{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:RUHKNKAKTD6OPV63IXWCV42RXL","short_pith_number":"pith:RUHKNKAK","canonical_record":{"source":{"id":"2605.18763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-04-10T13:13:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"80be2ee7d247fb7dda8d242935b9e9343cc91ebff321650f2bfcee96c39c4304","abstract_canon_sha256":"e5fbed5a65f8e795eed544d7f6d3a67d78be1180b52480b1e69492191e9346ab"},"schema_version":"1.0"},"canonical_sha256":"8d0ea6a80a98fce7d7db45ec2af351baeea1836267dc112908b00827265e2102","source":{"kind":"arxiv","id":"2605.18763","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.18763","created_at":"2026-05-20T00:06:20Z"},{"alias_kind":"arxiv_version","alias_value":"2605.18763v1","created_at":"2026-05-20T00:06:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18763","created_at":"2026-05-20T00:06:20Z"},{"alias_kind":"pith_short_12","alias_value":"RUHKNKAKTD6O","created_at":"2026-05-20T00:06:20Z"},{"alias_kind":"pith_short_16","alias_value":"RUHKNKAKTD6OPV63","created_at":"2026-05-20T00:06:20Z"},{"alias_kind":"pith_short_8","alias_value":"RUHKNKAK","created_at":"2026-05-20T00:06:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:RUHKNKAKTD6OPV63IXWCV42RXL","target":"record","payload":{"canonical_record":{"source":{"id":"2605.18763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-04-10T13:13:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"80be2ee7d247fb7dda8d242935b9e9343cc91ebff321650f2bfcee96c39c4304","abstract_canon_sha256":"e5fbed5a65f8e795eed544d7f6d3a67d78be1180b52480b1e69492191e9346ab"},"schema_version":"1.0"},"canonical_sha256":"8d0ea6a80a98fce7d7db45ec2af351baeea1836267dc112908b00827265e2102","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:06:20.627563Z","signature_b64":"ifr606NUgTxrK5sOrpNmm9n2tUJyzvQm6P39atgpjEd9C259wpuKrJApIqdJVAP0tTt9NeHO6KyI5LbT16wxAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d0ea6a80a98fce7d7db45ec2af351baeea1836267dc112908b00827265e2102","last_reissued_at":"2026-05-20T00:06:20.626738Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:06:20.626738Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.18763","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":"2026-05-20T00:06:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9hECccaaqMmrlcxCukDNrNG0dOMO79utUjcCXAj4Gqk1BheZbYnrFzISUzREmbX0TYdYC1HPE8rzfx/CjbnaDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T15:44:13.320999Z"},"content_sha256":"230dafab08da91b5b1da252754d80c1c96d7ee09daa9d12f25cae72a76d334c7","schema_version":"1.0","event_id":"sha256:230dafab08da91b5b1da252754d80c1c96d7ee09daa9d12f25cae72a76d334c7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:RUHKNKAKTD6OPV63IXWCV42RXL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Query-Conditioned Graph Retrieval for Contextualized LLM Reasoning in Personalized Wearable Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Amir M. Rahmani, Mahyar Abbasian, Zhenyu Lu","submitted_at":"2026-04-10T13:13:17Z","abstract_excerpt":"Large language models (LLMs) are increasingly applied to analyzing wearable sensing data, which are long-term, multimodal, and highly personalized. A key challenge is context selection: providing insufficient context limits reasoning, while including all available data leads to inefficiency and degraded generation quality. We propose Wearable As Graph (WAG), a graph-based context retrieval framework that enables query-adaptive reasoning over wearable data with LLMs. WAG organizes wearable metrics and user-specific signals into a personalized knowledge graph, and retrieves a query-conditioned s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18763","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/2605.18763/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":"2026-05-20T00:06:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QikcT62GW1hxkrxhCRcRefmkgA9hs7iki3Nd5nIdsZqpnszuxi7D13TFizZ6lbaZzYKvJDC1ygO19hXJ3AzpAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T15:44:13.321720Z"},"content_sha256":"a9a29fd69c345d1d1f2c6b2345d2f5938caee8bd976eb777a7f4b7f2dc76f7f0","schema_version":"1.0","event_id":"sha256:a9a29fd69c345d1d1f2c6b2345d2f5938caee8bd976eb777a7f4b7f2dc76f7f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RUHKNKAKTD6OPV63IXWCV42RXL/bundle.json","state_url":"https://pith.science/pith/RUHKNKAKTD6OPV63IXWCV42RXL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RUHKNKAKTD6OPV63IXWCV42RXL/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":"2026-06-04T15:44:13Z","links":{"resolver":"https://pith.science/pith/RUHKNKAKTD6OPV63IXWCV42RXL","bundle":"https://pith.science/pith/RUHKNKAKTD6OPV63IXWCV42RXL/bundle.json","state":"https://pith.science/pith/RUHKNKAKTD6OPV63IXWCV42RXL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RUHKNKAKTD6OPV63IXWCV42RXL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:RUHKNKAKTD6OPV63IXWCV42RXL","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":"e5fbed5a65f8e795eed544d7f6d3a67d78be1180b52480b1e69492191e9346ab","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-04-10T13:13:17Z","title_canon_sha256":"80be2ee7d247fb7dda8d242935b9e9343cc91ebff321650f2bfcee96c39c4304"},"schema_version":"1.0","source":{"id":"2605.18763","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.18763","created_at":"2026-05-20T00:06:20Z"},{"alias_kind":"arxiv_version","alias_value":"2605.18763v1","created_at":"2026-05-20T00:06:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18763","created_at":"2026-05-20T00:06:20Z"},{"alias_kind":"pith_short_12","alias_value":"RUHKNKAKTD6O","created_at":"2026-05-20T00:06:20Z"},{"alias_kind":"pith_short_16","alias_value":"RUHKNKAKTD6OPV63","created_at":"2026-05-20T00:06:20Z"},{"alias_kind":"pith_short_8","alias_value":"RUHKNKAK","created_at":"2026-05-20T00:06:20Z"}],"graph_snapshots":[{"event_id":"sha256:a9a29fd69c345d1d1f2c6b2345d2f5938caee8bd976eb777a7f4b7f2dc76f7f0","target":"graph","created_at":"2026-05-20T00:06:20Z","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/2605.18763/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) are increasingly applied to analyzing wearable sensing data, which are long-term, multimodal, and highly personalized. A key challenge is context selection: providing insufficient context limits reasoning, while including all available data leads to inefficiency and degraded generation quality. We propose Wearable As Graph (WAG), a graph-based context retrieval framework that enables query-adaptive reasoning over wearable data with LLMs. WAG organizes wearable metrics and user-specific signals into a personalized knowledge graph, and retrieves a query-conditioned s","authors_text":"Amir M. Rahmani, Mahyar Abbasian, Zhenyu Lu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-04-10T13:13:17Z","title":"Query-Conditioned Graph Retrieval for Contextualized LLM Reasoning in Personalized Wearable Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18763","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":"sha256:230dafab08da91b5b1da252754d80c1c96d7ee09daa9d12f25cae72a76d334c7","target":"record","created_at":"2026-05-20T00:06:20Z","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":"e5fbed5a65f8e795eed544d7f6d3a67d78be1180b52480b1e69492191e9346ab","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-04-10T13:13:17Z","title_canon_sha256":"80be2ee7d247fb7dda8d242935b9e9343cc91ebff321650f2bfcee96c39c4304"},"schema_version":"1.0","source":{"id":"2605.18763","kind":"arxiv","version":1}},"canonical_sha256":"8d0ea6a80a98fce7d7db45ec2af351baeea1836267dc112908b00827265e2102","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d0ea6a80a98fce7d7db45ec2af351baeea1836267dc112908b00827265e2102","first_computed_at":"2026-05-20T00:06:20.626738Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:06:20.626738Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ifr606NUgTxrK5sOrpNmm9n2tUJyzvQm6P39atgpjEd9C259wpuKrJApIqdJVAP0tTt9NeHO6KyI5LbT16wxAw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:06:20.627563Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.18763","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:230dafab08da91b5b1da252754d80c1c96d7ee09daa9d12f25cae72a76d334c7","sha256:a9a29fd69c345d1d1f2c6b2345d2f5938caee8bd976eb777a7f4b7f2dc76f7f0"],"state_sha256":"79b4707af76a5665b980547bfe60f336abd9f76f57b117b5b7345df76abc8702"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FQVIbUwV8lxpBpLxXdJVIPCv6qx44omtcOa6/TAaF1EK0G7v27J0EZd/TqCr/PFdjz7QkeI6jIWznBaCwRzVBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T15:44:13.324986Z","bundle_sha256":"140145ce46c5f7e23e186847d1ad86fd43b79b38faa85323cc9e4bd6932af462"}}