{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:THBYSEWLMHKDHXHB5VJ3OVJTT4","short_pith_number":"pith:THBYSEWL","canonical_record":{"source":{"id":"2608.11937","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-08-12T11:24:08Z","cross_cats_sorted":[],"title_canon_sha256":"c18cf6f68b0144c311b62d696fa0b7a39939dd754d157a4a88363537e49e9385","abstract_canon_sha256":"813e7222341e3b13ad78f985665e7a1ecad3f862a5b99043a908862f4fa8f9dd"},"schema_version":"1.0"},"canonical_sha256":"99c38912cb61d433dce1ed53b755339f3051a2068db32b579695c41985dcb897","source":{"kind":"arxiv","id":"2608.11937","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.11937","created_at":"2026-08-13T01:28:59Z"},{"alias_kind":"arxiv_version","alias_value":"2608.11937v1","created_at":"2026-08-13T01:28:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.11937","created_at":"2026-08-13T01:28:59Z"},{"alias_kind":"pith_short_12","alias_value":"THBYSEWLMHKD","created_at":"2026-08-13T01:28:59Z"},{"alias_kind":"pith_short_16","alias_value":"THBYSEWLMHKDHXHB","created_at":"2026-08-13T01:28:59Z"},{"alias_kind":"pith_short_8","alias_value":"THBYSEWL","created_at":"2026-08-13T01:28:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:THBYSEWLMHKDHXHB5VJ3OVJTT4","target":"record","payload":{"canonical_record":{"source":{"id":"2608.11937","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-08-12T11:24:08Z","cross_cats_sorted":[],"title_canon_sha256":"c18cf6f68b0144c311b62d696fa0b7a39939dd754d157a4a88363537e49e9385","abstract_canon_sha256":"813e7222341e3b13ad78f985665e7a1ecad3f862a5b99043a908862f4fa8f9dd"},"schema_version":"1.0"},"canonical_sha256":"99c38912cb61d433dce1ed53b755339f3051a2068db32b579695c41985dcb897","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-13T01:28:59.987455Z","signature_b64":"OijtwuOmjV4MnYV2rFUcPOEOP4Ueef34LVa/KsDtJYneIrbwlHdX79s+R19wyldTmWrLc3oSCmkKKcbVE/8cDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"99c38912cb61d433dce1ed53b755339f3051a2068db32b579695c41985dcb897","last_reissued_at":"2026-08-13T01:28:59.985228Z","signature_status":"signed_v1","first_computed_at":"2026-08-13T01:28:59.985228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.11937","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":"2026-08-13T01:28:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VGWqX90mkgy61KKNCiy+MfL8V6lq07DqyM9AQs5y+5O3gl3nL/Jbx+10bj4lHaEoFM5SIECqcgS6HUUbzShiAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T13:06:02.615494Z"},"content_sha256":"45c1bcb62c1786c8b22139d56270838387526758c710cce10cca0ecaa35245b4","schema_version":"1.0","event_id":"sha256:45c1bcb62c1786c8b22139d56270838387526758c710cce10cca0ecaa35245b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:THBYSEWLMHKDHXHB5VJ3OVJTT4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Distillation of Foundation Models for Time-dependent PDEs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andrei Manolache, Boshra Ariguib, Daniel Musekamp, Mathias Niepert","submitted_at":"2026-08-12T11:24:08Z","abstract_excerpt":"Foundation models for time-dependent partial differential equations (PDEs) are trained on large and diverse collections of physical systems and can generalize effectively to new downstream tasks. After fine-tuning on only a few trajectories from a target domain, they can achieve strong accuracy in low-data regimes. However, these models are typically large and computationally intensive, limiting their usefulness as fast surrogates for numerical solvers. We propose Teacher Rollout Extension (TREX), a knowledge distillation framework that transfers the predictive capability of a pretrained found"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.11937","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/2608.11937/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":"2026-08-13T01:28:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gdMnv57HeBayX8QfXYaW8A5PdpokNDaY73eXOVhvHx1PwCU1iYNptWyP3A6vlszMGPuobaSbrrEUiXhkSXGKAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T13:06:02.616039Z"},"content_sha256":"1ac83d310781ea86b254b21edfe9204c5646c8a0cf4285491a7b1801551c4c94","schema_version":"1.0","event_id":"sha256:1ac83d310781ea86b254b21edfe9204c5646c8a0cf4285491a7b1801551c4c94"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/THBYSEWLMHKDHXHB5VJ3OVJTT4/bundle.json","state_url":"https://pith.science/pith/THBYSEWLMHKDHXHB5VJ3OVJTT4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/THBYSEWLMHKDHXHB5VJ3OVJTT4/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":"2026-08-17T13:06:02Z","links":{"resolver":"https://pith.science/pith/THBYSEWLMHKDHXHB5VJ3OVJTT4","bundle":"https://pith.science/pith/THBYSEWLMHKDHXHB5VJ3OVJTT4/bundle.json","state":"https://pith.science/pith/THBYSEWLMHKDHXHB5VJ3OVJTT4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/THBYSEWLMHKDHXHB5VJ3OVJTT4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:THBYSEWLMHKDHXHB5VJ3OVJTT4","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":"813e7222341e3b13ad78f985665e7a1ecad3f862a5b99043a908862f4fa8f9dd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-08-12T11:24:08Z","title_canon_sha256":"c18cf6f68b0144c311b62d696fa0b7a39939dd754d157a4a88363537e49e9385"},"schema_version":"1.0","source":{"id":"2608.11937","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.11937","created_at":"2026-08-13T01:28:59Z"},{"alias_kind":"arxiv_version","alias_value":"2608.11937v1","created_at":"2026-08-13T01:28:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.11937","created_at":"2026-08-13T01:28:59Z"},{"alias_kind":"pith_short_12","alias_value":"THBYSEWLMHKD","created_at":"2026-08-13T01:28:59Z"},{"alias_kind":"pith_short_16","alias_value":"THBYSEWLMHKDHXHB","created_at":"2026-08-13T01:28:59Z"},{"alias_kind":"pith_short_8","alias_value":"THBYSEWL","created_at":"2026-08-13T01:28:59Z"}],"graph_snapshots":[{"event_id":"sha256:1ac83d310781ea86b254b21edfe9204c5646c8a0cf4285491a7b1801551c4c94","target":"graph","created_at":"2026-08-13T01:28:59Z","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/2608.11937/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Foundation models for time-dependent partial differential equations (PDEs) are trained on large and diverse collections of physical systems and can generalize effectively to new downstream tasks. After fine-tuning on only a few trajectories from a target domain, they can achieve strong accuracy in low-data regimes. However, these models are typically large and computationally intensive, limiting their usefulness as fast surrogates for numerical solvers. We propose Teacher Rollout Extension (TREX), a knowledge distillation framework that transfers the predictive capability of a pretrained found","authors_text":"Andrei Manolache, Boshra Ariguib, Daniel Musekamp, Mathias Niepert","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-08-12T11:24:08Z","title":"Distillation of Foundation Models for Time-dependent PDEs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.11937","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":"sha256:45c1bcb62c1786c8b22139d56270838387526758c710cce10cca0ecaa35245b4","target":"record","created_at":"2026-08-13T01:28:59Z","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":"813e7222341e3b13ad78f985665e7a1ecad3f862a5b99043a908862f4fa8f9dd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-08-12T11:24:08Z","title_canon_sha256":"c18cf6f68b0144c311b62d696fa0b7a39939dd754d157a4a88363537e49e9385"},"schema_version":"1.0","source":{"id":"2608.11937","kind":"arxiv","version":1}},"canonical_sha256":"99c38912cb61d433dce1ed53b755339f3051a2068db32b579695c41985dcb897","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"99c38912cb61d433dce1ed53b755339f3051a2068db32b579695c41985dcb897","first_computed_at":"2026-08-13T01:28:59.985228Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-13T01:28:59.985228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OijtwuOmjV4MnYV2rFUcPOEOP4Ueef34LVa/KsDtJYneIrbwlHdX79s+R19wyldTmWrLc3oSCmkKKcbVE/8cDw==","signature_status":"signed_v1","signed_at":"2026-08-13T01:28:59.987455Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.11937","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:45c1bcb62c1786c8b22139d56270838387526758c710cce10cca0ecaa35245b4","sha256:1ac83d310781ea86b254b21edfe9204c5646c8a0cf4285491a7b1801551c4c94"],"state_sha256":"756a45b71a5e5416ef017fa6a99948d6c3c3c75ffdb6caa0505a9635d50221e1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lIIkcSBJTsGddAsUjMg0I8b2e7L57npLqsFXpVPBSR+CQupj4y/V2P0kE4/aPAtOBUdMUCgnHFqaZjvPXdq3BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T13:06:02.635824Z","bundle_sha256":"55415fd58525a9c74f3b4ff2ae5a88c233eef756d03017f86cf1886320809ae5"}}