{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ATV4JSQXNMYZHKTTHVKN7FJLUW","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":"52d732371994c8e5ab8b763f46dce4dce7d00612d2e6def4f645463b3c44ac01","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T08:25:16Z","title_canon_sha256":"b8008a0141f566cfe3feb19e231f920ef9ab9aa55d7ccddb03cfade9ebfe208a"},"schema_version":"1.0","source":{"id":"2605.14546","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14546","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14546v1","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14546","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"pith_short_12","alias_value":"ATV4JSQXNMYZ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"ATV4JSQXNMYZHKTT","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"ATV4JSQX","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:aeb3de4e96f2c2c4c20d01f8867cfb67c99ba955502e0e633206461f814f6ecf","target":"graph","created_at":"2026-05-17T23:39:05Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"endpoint fine-tuning is not arbitrary checkpoint drift, but reveals a calibratable physical direction for training-free transfer across PDE regimes."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"that the observed separation of weight updates into family-shared adaptation and a direction aligned with the underlying physical parameter is stable, generalizable, and not an artifact of the specific fine-tuning procedure or chosen regimes."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Fine-tuning neural PDE operators to regime endpoints reveals a physical direction in weight space that CCM uses to compose accurate merged models for new or extrapolated regimes from metadata or short prefixes."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Fine-tuning endpoint experts on a shared neural PDE operator reveals a reusable physical direction in weight space for training-free regime composition."}],"snapshot_sha256":"70d2524f89757de129312388f2342a1ec6da36dbf1d0f34f069a45211b0cc214"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"bd556019bbca616847ed6d52f8db497501f2be690eabf490c9982e0cb033418e"},"paper":{"abstract_excerpt":"Recent advances in neural operators have made partial differential equation (PDE) surrogate modeling increasingly scalable and transferable through large-scale pretraining and in-context adaptation. However, after a shared operator is fine-tuned to multiple regimes within a continuous physical family, it remains unclear whether the resulting weight-space updates merely form isolated regime experts or reveal reusable physical structure. Starting from a shared family anchor, we fine-tune low- and high-regime endpoint experts and show that their updates can be separated into a family-shared adapt","authors_text":"Dong Ni, Guanyu Chen, Pengkai Wang, Pengwei Liu, Qixin Zhang, Xiaolong Li, Xingyu Ren, Yuanyi Wang, Yuting Kong, Zhongkai Hao","cross_cats":[],"headline":"Fine-tuning endpoint experts on a shared neural PDE operator reveals a reusable physical direction in weight space for training-free regime composition.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T08:25:16Z","title":"Discovering Physical Directions in Weight Space: Composing Neural PDE Experts"},"references":{"count":61,"internal_anchors":5,"resolved_work":61,"sample":[{"cited_arxiv_id":"2010.08895","doi":"","is_internal_anchor":true,"ref_index":1,"title":"Fourier Neural Operator for Parametric Partial Differential Equations","work_id":"cc647655-121e-4055-85f0-fad4530ea964","year":2010},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Neural operators for accelerating scientific simulations and design","work_id":"3109e4ff-48e4-4cab-ba3f-8be9e47a7450","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Gnot: A general neural operator transformer for operator learning","work_id":"06a918b1-6e2e-4214-94e7-ad376a5a17a6","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Laplace neural operator for solving differential equations.Nature Machine Intelligence, 6(6):631–640, 2024","work_id":"aa762c8c-c93a-4842-bcbd-cbcd905ca12d","year":2024},{"cited_arxiv_id":"2003.03485","doi":"","is_internal_anchor":true,"ref_index":5,"title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","work_id":"00a591bc-6cad-477c-be12-26d5623f625d","year":2003}],"snapshot_sha256":"429b5eac9c2d5f15ac060d5a63da028286993f7a3ece7010df57293576b057c3"},"source":{"id":"2605.14546","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T01:59:33.617795Z","id":"79fad18b-0a94-43e0-b49e-3f8421cbcd10","model_set":{"reader":"grok-4.3"},"one_line_summary":"Fine-tuning neural PDE operators to regime endpoints reveals a physical direction in weight space that CCM uses to compose accurate merged models for new or extrapolated regimes from metadata or short prefixes.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Fine-tuning endpoint experts on a shared neural PDE operator reveals a reusable physical direction in weight space for training-free regime composition.","strongest_claim":"endpoint fine-tuning is not arbitrary checkpoint drift, but reveals a calibratable physical direction for training-free transfer across PDE regimes.","weakest_assumption":"that the observed separation of weight updates into family-shared adaptation and a direction aligned with the underlying physical parameter is stable, generalizable, and not an artifact of the specific fine-tuning procedure or chosen regimes."}},"verdict_id":"79fad18b-0a94-43e0-b49e-3f8421cbcd10"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b53e86d4f4f3395bff93268b7e0b409b5f71879cb66496e120bfcf48e01107ce","target":"record","created_at":"2026-05-17T23:39:05Z","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":"52d732371994c8e5ab8b763f46dce4dce7d00612d2e6def4f645463b3c44ac01","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T08:25:16Z","title_canon_sha256":"b8008a0141f566cfe3feb19e231f920ef9ab9aa55d7ccddb03cfade9ebfe208a"},"schema_version":"1.0","source":{"id":"2605.14546","kind":"arxiv","version":1}},"canonical_sha256":"04ebc4ca176b3193aa733d54df952ba5a6ef8123125170c3433db4da892421ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"04ebc4ca176b3193aa733d54df952ba5a6ef8123125170c3433db4da892421ce","first_computed_at":"2026-05-17T23:39:05.764339Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:05.764339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ydf01myMbuVWPdbIwbaEokQRwX3gHMqZ0P+Bejst34laM11pWiFZ3gtdwhyyUQy3LueimDJvv1cXQgPHhxRHBA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:05.765051Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.14546","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b53e86d4f4f3395bff93268b7e0b409b5f71879cb66496e120bfcf48e01107ce","sha256:aeb3de4e96f2c2c4c20d01f8867cfb67c99ba955502e0e633206461f814f6ecf"],"state_sha256":"1fc335b9cfed7cbdaaedb227311129bc3958459c4eb91f0ae467a917627767a8"}