O76FD3Q3JCZJU2MQ6W7I7YPPO2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O76FD3Q3JCZJU2MQ6W7I7YPPO2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:O76FD3Q3JCZJU2MQ6W7I7YPPO2","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":"9781b0f8de28bcd53b28abedd6ec549627ca0290d9603a430c4a6d8527cb0c41","cross_cats_sorted":["cs.CL","cs.LG","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-28T13:58:15Z","title_canon_sha256":"c9b774c58aa52396a13c06fdec6f8716184f92a9c9b2927786b8886e0652d529"},"schema_version":"1.0","source":{"id":"2605.29951","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.29951","created_at":"2026-05-29T02:06:03Z"},{"alias_kind":"arxiv_version","alias_value":"2605.29951v1","created_at":"2026-05-29T02:06:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29951","created_at":"2026-05-29T02:06:03Z"},{"alias_kind":"pith_short_12","alias_value":"O76FD3Q3JCZJ","created_at":"2026-05-29T02:06:03Z"},{"alias_kind":"pith_short_16","alias_value":"O76FD3Q3JCZJU2MQ","created_at":"2026-05-29T02:06:03Z"},{"alias_kind":"pith_short_8","alias_value":"O76FD3Q3","created_at":"2026-05-29T02:06:03Z"}],"graph_snapshots":[{"event_id":"sha256:9bbfa1ca8680a80a16a9ec89c1f12fa8adbf5e21789957e9a6454827d31fd373","target":"graph","created_at":"2026-05-29T02:06:03Z","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/2605.29951/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Understanding how harm emerges from interaction between otherwise benign image-text pairs requires intent-aware cross-modal reasoning beyond surface-level features. Existing vision-language models (VLMs) excel at literal reasoning over perceptual cues but often fail to derive harmful semantics that rely on implicit, context-dependent reasoning. To evaluate VLMs on compositional harm detection and reasoning, we introduce Multimodal Pragmatic Harm Interpretation (MuPHI), a dataset containing image-text pairs where harm is encoded in subtle multimodal cues. MuPHI spans diverse harm categories and","authors_text":"Anisha Saha, Sophia Wiedmann, Teodora Kamova, Timothy Hospedales, Varsha Suresh, Vera Demberg","cross_cats":["cs.CL","cs.LG","cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-28T13:58:15Z","title":"MuPHI: Learning Implicit Multimodal Harm Reasoning via Semantically Grounded Reward Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29951","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":"sha256:947a08345a0e9ee99bb0c1fef43f6f3f520f117c9171d4d73f5d7ee7da57138f","target":"record","created_at":"2026-05-29T02:06:03Z","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":"9781b0f8de28bcd53b28abedd6ec549627ca0290d9603a430c4a6d8527cb0c41","cross_cats_sorted":["cs.CL","cs.LG","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-28T13:58:15Z","title_canon_sha256":"c9b774c58aa52396a13c06fdec6f8716184f92a9c9b2927786b8886e0652d529"},"schema_version":"1.0","source":{"id":"2605.29951","kind":"arxiv","version":1}},"canonical_sha256":"77fc51ee1b48b29a6990f5be8fe1ef76890e2c1ea8ad3728dbfe6fc7508d563e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"77fc51ee1b48b29a6990f5be8fe1ef76890e2c1ea8ad3728dbfe6fc7508d563e","first_computed_at":"2026-05-29T02:06:03.400820Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T02:06:03.400820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cgI6Sel5q0ygb7KWyXjOylp7rxi0u4/vdL6snIBGW29hinFeqOBvv8Y+RQ4FfulY00+EZR90gWxoCuoH6x5pBQ==","signature_status":"signed_v1","signed_at":"2026-05-29T02:06:03.401644Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.29951","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:947a08345a0e9ee99bb0c1fef43f6f3f520f117c9171d4d73f5d7ee7da57138f","sha256:9bbfa1ca8680a80a16a9ec89c1f12fa8adbf5e21789957e9a6454827d31fd373"],"state_sha256":"71f29e237b84ac4eca59ebf829eb48959d9f6fa0b999222b3bdc6110b27516ce"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7uNUKtdl1fCyO6lCE+701gHjUd5izQidslP5zhdgdAQ/izCj+CTqyHW2zH/Nfo+2cCHlzDopvSOcfmAACAnqBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T06:48:38.985298Z","bundle_sha256":"b5c6f06d7da7253302f29ad928d06117003fbfb9f432954a65001c3503377df7"}}