{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:G6AUW6KSDQZ3DGJZM4UIM5SLG4","short_pith_number":"pith:G6AUW6KS","canonical_record":{"source":{"id":"2606.18019","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2026-06-16T15:01:30Z","cross_cats_sorted":["cs.CL","cs.SD"],"title_canon_sha256":"69699226466ec8efa15663e32a3523f740d3e3704befdeedc4c5fffed279c06d","abstract_canon_sha256":"cbb8c81717cb29688cf2532b3dc1f954b48ee6cce5c477d384aa4ffbe56e58a4"},"schema_version":"1.0"},"canonical_sha256":"37814b79521c33b19939672886764b370f8deacc6d6f0adb010d310419804a11","source":{"kind":"arxiv","id":"2606.18019","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.18019","created_at":"2026-06-19T16:10:45Z"},{"alias_kind":"arxiv_version","alias_value":"2606.18019v1","created_at":"2026-06-19T16:10:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.18019","created_at":"2026-06-19T16:10:45Z"},{"alias_kind":"pith_short_12","alias_value":"G6AUW6KSDQZ3","created_at":"2026-06-19T16:10:45Z"},{"alias_kind":"pith_short_16","alias_value":"G6AUW6KSDQZ3DGJZ","created_at":"2026-06-19T16:10:45Z"},{"alias_kind":"pith_short_8","alias_value":"G6AUW6KS","created_at":"2026-06-19T16:10:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:G6AUW6KSDQZ3DGJZM4UIM5SLG4","target":"record","payload":{"canonical_record":{"source":{"id":"2606.18019","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2026-06-16T15:01:30Z","cross_cats_sorted":["cs.CL","cs.SD"],"title_canon_sha256":"69699226466ec8efa15663e32a3523f740d3e3704befdeedc4c5fffed279c06d","abstract_canon_sha256":"cbb8c81717cb29688cf2532b3dc1f954b48ee6cce5c477d384aa4ffbe56e58a4"},"schema_version":"1.0"},"canonical_sha256":"37814b79521c33b19939672886764b370f8deacc6d6f0adb010d310419804a11","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:45.993949Z","signature_b64":"5a7r44Q0YWTU66lkjDBdTIhKRr4x1CzfyQXiIL7be8lxR3IRqL5GvC4KlSGe3jK4Olu4J6GfA7NAdC4htCy3Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37814b79521c33b19939672886764b370f8deacc6d6f0adb010d310419804a11","last_reissued_at":"2026-06-19T16:10:45.993586Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:45.993586Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.18019","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":"2026-06-19T16:10:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SCEFLozLOe1uHQ6S9zEan/pmjaRXkNvFQaSt4fBpB5XiNIaeDe4b5bJCwU1T7z6vtvUcLGvDs3OsBwwkmC5QCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T09:11:15.629153Z"},"content_sha256":"ce55270ec661e044377f86b46662c18b9282277b372055afe7c8d0759b07ba17","schema_version":"1.0","event_id":"sha256:ce55270ec661e044377f86b46662c18b9282277b372055afe7c8d0759b07ba17"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:G6AUW6KSDQZ3DGJZM4UIM5SLG4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Alea R\\\"uggeberg, Franziska Braun, Hartmut Lehfeld, Korbinian Riedhammer, Thomas Hillemacher, Thomas Ranzenberger, Tobias Bocklet","submitted_at":"2026-06-16T15:01:30Z","abstract_excerpt":"Dementia and depression are the most prevalent neuropsychiatric disorders in geriatric populations, and their overlapping symptoms pose major challenges for differential diagnosis. In this study, we investigate open-weights Large Language Models (LLMs) for predicting dementia and depression severity from speech samples collected during standardized history taking interviews with 154 German-speaking subjects. We introduce an observer-based Global Depression Scale (GDS-D) aligned with the established Global Deterioration Scale (GDS), enabling parallel global staging of affective and cognitive sy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.18019","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/2606.18019/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":"2026-06-19T16:10:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6iFt8oaKAweI6S8dkAtaq02h2y/Uob5v7fMo0K9DCekuj8U9npNQedosSDX9n4gZy8KfMTxVGi5WGUsls+SYAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T09:11:15.629544Z"},"content_sha256":"59784db47ab9e7ec236a3a8ad9c6eec28b8d38b9a143b41824b0eb7f94ab495b","schema_version":"1.0","event_id":"sha256:59784db47ab9e7ec236a3a8ad9c6eec28b8d38b9a143b41824b0eb7f94ab495b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G6AUW6KSDQZ3DGJZM4UIM5SLG4/bundle.json","state_url":"https://pith.science/pith/G6AUW6KSDQZ3DGJZM4UIM5SLG4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G6AUW6KSDQZ3DGJZM4UIM5SLG4/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":"2026-06-26T09:11:15Z","links":{"resolver":"https://pith.science/pith/G6AUW6KSDQZ3DGJZM4UIM5SLG4","bundle":"https://pith.science/pith/G6AUW6KSDQZ3DGJZM4UIM5SLG4/bundle.json","state":"https://pith.science/pith/G6AUW6KSDQZ3DGJZM4UIM5SLG4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G6AUW6KSDQZ3DGJZM4UIM5SLG4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:G6AUW6KSDQZ3DGJZM4UIM5SLG4","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":"cbb8c81717cb29688cf2532b3dc1f954b48ee6cce5c477d384aa4ffbe56e58a4","cross_cats_sorted":["cs.CL","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2026-06-16T15:01:30Z","title_canon_sha256":"69699226466ec8efa15663e32a3523f740d3e3704befdeedc4c5fffed279c06d"},"schema_version":"1.0","source":{"id":"2606.18019","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.18019","created_at":"2026-06-19T16:10:45Z"},{"alias_kind":"arxiv_version","alias_value":"2606.18019v1","created_at":"2026-06-19T16:10:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.18019","created_at":"2026-06-19T16:10:45Z"},{"alias_kind":"pith_short_12","alias_value":"G6AUW6KSDQZ3","created_at":"2026-06-19T16:10:45Z"},{"alias_kind":"pith_short_16","alias_value":"G6AUW6KSDQZ3DGJZ","created_at":"2026-06-19T16:10:45Z"},{"alias_kind":"pith_short_8","alias_value":"G6AUW6KS","created_at":"2026-06-19T16:10:45Z"}],"graph_snapshots":[{"event_id":"sha256:59784db47ab9e7ec236a3a8ad9c6eec28b8d38b9a143b41824b0eb7f94ab495b","target":"graph","created_at":"2026-06-19T16:10:45Z","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/2606.18019/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dementia and depression are the most prevalent neuropsychiatric disorders in geriatric populations, and their overlapping symptoms pose major challenges for differential diagnosis. In this study, we investigate open-weights Large Language Models (LLMs) for predicting dementia and depression severity from speech samples collected during standardized history taking interviews with 154 German-speaking subjects. We introduce an observer-based Global Depression Scale (GDS-D) aligned with the established Global Deterioration Scale (GDS), enabling parallel global staging of affective and cognitive sy","authors_text":"Alea R\\\"uggeberg, Franziska Braun, Hartmut Lehfeld, Korbinian Riedhammer, Thomas Hillemacher, Thomas Ranzenberger, Tobias Bocklet","cross_cats":["cs.CL","cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2026-06-16T15:01:30Z","title":"Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.18019","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:ce55270ec661e044377f86b46662c18b9282277b372055afe7c8d0759b07ba17","target":"record","created_at":"2026-06-19T16:10:45Z","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":"cbb8c81717cb29688cf2532b3dc1f954b48ee6cce5c477d384aa4ffbe56e58a4","cross_cats_sorted":["cs.CL","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2026-06-16T15:01:30Z","title_canon_sha256":"69699226466ec8efa15663e32a3523f740d3e3704befdeedc4c5fffed279c06d"},"schema_version":"1.0","source":{"id":"2606.18019","kind":"arxiv","version":1}},"canonical_sha256":"37814b79521c33b19939672886764b370f8deacc6d6f0adb010d310419804a11","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37814b79521c33b19939672886764b370f8deacc6d6f0adb010d310419804a11","first_computed_at":"2026-06-19T16:10:45.993586Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-19T16:10:45.993586Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5a7r44Q0YWTU66lkjDBdTIhKRr4x1CzfyQXiIL7be8lxR3IRqL5GvC4KlSGe3jK4Olu4J6GfA7NAdC4htCy3Cg==","signature_status":"signed_v1","signed_at":"2026-06-19T16:10:45.993949Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.18019","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ce55270ec661e044377f86b46662c18b9282277b372055afe7c8d0759b07ba17","sha256:59784db47ab9e7ec236a3a8ad9c6eec28b8d38b9a143b41824b0eb7f94ab495b"],"state_sha256":"3e0d5cbfdc278613abb651349f49af19798c8370d1d9e2c0afb5076a1cdfe765"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8xz0I4ci51Bf2wGe+FcQajMjS0Xh+jMSYoEWH7/O222oVKY7ujNTjO9k2K4PEdfszxMYdyrJPOoqbZDj9eAEBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-26T09:11:15.631626Z","bundle_sha256":"60ce663992265993714528007590d8aa00a257aa49c3d24e5bfbc50681832468"}}