{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:HEYM2DWNYL6SN5BZF7N7FWQNO4","short_pith_number":"pith:HEYM2DWN","canonical_record":{"source":{"id":"2507.10614","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-13T15:21:23Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"53ab6c755bfe9a01327753ec2d49d27dd5ae7ef769647a9f1be78e498b7e2089","abstract_canon_sha256":"7030b00ffc6fcf8a7678f992ccfdabc266314fb7c454087780e6483bcb9b1b49"},"schema_version":"1.0"},"canonical_sha256":"3930cd0ecdc2fd26f4392fdbf2da0d771067c631d6518cfbba9e6ae72bfb737d","source":{"kind":"arxiv","id":"2507.10614","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.10614","created_at":"2026-05-20T01:04:56Z"},{"alias_kind":"arxiv_version","alias_value":"2507.10614v2","created_at":"2026-05-20T01:04:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.10614","created_at":"2026-05-20T01:04:56Z"},{"alias_kind":"pith_short_12","alias_value":"HEYM2DWNYL6S","created_at":"2026-05-20T01:04:56Z"},{"alias_kind":"pith_short_16","alias_value":"HEYM2DWNYL6SN5BZ","created_at":"2026-05-20T01:04:56Z"},{"alias_kind":"pith_short_8","alias_value":"HEYM2DWN","created_at":"2026-05-20T01:04:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:HEYM2DWNYL6SN5BZF7N7FWQNO4","target":"record","payload":{"canonical_record":{"source":{"id":"2507.10614","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-13T15:21:23Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"53ab6c755bfe9a01327753ec2d49d27dd5ae7ef769647a9f1be78e498b7e2089","abstract_canon_sha256":"7030b00ffc6fcf8a7678f992ccfdabc266314fb7c454087780e6483bcb9b1b49"},"schema_version":"1.0"},"canonical_sha256":"3930cd0ecdc2fd26f4392fdbf2da0d771067c631d6518cfbba9e6ae72bfb737d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:04:56.044459Z","signature_b64":"UoXPWr9qweXp0mCKMSuaT8kFpjorUzESQGOMAFWTnUyKtQxHvSIQONdkTOghMPPtKj3DXneDNr2Lk8q+zsCdBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3930cd0ecdc2fd26f4392fdbf2da0d771067c631d6518cfbba9e6ae72bfb737d","last_reissued_at":"2026-05-20T01:04:56.043458Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:04:56.043458Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.10614","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T01:04:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J6YUIei5LsxzlqL31AH0Mwwxm6jpZdZ4HbEotO40R9h2xq293KrLqZ3u+R+5gqJCr9ptpXy7yf0U5jdOj+jXBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:05:53.235467Z"},"content_sha256":"8be0e8ad55852379ff37c8bf47899b1eb53e2aa692825354eb31af4fcfa600ce","schema_version":"1.0","event_id":"sha256:8be0e8ad55852379ff37c8bf47899b1eb53e2aa692825354eb31af4fcfa600ce"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:HEYM2DWNYL6SN5BZF7N7FWQNO4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fine-tuning Large Language Model for Automated Algorithm Design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Fei Liu, Qingfu Zhang, Rui Zhang, Xi Lin, Zhichao Lu","submitted_at":"2025-07-13T15:21:23Z","abstract_excerpt":"The integration of large language models (LLMs) into automated algorithm design has shown promising potential. A prevalent approach embeds LLMs within search routines to iteratively generate and refine candidate algorithms. However, most existing methods rely on off-the-shelf LLMs trained for general coding tasks, leaving a key question open: Do we need LLMs specifically tailored for algorithm design? If so, how can such LLMs be effectively obtained and how well can they generalize across different algorithm design tasks? In this paper, we take a preliminary step toward answering these questio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.10614","kind":"arxiv","version":2},"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/2507.10614/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":"2026-05-20T01:04:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"POj9/VNRec6FqJTegfmPJUvs9H1cCH4RGi/yc7yaiS8bAIPKc8kCcb8nJkSVBqBU4a3VRxwE2oafhoirhYAfAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:05:53.236012Z"},"content_sha256":"aed9551dfdda9a305ba29ba129a00f55b22789e713763df195782ee5aa866a87","schema_version":"1.0","event_id":"sha256:aed9551dfdda9a305ba29ba129a00f55b22789e713763df195782ee5aa866a87"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HEYM2DWNYL6SN5BZF7N7FWQNO4/bundle.json","state_url":"https://pith.science/pith/HEYM2DWNYL6SN5BZF7N7FWQNO4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HEYM2DWNYL6SN5BZF7N7FWQNO4/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":"2026-05-29T18:05:53Z","links":{"resolver":"https://pith.science/pith/HEYM2DWNYL6SN5BZF7N7FWQNO4","bundle":"https://pith.science/pith/HEYM2DWNYL6SN5BZF7N7FWQNO4/bundle.json","state":"https://pith.science/pith/HEYM2DWNYL6SN5BZF7N7FWQNO4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HEYM2DWNYL6SN5BZF7N7FWQNO4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HEYM2DWNYL6SN5BZF7N7FWQNO4","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":"7030b00ffc6fcf8a7678f992ccfdabc266314fb7c454087780e6483bcb9b1b49","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-13T15:21:23Z","title_canon_sha256":"53ab6c755bfe9a01327753ec2d49d27dd5ae7ef769647a9f1be78e498b7e2089"},"schema_version":"1.0","source":{"id":"2507.10614","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.10614","created_at":"2026-05-20T01:04:56Z"},{"alias_kind":"arxiv_version","alias_value":"2507.10614v2","created_at":"2026-05-20T01:04:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.10614","created_at":"2026-05-20T01:04:56Z"},{"alias_kind":"pith_short_12","alias_value":"HEYM2DWNYL6S","created_at":"2026-05-20T01:04:56Z"},{"alias_kind":"pith_short_16","alias_value":"HEYM2DWNYL6SN5BZ","created_at":"2026-05-20T01:04:56Z"},{"alias_kind":"pith_short_8","alias_value":"HEYM2DWN","created_at":"2026-05-20T01:04:56Z"}],"graph_snapshots":[{"event_id":"sha256:aed9551dfdda9a305ba29ba129a00f55b22789e713763df195782ee5aa866a87","target":"graph","created_at":"2026-05-20T01:04:56Z","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/2507.10614/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The integration of large language models (LLMs) into automated algorithm design has shown promising potential. A prevalent approach embeds LLMs within search routines to iteratively generate and refine candidate algorithms. However, most existing methods rely on off-the-shelf LLMs trained for general coding tasks, leaving a key question open: Do we need LLMs specifically tailored for algorithm design? If so, how can such LLMs be effectively obtained and how well can they generalize across different algorithm design tasks? In this paper, we take a preliminary step toward answering these questio","authors_text":"Fei Liu, Qingfu Zhang, Rui Zhang, Xi Lin, Zhichao Lu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-13T15:21:23Z","title":"Fine-tuning Large Language Model for Automated Algorithm Design"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.10614","kind":"arxiv","version":2},"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":"sha256:8be0e8ad55852379ff37c8bf47899b1eb53e2aa692825354eb31af4fcfa600ce","target":"record","created_at":"2026-05-20T01:04:56Z","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":"7030b00ffc6fcf8a7678f992ccfdabc266314fb7c454087780e6483bcb9b1b49","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-13T15:21:23Z","title_canon_sha256":"53ab6c755bfe9a01327753ec2d49d27dd5ae7ef769647a9f1be78e498b7e2089"},"schema_version":"1.0","source":{"id":"2507.10614","kind":"arxiv","version":2}},"canonical_sha256":"3930cd0ecdc2fd26f4392fdbf2da0d771067c631d6518cfbba9e6ae72bfb737d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3930cd0ecdc2fd26f4392fdbf2da0d771067c631d6518cfbba9e6ae72bfb737d","first_computed_at":"2026-05-20T01:04:56.043458Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T01:04:56.043458Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UoXPWr9qweXp0mCKMSuaT8kFpjorUzESQGOMAFWTnUyKtQxHvSIQONdkTOghMPPtKj3DXneDNr2Lk8q+zsCdBQ==","signature_status":"signed_v1","signed_at":"2026-05-20T01:04:56.044459Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.10614","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8be0e8ad55852379ff37c8bf47899b1eb53e2aa692825354eb31af4fcfa600ce","sha256:aed9551dfdda9a305ba29ba129a00f55b22789e713763df195782ee5aa866a87"],"state_sha256":"40f1b0873abcf97a6a2fb0a679d7ba5ad8fb9f01d2afb86f974e17c2a1ab4839"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/SNVIOjPH/3CZNinbGc6Eua1rPEs6T6t/WC03psGt+92YzdrFqfE6uLWahsSSiPdZW5cTVt3HlWeQ3zKsy7SDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T18:05:53.240733Z","bundle_sha256":"db5d727139fa693171db1da05af98c80b90c0e7daac2d8e126f11c313c85bad2"}}