{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:6KPUZ2QHW2Z6UDDVV7DVSTWMPU","short_pith_number":"pith:6KPUZ2QH","canonical_record":{"source":{"id":"2606.20167","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-18T12:35:14Z","cross_cats_sorted":[],"title_canon_sha256":"d7f5d3efd45283790f11c0d0961f449093243a4add521efdb716953b0772302c","abstract_canon_sha256":"4f9d79dfdd173ba8172072c53b80d94c8b9311ed87130b8b268ad97661efbfd2"},"schema_version":"1.0"},"canonical_sha256":"f29f4cea07b6b3ea0c75afc7594ecc7d0c49fd288f46387205d7bc6ca0040b4e","source":{"kind":"arxiv","id":"2606.20167","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.20167","created_at":"2026-06-19T16:13:04Z"},{"alias_kind":"arxiv_version","alias_value":"2606.20167v1","created_at":"2026-06-19T16:13:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.20167","created_at":"2026-06-19T16:13:04Z"},{"alias_kind":"pith_short_12","alias_value":"6KPUZ2QHW2Z6","created_at":"2026-06-19T16:13:04Z"},{"alias_kind":"pith_short_16","alias_value":"6KPUZ2QHW2Z6UDDV","created_at":"2026-06-19T16:13:04Z"},{"alias_kind":"pith_short_8","alias_value":"6KPUZ2QH","created_at":"2026-06-19T16:13:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:6KPUZ2QHW2Z6UDDVV7DVSTWMPU","target":"record","payload":{"canonical_record":{"source":{"id":"2606.20167","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-18T12:35:14Z","cross_cats_sorted":[],"title_canon_sha256":"d7f5d3efd45283790f11c0d0961f449093243a4add521efdb716953b0772302c","abstract_canon_sha256":"4f9d79dfdd173ba8172072c53b80d94c8b9311ed87130b8b268ad97661efbfd2"},"schema_version":"1.0"},"canonical_sha256":"f29f4cea07b6b3ea0c75afc7594ecc7d0c49fd288f46387205d7bc6ca0040b4e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:13:04.555132Z","signature_b64":"w/l58zkk3fywgM+4SbDRnxZAd11MxBQPbCOiWEvBiUuizMnFu34PbmP/bJnibx0QUBvvY4V0DS44e6+gN2uXBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f29f4cea07b6b3ea0c75afc7594ecc7d0c49fd288f46387205d7bc6ca0040b4e","last_reissued_at":"2026-06-19T16:13:04.554721Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:13:04.554721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.20167","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":"2026-06-19T16:13:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yIf0fxzBfvELKSO5JV52Abv652BEmXDrsp8DV2Doyo7sVx1l5YqRwPgD0qPvfFZSl3u7HEowTdC5DvKbBsgqDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T16:03:33.329554Z"},"content_sha256":"50686d7bcae182551a1568fba94325f098885d72363448b8b68e24153bd991ab","schema_version":"1.0","event_id":"sha256:50686d7bcae182551a1568fba94325f098885d72363448b8b68e24153bd991ab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:6KPUZ2QHW2Z6UDDVV7DVSTWMPU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Modal Contrastive Learning for Implicit Earth Embeddings via Location Tying","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jonathan Hecht, Lukas Arzoumanidis, Youness Dehbi, Ziyue Li","submitted_at":"2026-06-18T12:35:14Z","abstract_excerpt":"Spatial prediction tasks are often limited by a lack of high-quality labelled ground-truth observations. To overcome this challenge, self-supervised pre-training is a possible solution, with contrastive learning dominant for location encoders. Those approaches usually align geographic coordinates with just one additional modality. We propose two multimodal contrastive learning architectures: Multimodal Embedding via Location Tying (MELT) and Sequential Alternating Location Training (SALT). These architectures expand this framework beyond two modalities by utilising unpaired geospatial data. Bo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.20167","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/2606.20167/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":"2026-06-19T16:13:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GPchdGwPYqfEbaVxNeFEBdIxVqORSUblkaqsPZWV87462WR2ozdeabmf6H1i/Flxj9a21xpuW/FEnfDu3XcoDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T16:03:33.329930Z"},"content_sha256":"bb8a3561d82e2cb775b65297ad2b5ce214c3e12963f60e5cca58d993d964d891","schema_version":"1.0","event_id":"sha256:bb8a3561d82e2cb775b65297ad2b5ce214c3e12963f60e5cca58d993d964d891"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6KPUZ2QHW2Z6UDDVV7DVSTWMPU/bundle.json","state_url":"https://pith.science/pith/6KPUZ2QHW2Z6UDDVV7DVSTWMPU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6KPUZ2QHW2Z6UDDVV7DVSTWMPU/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":"2026-06-22T16:03:33Z","links":{"resolver":"https://pith.science/pith/6KPUZ2QHW2Z6UDDVV7DVSTWMPU","bundle":"https://pith.science/pith/6KPUZ2QHW2Z6UDDVV7DVSTWMPU/bundle.json","state":"https://pith.science/pith/6KPUZ2QHW2Z6UDDVV7DVSTWMPU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6KPUZ2QHW2Z6UDDVV7DVSTWMPU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:6KPUZ2QHW2Z6UDDVV7DVSTWMPU","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":"4f9d79dfdd173ba8172072c53b80d94c8b9311ed87130b8b268ad97661efbfd2","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-18T12:35:14Z","title_canon_sha256":"d7f5d3efd45283790f11c0d0961f449093243a4add521efdb716953b0772302c"},"schema_version":"1.0","source":{"id":"2606.20167","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.20167","created_at":"2026-06-19T16:13:04Z"},{"alias_kind":"arxiv_version","alias_value":"2606.20167v1","created_at":"2026-06-19T16:13:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.20167","created_at":"2026-06-19T16:13:04Z"},{"alias_kind":"pith_short_12","alias_value":"6KPUZ2QHW2Z6","created_at":"2026-06-19T16:13:04Z"},{"alias_kind":"pith_short_16","alias_value":"6KPUZ2QHW2Z6UDDV","created_at":"2026-06-19T16:13:04Z"},{"alias_kind":"pith_short_8","alias_value":"6KPUZ2QH","created_at":"2026-06-19T16:13:04Z"}],"graph_snapshots":[{"event_id":"sha256:bb8a3561d82e2cb775b65297ad2b5ce214c3e12963f60e5cca58d993d964d891","target":"graph","created_at":"2026-06-19T16:13:04Z","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/2606.20167/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spatial prediction tasks are often limited by a lack of high-quality labelled ground-truth observations. To overcome this challenge, self-supervised pre-training is a possible solution, with contrastive learning dominant for location encoders. Those approaches usually align geographic coordinates with just one additional modality. We propose two multimodal contrastive learning architectures: Multimodal Embedding via Location Tying (MELT) and Sequential Alternating Location Training (SALT). These architectures expand this framework beyond two modalities by utilising unpaired geospatial data. Bo","authors_text":"Jonathan Hecht, Lukas Arzoumanidis, Youness Dehbi, Ziyue Li","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-18T12:35:14Z","title":"Multi-Modal Contrastive Learning for Implicit Earth Embeddings via Location Tying"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.20167","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":"sha256:50686d7bcae182551a1568fba94325f098885d72363448b8b68e24153bd991ab","target":"record","created_at":"2026-06-19T16:13:04Z","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":"4f9d79dfdd173ba8172072c53b80d94c8b9311ed87130b8b268ad97661efbfd2","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-18T12:35:14Z","title_canon_sha256":"d7f5d3efd45283790f11c0d0961f449093243a4add521efdb716953b0772302c"},"schema_version":"1.0","source":{"id":"2606.20167","kind":"arxiv","version":1}},"canonical_sha256":"f29f4cea07b6b3ea0c75afc7594ecc7d0c49fd288f46387205d7bc6ca0040b4e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f29f4cea07b6b3ea0c75afc7594ecc7d0c49fd288f46387205d7bc6ca0040b4e","first_computed_at":"2026-06-19T16:13:04.554721Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-19T16:13:04.554721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w/l58zkk3fywgM+4SbDRnxZAd11MxBQPbCOiWEvBiUuizMnFu34PbmP/bJnibx0QUBvvY4V0DS44e6+gN2uXBg==","signature_status":"signed_v1","signed_at":"2026-06-19T16:13:04.555132Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.20167","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:50686d7bcae182551a1568fba94325f098885d72363448b8b68e24153bd991ab","sha256:bb8a3561d82e2cb775b65297ad2b5ce214c3e12963f60e5cca58d993d964d891"],"state_sha256":"257f7ae9fb23119245f66d18e10b589854a05c09a901aa2db2ee8e40c8f5837a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hVt415hIBEzLdHYlj83IlGJMWBr+FTjln05mIpkSztDId3iaVYNvjD9hio+r2hosc0pGRWB9PecktRBCGY5xDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-22T16:03:33.331968Z","bundle_sha256":"373f57bd7920d0698580fc5263cadc2eb391fbed51b230975b51c15a8e8e7d46"}}