{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7M4XBIKBQ5VOJ4LEW4UGAOMSVY","short_pith_number":"pith:7M4XBIKB","canonical_record":{"source":{"id":"2410.03334","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-04T11:40:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"34d95fe24f2e93a0a766e006292d23eba392f8d619053dadd6aeef80ca57e54c","abstract_canon_sha256":"fe7e626cd46fe07493f0950fb9755d7fd669d084ff2fc3c1a08ea39eb0314d42"},"schema_version":"1.0"},"canonical_sha256":"fb3970a141876ae4f164b728603992ae1577751f2220434e68ef565b1ad2b40e","source":{"kind":"arxiv","id":"2410.03334","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.03334","created_at":"2026-07-05T09:15:47Z"},{"alias_kind":"arxiv_version","alias_value":"2410.03334v1","created_at":"2026-07-05T09:15:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.03334","created_at":"2026-07-05T09:15:47Z"},{"alias_kind":"pith_short_12","alias_value":"7M4XBIKBQ5VO","created_at":"2026-07-05T09:15:47Z"},{"alias_kind":"pith_short_16","alias_value":"7M4XBIKBQ5VOJ4LE","created_at":"2026-07-05T09:15:47Z"},{"alias_kind":"pith_short_8","alias_value":"7M4XBIKB","created_at":"2026-07-05T09:15:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7M4XBIKBQ5VOJ4LEW4UGAOMSVY","target":"record","payload":{"canonical_record":{"source":{"id":"2410.03334","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-04T11:40:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"34d95fe24f2e93a0a766e006292d23eba392f8d619053dadd6aeef80ca57e54c","abstract_canon_sha256":"fe7e626cd46fe07493f0950fb9755d7fd669d084ff2fc3c1a08ea39eb0314d42"},"schema_version":"1.0"},"canonical_sha256":"fb3970a141876ae4f164b728603992ae1577751f2220434e68ef565b1ad2b40e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:47.864434Z","signature_b64":"u5TsTTm0wU34s4TgFe/EJAte2KMNXeQlCrFqUpqti+wU00UGy21iZVjtgwbLXuuULquzyGn56cGrVX5gfwBABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb3970a141876ae4f164b728603992ae1577751f2220434e68ef565b1ad2b40e","last_reissued_at":"2026-07-05T09:15:47.863989Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:47.863989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.03334","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":"2026-07-05T09:15:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qEzxNgqJ71wDCp+7O0cH4ABiYHgoucRd2etI+fGALTvmUERpEDVUcZZgPTiJWeafvadsI7wTrW1sdtvYYqqOAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:09:27.508050Z"},"content_sha256":"68afb060bb4b153986b6aab2765339ffb132c0cb41a09168f0f6ebc330e9f3b6","schema_version":"1.0","event_id":"sha256:68afb060bb4b153986b6aab2765339ffb132c0cb41a09168f0f6ebc330e9f3b6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7M4XBIKBQ5VOJ4LEW4UGAOMSVY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ahmed Abdulaal, Ayodeji Ijishakin, Daniel C. Alexander, Daniel C. Castro, Hugo Fry, Jack Gao, Nina Monta\\~na-Brown, Stephanie Hyland","submitted_at":"2024-10-04T11:40:21Z","abstract_excerpt":"Radiological services are experiencing unprecedented demand, leading to increased interest in automating radiology report generation. Existing Vision-Language Models (VLMs) suffer from hallucinations, lack interpretability, and require expensive fine-tuning. We introduce SAE-Rad, which uses sparse autoencoders (SAEs) to decompose latent representations from a pre-trained vision transformer into human-interpretable features. Our hybrid architecture combines state-of-the-art SAE advancements, achieving accurate latent reconstructions while maintaining sparsity. Using an off-the-shelf language mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.03334","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/2410.03334/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":"2026-07-05T09:15:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aGVciGYwUxyMQItchjiIS2Ga3ZVa4DstkdU8WereovlSxGHojXStR5GR+ZBEEeCL4XszC4oS14HPBg85HFCNAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:09:27.508483Z"},"content_sha256":"9293ec93521c77dfad472bbe1aa2e8b265d56bdeaac3ddb89eb2a63f19880024","schema_version":"1.0","event_id":"sha256:9293ec93521c77dfad472bbe1aa2e8b265d56bdeaac3ddb89eb2a63f19880024"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7M4XBIKBQ5VOJ4LEW4UGAOMSVY/bundle.json","state_url":"https://pith.science/pith/7M4XBIKBQ5VOJ4LEW4UGAOMSVY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7M4XBIKBQ5VOJ4LEW4UGAOMSVY/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":"2026-07-07T14:09:27Z","links":{"resolver":"https://pith.science/pith/7M4XBIKBQ5VOJ4LEW4UGAOMSVY","bundle":"https://pith.science/pith/7M4XBIKBQ5VOJ4LEW4UGAOMSVY/bundle.json","state":"https://pith.science/pith/7M4XBIKBQ5VOJ4LEW4UGAOMSVY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7M4XBIKBQ5VOJ4LEW4UGAOMSVY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7M4XBIKBQ5VOJ4LEW4UGAOMSVY","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":"fe7e626cd46fe07493f0950fb9755d7fd669d084ff2fc3c1a08ea39eb0314d42","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-04T11:40:21Z","title_canon_sha256":"34d95fe24f2e93a0a766e006292d23eba392f8d619053dadd6aeef80ca57e54c"},"schema_version":"1.0","source":{"id":"2410.03334","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.03334","created_at":"2026-07-05T09:15:47Z"},{"alias_kind":"arxiv_version","alias_value":"2410.03334v1","created_at":"2026-07-05T09:15:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.03334","created_at":"2026-07-05T09:15:47Z"},{"alias_kind":"pith_short_12","alias_value":"7M4XBIKBQ5VO","created_at":"2026-07-05T09:15:47Z"},{"alias_kind":"pith_short_16","alias_value":"7M4XBIKBQ5VOJ4LE","created_at":"2026-07-05T09:15:47Z"},{"alias_kind":"pith_short_8","alias_value":"7M4XBIKB","created_at":"2026-07-05T09:15:47Z"}],"graph_snapshots":[{"event_id":"sha256:9293ec93521c77dfad472bbe1aa2e8b265d56bdeaac3ddb89eb2a63f19880024","target":"graph","created_at":"2026-07-05T09:15:47Z","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/2410.03334/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Radiological services are experiencing unprecedented demand, leading to increased interest in automating radiology report generation. Existing Vision-Language Models (VLMs) suffer from hallucinations, lack interpretability, and require expensive fine-tuning. We introduce SAE-Rad, which uses sparse autoencoders (SAEs) to decompose latent representations from a pre-trained vision transformer into human-interpretable features. Our hybrid architecture combines state-of-the-art SAE advancements, achieving accurate latent reconstructions while maintaining sparsity. Using an off-the-shelf language mo","authors_text":"Ahmed Abdulaal, Ayodeji Ijishakin, Daniel C. Alexander, Daniel C. Castro, Hugo Fry, Jack Gao, Nina Monta\\~na-Brown, Stephanie Hyland","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-04T11:40:21Z","title":"An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.03334","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":"sha256:68afb060bb4b153986b6aab2765339ffb132c0cb41a09168f0f6ebc330e9f3b6","target":"record","created_at":"2026-07-05T09:15:47Z","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":"fe7e626cd46fe07493f0950fb9755d7fd669d084ff2fc3c1a08ea39eb0314d42","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-04T11:40:21Z","title_canon_sha256":"34d95fe24f2e93a0a766e006292d23eba392f8d619053dadd6aeef80ca57e54c"},"schema_version":"1.0","source":{"id":"2410.03334","kind":"arxiv","version":1}},"canonical_sha256":"fb3970a141876ae4f164b728603992ae1577751f2220434e68ef565b1ad2b40e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fb3970a141876ae4f164b728603992ae1577751f2220434e68ef565b1ad2b40e","first_computed_at":"2026-07-05T09:15:47.863989Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:15:47.863989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u5TsTTm0wU34s4TgFe/EJAte2KMNXeQlCrFqUpqti+wU00UGy21iZVjtgwbLXuuULquzyGn56cGrVX5gfwBABA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:15:47.864434Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.03334","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:68afb060bb4b153986b6aab2765339ffb132c0cb41a09168f0f6ebc330e9f3b6","sha256:9293ec93521c77dfad472bbe1aa2e8b265d56bdeaac3ddb89eb2a63f19880024"],"state_sha256":"32056eeb7c4c09d2004e57e526ffec6781b3d3e41d02d0828c88f3f36e127864"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uRGhKlPCtkbfGMYJueR4hCyt6znJ9SLZ+R2MSbioIGyh5gyZBEBGmsAwKjt/QHkJqC6L3ANINHMeZGowCZG7BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T14:09:27.510521Z","bundle_sha256":"cccaec95b2309392334df045c4fa021c98b13dc04712636a0cf803d30c6e1d82"}}