{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:OEO52C5ZYYWHT7OIIRZDEA3B53","short_pith_number":"pith:OEO52C5Z","canonical_record":{"source":{"id":"2505.07956","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-12T18:00:41Z","cross_cats_sorted":["cs.NE","cs.SC"],"title_canon_sha256":"4805947eccb7d5fee2b5ac1bc1470164dd43e734b17facd7d785939f95eb898b","abstract_canon_sha256":"e2097451fa4fa3572f72a320a19f50ac0c439d3d0e0eac2c1cfaf8a366e81d47"},"schema_version":"1.0"},"canonical_sha256":"711ddd0bb9c62c79fdc84472320361eef4e082af023bffdeee55d0bf74210d61","source":{"kind":"arxiv","id":"2505.07956","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.07956","created_at":"2026-07-05T11:05:24Z"},{"alias_kind":"arxiv_version","alias_value":"2505.07956v1","created_at":"2026-07-05T11:05:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.07956","created_at":"2026-07-05T11:05:24Z"},{"alias_kind":"pith_short_12","alias_value":"OEO52C5ZYYWH","created_at":"2026-07-05T11:05:24Z"},{"alias_kind":"pith_short_16","alias_value":"OEO52C5ZYYWHT7OI","created_at":"2026-07-05T11:05:24Z"},{"alias_kind":"pith_short_8","alias_value":"OEO52C5Z","created_at":"2026-07-05T11:05:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:OEO52C5ZYYWHT7OIIRZDEA3B53","target":"record","payload":{"canonical_record":{"source":{"id":"2505.07956","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-12T18:00:41Z","cross_cats_sorted":["cs.NE","cs.SC"],"title_canon_sha256":"4805947eccb7d5fee2b5ac1bc1470164dd43e734b17facd7d785939f95eb898b","abstract_canon_sha256":"e2097451fa4fa3572f72a320a19f50ac0c439d3d0e0eac2c1cfaf8a366e81d47"},"schema_version":"1.0"},"canonical_sha256":"711ddd0bb9c62c79fdc84472320361eef4e082af023bffdeee55d0bf74210d61","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:24.834154Z","signature_b64":"+WTxLRSGOIo5+ANfEcN18BO70HAtQUy91PgmHac5XTB0aZEPEj+Vs9/nH8Xic0cfT8DGTwiGZAHo5iPOHtjgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"711ddd0bb9c62c79fdc84472320361eef4e082af023bffdeee55d0bf74210d61","last_reissued_at":"2026-07-05T11:05:24.833685Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:24.833685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.07956","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":"2026-07-05T11:05:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YeAffD5MpYmQtDRIScF480UunN7fkXi/UqsKck1o48ie/UIUVbS6MOnphll2CDW+3LqCqFh1FvZoW7td30JSDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T19:24:33.147810Z"},"content_sha256":"13e5d9d0fa72d7cfb19f517663a2134b7ec416451ebbecfd2fcb954a7874c165","schema_version":"1.0","event_id":"sha256:13e5d9d0fa72d7cfb19f517663a2134b7ec416451ebbecfd2fcb954a7874c165"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:OEO52C5ZYYWHT7OIIRZDEA3B53","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE","cs.SC"],"primary_cat":"cs.LG","authors_text":"Fabian Ruehle, James Halverson, Kit Fraser-Taliente, Thomas R. Harvey","submitted_at":"2025-05-12T18:00:41Z","abstract_excerpt":"We present a novel approach to symbolic regression using vision-capable large language models (LLMs) and the ideas behind Google DeepMind's Funsearch. The LLM is given a plot of a univariate function and tasked with proposing an ansatz for that function. The free parameters of the ansatz are fitted using standard numerical optimisers, and a collection of such ans\\\"atze make up the population of a genetic algorithm. Unlike other symbolic regression techniques, our method does not require the specification of a set of functions to be used in regression, but with appropriate prompt engineering, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.07956","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/2505.07956/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":"2026-07-05T11:05:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lwAU8tfC2jmUqOjqZCs3OhMEltrXqG5p5u999xldmI5SnqxAtGcNMJ3WoMTP7ylvu4izTXh0l1FtDJvqpY7cBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T19:24:33.148298Z"},"content_sha256":"a16118461e117e302c966d9798fa38734e4aaf7f62e6e99754c49663b780a903","schema_version":"1.0","event_id":"sha256:a16118461e117e302c966d9798fa38734e4aaf7f62e6e99754c49663b780a903"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OEO52C5ZYYWHT7OIIRZDEA3B53/bundle.json","state_url":"https://pith.science/pith/OEO52C5ZYYWHT7OIIRZDEA3B53/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OEO52C5ZYYWHT7OIIRZDEA3B53/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":"2026-08-04T19:24:33Z","links":{"resolver":"https://pith.science/pith/OEO52C5ZYYWHT7OIIRZDEA3B53","bundle":"https://pith.science/pith/OEO52C5ZYYWHT7OIIRZDEA3B53/bundle.json","state":"https://pith.science/pith/OEO52C5ZYYWHT7OIIRZDEA3B53/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OEO52C5ZYYWHT7OIIRZDEA3B53/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:OEO52C5ZYYWHT7OIIRZDEA3B53","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":"e2097451fa4fa3572f72a320a19f50ac0c439d3d0e0eac2c1cfaf8a366e81d47","cross_cats_sorted":["cs.NE","cs.SC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-12T18:00:41Z","title_canon_sha256":"4805947eccb7d5fee2b5ac1bc1470164dd43e734b17facd7d785939f95eb898b"},"schema_version":"1.0","source":{"id":"2505.07956","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.07956","created_at":"2026-07-05T11:05:24Z"},{"alias_kind":"arxiv_version","alias_value":"2505.07956v1","created_at":"2026-07-05T11:05:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.07956","created_at":"2026-07-05T11:05:24Z"},{"alias_kind":"pith_short_12","alias_value":"OEO52C5ZYYWH","created_at":"2026-07-05T11:05:24Z"},{"alias_kind":"pith_short_16","alias_value":"OEO52C5ZYYWHT7OI","created_at":"2026-07-05T11:05:24Z"},{"alias_kind":"pith_short_8","alias_value":"OEO52C5Z","created_at":"2026-07-05T11:05:24Z"}],"graph_snapshots":[{"event_id":"sha256:a16118461e117e302c966d9798fa38734e4aaf7f62e6e99754c49663b780a903","target":"graph","created_at":"2026-07-05T11:05:24Z","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/2505.07956/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a novel approach to symbolic regression using vision-capable large language models (LLMs) and the ideas behind Google DeepMind's Funsearch. The LLM is given a plot of a univariate function and tasked with proposing an ansatz for that function. The free parameters of the ansatz are fitted using standard numerical optimisers, and a collection of such ans\\\"atze make up the population of a genetic algorithm. Unlike other symbolic regression techniques, our method does not require the specification of a set of functions to be used in regression, but with appropriate prompt engineering, w","authors_text":"Fabian Ruehle, James Halverson, Kit Fraser-Taliente, Thomas R. Harvey","cross_cats":["cs.NE","cs.SC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-12T18:00:41Z","title":"Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.07956","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":"sha256:13e5d9d0fa72d7cfb19f517663a2134b7ec416451ebbecfd2fcb954a7874c165","target":"record","created_at":"2026-07-05T11:05:24Z","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":"e2097451fa4fa3572f72a320a19f50ac0c439d3d0e0eac2c1cfaf8a366e81d47","cross_cats_sorted":["cs.NE","cs.SC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-12T18:00:41Z","title_canon_sha256":"4805947eccb7d5fee2b5ac1bc1470164dd43e734b17facd7d785939f95eb898b"},"schema_version":"1.0","source":{"id":"2505.07956","kind":"arxiv","version":1}},"canonical_sha256":"711ddd0bb9c62c79fdc84472320361eef4e082af023bffdeee55d0bf74210d61","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"711ddd0bb9c62c79fdc84472320361eef4e082af023bffdeee55d0bf74210d61","first_computed_at":"2026-07-05T11:05:24.833685Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:05:24.833685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+WTxLRSGOIo5+ANfEcN18BO70HAtQUy91PgmHac5XTB0aZEPEj+Vs9/nH8Xic0cfT8DGTwiGZAHo5iPOHtjgDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:05:24.834154Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.07956","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:13e5d9d0fa72d7cfb19f517663a2134b7ec416451ebbecfd2fcb954a7874c165","sha256:a16118461e117e302c966d9798fa38734e4aaf7f62e6e99754c49663b780a903"],"state_sha256":"e5c951f23233e75ed22879197c88f32ed2d2320ccaaca71585bf0b7ede34fef0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z5S8YNO0ahEg+pSJwYee6Ko+4y4o5/LBhW2d7sJBbB9I2/iBaS2U9bnyq6bNcvdaF0I/LaWViYjM0wRp/8eRBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T19:24:33.151585Z","bundle_sha256":"809d5731c9c878c57295822219a0440a0471323cd609ce8b89e6d190244b0ff5"}}