{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:R7SRMQCJU4RPLXXPPDAEVTNDIE","short_pith_number":"pith:R7SRMQCJ","canonical_record":{"source":{"id":"2608.08876","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-08-09T19:31:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4fcbc6a1e5051e716f3b4ea0596d25a763e426f20d14a2f153e3002725d23092","abstract_canon_sha256":"2dbe148d470fb200e8d7be087b2b0ed4f893a7d235b11915d29a3a8b6c991e02"},"schema_version":"1.0"},"canonical_sha256":"8fe5164049a722f5deef78c04acda3411e3df7fd6717fdb48646dc96cb869bbd","source":{"kind":"arxiv","id":"2608.08876","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.08876","created_at":"2026-08-11T01:25:35Z"},{"alias_kind":"arxiv_version","alias_value":"2608.08876v1","created_at":"2026-08-11T01:25:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.08876","created_at":"2026-08-11T01:25:35Z"},{"alias_kind":"pith_short_12","alias_value":"R7SRMQCJU4RP","created_at":"2026-08-11T01:25:35Z"},{"alias_kind":"pith_short_16","alias_value":"R7SRMQCJU4RPLXXP","created_at":"2026-08-11T01:25:35Z"},{"alias_kind":"pith_short_8","alias_value":"R7SRMQCJ","created_at":"2026-08-11T01:25:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:R7SRMQCJU4RPLXXPPDAEVTNDIE","target":"record","payload":{"canonical_record":{"source":{"id":"2608.08876","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-08-09T19:31:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4fcbc6a1e5051e716f3b4ea0596d25a763e426f20d14a2f153e3002725d23092","abstract_canon_sha256":"2dbe148d470fb200e8d7be087b2b0ed4f893a7d235b11915d29a3a8b6c991e02"},"schema_version":"1.0"},"canonical_sha256":"8fe5164049a722f5deef78c04acda3411e3df7fd6717fdb48646dc96cb869bbd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-11T01:25:35.873066Z","signature_b64":"B59OZGpXPAkgyddiqhi96O6nnThmm20XNOwVSTRqebBZ8mm6I2Aik+hcIFzOCDsmdPxeQsJAYCLl9FBv2kQqAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8fe5164049a722f5deef78c04acda3411e3df7fd6717fdb48646dc96cb869bbd","last_reissued_at":"2026-08-11T01:25:35.870471Z","signature_status":"signed_v1","first_computed_at":"2026-08-11T01:25:35.870471Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.08876","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":"2026-08-11T01:25:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E+wAr02lcQntHTnR9A+J/0afaeU9vnPRMGccMf5V6CY0a+/cR8WpBS5ELsj2c+nrK3PWoeGJgePgJaJ2YF6YAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T08:36:53.092265Z"},"content_sha256":"cb4cfbff803c754c3765caae0bb5b809eca0b105ba3f4e22455d64f554e8529c","schema_version":"1.0","event_id":"sha256:cb4cfbff803c754c3765caae0bb5b809eca0b105ba3f4e22455d64f554e8529c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:R7SRMQCJU4RPLXXPPDAEVTNDIE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Inductive Graph Layout with Implicit Neural Fields","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.HC","authors_text":"Berfin Inal, Daniel Probst","submitted_at":"2026-08-09T19:31:12Z","abstract_excerpt":"A graph layout is normally a table of $N$ free coordinates. We optimise a function with a fixed number of parameters instead. This gives a drawing a sample complexity and an extensible domain. Force-directed algorithms remain the standard tools for graph drawing. The most accurate among them minimise stress in the Kamada-Kawai formulation by directly optimising the node coordinates, at a full objective cost of $O(N^2)$ in time and space. Here, we propose Fling (Field Layout via Implicit Neural Geometry), a small neural network mapping the distances of each node to a set of landmarks, positioni"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.08876","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/2608.08876/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":"2026-08-11T01:25:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mTz/HSJAzrreRhkg3lMNnHYA6KRqvMBNQDHgTWeN7HGzG+FpIzj7/mA5cjRoSxIU6/K1bV0nbehTYE0tzrFMAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T08:36:53.092641Z"},"content_sha256":"0ac5e7613de80b887a87b20bb39bbeba1430d951e6d5a34f3c6ae7d7b6b0cf18","schema_version":"1.0","event_id":"sha256:0ac5e7613de80b887a87b20bb39bbeba1430d951e6d5a34f3c6ae7d7b6b0cf18"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:R7SRMQCJU4RPLXXPPDAEVTNDIE","target":"integrity","payload":{"note":"Identifier '10.1109/tvcg.2022' 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":"Reyan Ahmed, Felice De Luca, Sabin Devkota, Stephen Kobourov, and Mingwei Li. Multicrite- ria scalable graph drawing via stochastic gradient descent, (sgd)2(sgd)2.IEEE Transactions on Visualization and Computer Graphics, 28(6):2388–2399, 20","arxiv_id":"2608.08876","detector":"doi_compliance","evidence":{"doi":"10.1109/tvcg.2022","arxiv_id":null,"ref_index":20,"raw_excerpt":"Reyan Ahmed, Felice De Luca, Sabin Devkota, Stephen Kobourov, and Mingwei Li. Multicrite- ria scalable graph drawing via stochastic gradient descent, (sgd)2(sgd)2.IEEE Transactions on Visualization and Computer Graphics, 28(6):2388–2399, 2022. doi: 10.1109/TVCG.2022. 3155564. 3","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":20,"audited_at":"2026-08-14T04:49:09.996062Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/tvcg.2022","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"762b3d2d3184a40214054f7358cc8748f099aa92229783819e55e208d40f867f","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":19532,"payload_sha256":"96ef5ad0b7ad8ff98e33ff0690cf905e324a2b2ef695b48ced16065d9c55fa78","signature_b64":"ESsW41RmxyD0n2HuuyYKEtc/XTvLZKhhvmCmEt6vW4tyoarj+InfIoAytkyUaCtW0AZx6BZzzGOJqzl81EvdAQ==","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":"2026-08-14T04:53:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hM+fx7QrhACspU2uq4kksLlz4E1TNk43pdCUka9j7OXmwvUVrLWb9yoFRG98vjpII99SFYT/ieODORmzKoHTAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T08:36:53.094502Z"},"content_sha256":"5ebd78b5a3bd023c7e4b409ec5cd3691b8a5c62e57eb1686a1db2c934d0db4fa","schema_version":"1.0","event_id":"sha256:5ebd78b5a3bd023c7e4b409ec5cd3691b8a5c62e57eb1686a1db2c934d0db4fa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R7SRMQCJU4RPLXXPPDAEVTNDIE/bundle.json","state_url":"https://pith.science/pith/R7SRMQCJU4RPLXXPPDAEVTNDIE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R7SRMQCJU4RPLXXPPDAEVTNDIE/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":"2026-08-18T08:36:53Z","links":{"resolver":"https://pith.science/pith/R7SRMQCJU4RPLXXPPDAEVTNDIE","bundle":"https://pith.science/pith/R7SRMQCJU4RPLXXPPDAEVTNDIE/bundle.json","state":"https://pith.science/pith/R7SRMQCJU4RPLXXPPDAEVTNDIE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R7SRMQCJU4RPLXXPPDAEVTNDIE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:R7SRMQCJU4RPLXXPPDAEVTNDIE","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2dbe148d470fb200e8d7be087b2b0ed4f893a7d235b11915d29a3a8b6c991e02","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-08-09T19:31:12Z","title_canon_sha256":"4fcbc6a1e5051e716f3b4ea0596d25a763e426f20d14a2f153e3002725d23092"},"schema_version":"1.0","source":{"id":"2608.08876","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.08876","created_at":"2026-08-11T01:25:35Z"},{"alias_kind":"arxiv_version","alias_value":"2608.08876v1","created_at":"2026-08-11T01:25:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.08876","created_at":"2026-08-11T01:25:35Z"},{"alias_kind":"pith_short_12","alias_value":"R7SRMQCJU4RP","created_at":"2026-08-11T01:25:35Z"},{"alias_kind":"pith_short_16","alias_value":"R7SRMQCJU4RPLXXP","created_at":"2026-08-11T01:25:35Z"},{"alias_kind":"pith_short_8","alias_value":"R7SRMQCJ","created_at":"2026-08-11T01:25:35Z"}],"graph_snapshots":[{"event_id":"sha256:0ac5e7613de80b887a87b20bb39bbeba1430d951e6d5a34f3c6ae7d7b6b0cf18","target":"graph","created_at":"2026-08-11T01:25:35Z","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/2608.08876/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A graph layout is normally a table of $N$ free coordinates. We optimise a function with a fixed number of parameters instead. This gives a drawing a sample complexity and an extensible domain. Force-directed algorithms remain the standard tools for graph drawing. The most accurate among them minimise stress in the Kamada-Kawai formulation by directly optimising the node coordinates, at a full objective cost of $O(N^2)$ in time and space. Here, we propose Fling (Field Layout via Implicit Neural Geometry), a small neural network mapping the distances of each node to a set of landmarks, positioni","authors_text":"Berfin Inal, Daniel Probst","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-08-09T19:31:12Z","title":"Inductive Graph Layout with Implicit Neural Fields"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.08876","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:cb4cfbff803c754c3765caae0bb5b809eca0b105ba3f4e22455d64f554e8529c","target":"record","created_at":"2026-08-11T01:25:35Z","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":"2dbe148d470fb200e8d7be087b2b0ed4f893a7d235b11915d29a3a8b6c991e02","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-08-09T19:31:12Z","title_canon_sha256":"4fcbc6a1e5051e716f3b4ea0596d25a763e426f20d14a2f153e3002725d23092"},"schema_version":"1.0","source":{"id":"2608.08876","kind":"arxiv","version":1}},"canonical_sha256":"8fe5164049a722f5deef78c04acda3411e3df7fd6717fdb48646dc96cb869bbd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8fe5164049a722f5deef78c04acda3411e3df7fd6717fdb48646dc96cb869bbd","first_computed_at":"2026-08-11T01:25:35.870471Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-11T01:25:35.870471Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B59OZGpXPAkgyddiqhi96O6nnThmm20XNOwVSTRqebBZ8mm6I2Aik+hcIFzOCDsmdPxeQsJAYCLl9FBv2kQqAg==","signature_status":"signed_v1","signed_at":"2026-08-11T01:25:35.873066Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.08876","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb4cfbff803c754c3765caae0bb5b809eca0b105ba3f4e22455d64f554e8529c","sha256:0ac5e7613de80b887a87b20bb39bbeba1430d951e6d5a34f3c6ae7d7b6b0cf18","sha256:5ebd78b5a3bd023c7e4b409ec5cd3691b8a5c62e57eb1686a1db2c934d0db4fa"],"state_sha256":"f07d7d89dae09b1ffa6a05f590425adf1112f214c756ef9b3dd37e832d57320d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RXQQbdORcAWZ3KDzgugeaP+BVYlkXngwVrIEMHPP/0dcaigU/UcGRuxJ9yCjIlt6eEUeCUUFYGVkTWrkyJ/0DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T08:36:53.096046Z","bundle_sha256":"18891625b03ac8ceb04faed452a9d24b29e4fbb7538dd297d902cbc5dfa6668e"}}