{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5D7SKVSCLSDAHURCGIP5WELMUO","short_pith_number":"pith:5D7SKVSC","schema_version":"1.0","canonical_sha256":"e8ff2556425c8603d222321fdb116ca385ee1f2144dc599df7789a1d35037909","source":{"kind":"arxiv","id":"2507.16069","version":1},"attestation_state":"computed","paper":{"title":"Interpreting CFD Surrogates through Sparse Autoencoders","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CE","authors_text":"Shusen Liu, Yeping Hu","submitted_at":"2025-07-21T21:09:45Z","abstract_excerpt":"Learning-based surrogate models have become a practical alternative to high-fidelity CFD solvers, but their latent representations remain opaque and hinder adoption in safety-critical or regulation-bound settings. This work introduces a posthoc interpretability framework for graph-based surrogate models used in computational fluid dynamics (CFD) by leveraging sparse autoencoders (SAEs). By obtaining an overcomplete basis in the node embedding space of a pretrained surrogate, the method extracts a dictionary of interpretable latent features. The approach enables the identification of monosemant"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.16069","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2025-07-21T21:09:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d587127c0d12b8981b7b640a384522587816637d0b3a583de62da707ec0bd15c","abstract_canon_sha256":"2b8898691b3b786edf5a354e646396e931e84b9ec324f1fe47e1856224feb13f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:41:02.251485Z","signature_b64":"m0SlWm9ezP30U309U93eTeZPnYihhbwG7AM36BuuaEI89W5Du8SIzNaNY63INYUSTs+JBSkdQcE/xBMuNYg3AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8ff2556425c8603d222321fdb116ca385ee1f2144dc599df7789a1d35037909","last_reissued_at":"2026-07-05T11:41:02.250994Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:41:02.250994Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interpreting CFD Surrogates through Sparse Autoencoders","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CE","authors_text":"Shusen Liu, Yeping Hu","submitted_at":"2025-07-21T21:09:45Z","abstract_excerpt":"Learning-based surrogate models have become a practical alternative to high-fidelity CFD solvers, but their latent representations remain opaque and hinder adoption in safety-critical or regulation-bound settings. This work introduces a posthoc interpretability framework for graph-based surrogate models used in computational fluid dynamics (CFD) by leveraging sparse autoencoders (SAEs). By obtaining an overcomplete basis in the node embedding space of a pretrained surrogate, the method extracts a dictionary of interpretable latent features. The approach enables the identification of monosemant"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.16069","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/2507.16069/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.16069","created_at":"2026-07-05T11:41:02.251051+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.16069v1","created_at":"2026-07-05T11:41:02.251051+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.16069","created_at":"2026-07-05T11:41:02.251051+00:00"},{"alias_kind":"pith_short_12","alias_value":"5D7SKVSCLSDA","created_at":"2026-07-05T11:41:02.251051+00:00"},{"alias_kind":"pith_short_16","alias_value":"5D7SKVSCLSDAHURC","created_at":"2026-07-05T11:41:02.251051+00:00"},{"alias_kind":"pith_short_8","alias_value":"5D7SKVSC","created_at":"2026-07-05T11:41:02.251051+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25115","citing_title":"Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5D7SKVSCLSDAHURCGIP5WELMUO","json":"https://pith.science/pith/5D7SKVSCLSDAHURCGIP5WELMUO.json","graph_json":"https://pith.science/api/pith-number/5D7SKVSCLSDAHURCGIP5WELMUO/graph.json","events_json":"https://pith.science/api/pith-number/5D7SKVSCLSDAHURCGIP5WELMUO/events.json","paper":"https://pith.science/paper/5D7SKVSC"},"agent_actions":{"view_html":"https://pith.science/pith/5D7SKVSCLSDAHURCGIP5WELMUO","download_json":"https://pith.science/pith/5D7SKVSCLSDAHURCGIP5WELMUO.json","view_paper":"https://pith.science/paper/5D7SKVSC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.16069&json=true","fetch_graph":"https://pith.science/api/pith-number/5D7SKVSCLSDAHURCGIP5WELMUO/graph.json","fetch_events":"https://pith.science/api/pith-number/5D7SKVSCLSDAHURCGIP5WELMUO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5D7SKVSCLSDAHURCGIP5WELMUO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5D7SKVSCLSDAHURCGIP5WELMUO/action/storage_attestation","attest_author":"https://pith.science/pith/5D7SKVSCLSDAHURCGIP5WELMUO/action/author_attestation","sign_citation":"https://pith.science/pith/5D7SKVSCLSDAHURCGIP5WELMUO/action/citation_signature","submit_replication":"https://pith.science/pith/5D7SKVSCLSDAHURCGIP5WELMUO/action/replication_record"}},"created_at":"2026-07-05T11:41:02.251051+00:00","updated_at":"2026-07-05T11:41:02.251051+00:00"}