{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:UC67XJGIVMPCEGXG2KPWCJXJ4E","short_pith_number":"pith:UC67XJGI","canonical_record":{"source":{"id":"2507.05305","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2025-07-07T08:03:49Z","cross_cats_sorted":["cs.AI","cs.CL","cs.SE"],"title_canon_sha256":"39e0abfb10ce6965c17c46df57e2291347d782307e7ea7c4e48f4242a956a432","abstract_canon_sha256":"419e629dc68a64a316b4afa5a0411b5063b565295bd78e5980f7692fa52a4f6a"},"schema_version":"1.0"},"canonical_sha256":"a0bdfba4c8ab1e221ae6d29f6126e9e12dba5fc0cb4e126a3d80ae5fc45d4303","source":{"kind":"arxiv","id":"2507.05305","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.05305","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"arxiv_version","alias_value":"2507.05305v1","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.05305","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_12","alias_value":"UC67XJGIVMPC","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"UC67XJGIVMPCEGXG","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"UC67XJGI","created_at":"2026-07-05T11:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:UC67XJGIVMPCEGXG2KPWCJXJ4E","target":"record","payload":{"canonical_record":{"source":{"id":"2507.05305","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2025-07-07T08:03:49Z","cross_cats_sorted":["cs.AI","cs.CL","cs.SE"],"title_canon_sha256":"39e0abfb10ce6965c17c46df57e2291347d782307e7ea7c4e48f4242a956a432","abstract_canon_sha256":"419e629dc68a64a316b4afa5a0411b5063b565295bd78e5980f7692fa52a4f6a"},"schema_version":"1.0"},"canonical_sha256":"a0bdfba4c8ab1e221ae6d29f6126e9e12dba5fc0cb4e126a3d80ae5fc45d4303","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:27.978751Z","signature_b64":"CG6mIy614FbeVKT5eliUnJYUQk76vf4xBTiumN97jJxr+ryUI3VkJ3tVBmem8KPayttfZH2Qa8BbC3szxgr4CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0bdfba4c8ab1e221ae6d29f6126e9e12dba5fc0cb4e126a3d80ae5fc45d4303","last_reissued_at":"2026-07-05T11:33:27.978345Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:27.978345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.05305","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":"2026-07-05T11:33:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0Z1vk69E39tWHRdRPTYIlFdwiIMgs6ngLqRqGmLyqu2QJAQKeTs8PF+FD0SDk0belQHU80t1hotUx3XiIPXbBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T13:55:57.485620Z"},"content_sha256":"255783c0f2b61cb02eb779f921111d0b49521389a00c23e6c190b364e3741da5","schema_version":"1.0","event_id":"sha256:255783c0f2b61cb02eb779f921111d0b49521389a00c23e6c190b364e3741da5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:UC67XJGIVMPCEGXG2KPWCJXJ4E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.SE"],"primary_cat":"cs.CY","authors_text":"Alexandra Vassar, Charles Koutcheme, Jake Renzella, Juho Leinonen, Lorenzo Lee Solano","submitted_at":"2025-07-07T08:03:49Z","abstract_excerpt":"Frontier Large language models (LLMs) like ChatGPT and Gemini can decipher cryptic compiler errors for novice programmers, but their computational scale, cost, and tendency to over-assist make them problematic for widespread pedagogical adoption. This work demonstrates that smaller, specialised language models, enhanced via Supervised Fine-Tuning (SFT), present a more viable alternative for educational tools. We utilise a new dataset of 40,000 C compiler error explanations, derived from real introductory programming (CS1/2) student-generated programming errors, which we used to fine-tune three"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.05305","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/2507.05305/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":"2026-07-05T11:33:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GACexitQ6rBi+L62RWtJeJCJEOR18n4rufrIsLCGDi1ERTe4oLYw7pfPq2s7H8Th4+Q5ChAZxePZ+GxtiVXSDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T13:55:57.486106Z"},"content_sha256":"e32479443c223230393677b69d5eca7309bc1b0b01a48fd410fbb10a8f0b8b9c","schema_version":"1.0","event_id":"sha256:e32479443c223230393677b69d5eca7309bc1b0b01a48fd410fbb10a8f0b8b9c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UC67XJGIVMPCEGXG2KPWCJXJ4E/bundle.json","state_url":"https://pith.science/pith/UC67XJGIVMPCEGXG2KPWCJXJ4E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UC67XJGIVMPCEGXG2KPWCJXJ4E/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":"2026-08-09T13:55:57Z","links":{"resolver":"https://pith.science/pith/UC67XJGIVMPCEGXG2KPWCJXJ4E","bundle":"https://pith.science/pith/UC67XJGIVMPCEGXG2KPWCJXJ4E/bundle.json","state":"https://pith.science/pith/UC67XJGIVMPCEGXG2KPWCJXJ4E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UC67XJGIVMPCEGXG2KPWCJXJ4E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:UC67XJGIVMPCEGXG2KPWCJXJ4E","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":"419e629dc68a64a316b4afa5a0411b5063b565295bd78e5980f7692fa52a4f6a","cross_cats_sorted":["cs.AI","cs.CL","cs.SE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2025-07-07T08:03:49Z","title_canon_sha256":"39e0abfb10ce6965c17c46df57e2291347d782307e7ea7c4e48f4242a956a432"},"schema_version":"1.0","source":{"id":"2507.05305","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.05305","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"arxiv_version","alias_value":"2507.05305v1","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.05305","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_12","alias_value":"UC67XJGIVMPC","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"UC67XJGIVMPCEGXG","created_at":"2026-07-05T11:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"UC67XJGI","created_at":"2026-07-05T11:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:e32479443c223230393677b69d5eca7309bc1b0b01a48fd410fbb10a8f0b8b9c","target":"graph","created_at":"2026-07-05T11:33:27Z","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/2507.05305/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Frontier Large language models (LLMs) like ChatGPT and Gemini can decipher cryptic compiler errors for novice programmers, but their computational scale, cost, and tendency to over-assist make them problematic for widespread pedagogical adoption. This work demonstrates that smaller, specialised language models, enhanced via Supervised Fine-Tuning (SFT), present a more viable alternative for educational tools. We utilise a new dataset of 40,000 C compiler error explanations, derived from real introductory programming (CS1/2) student-generated programming errors, which we used to fine-tune three","authors_text":"Alexandra Vassar, Charles Koutcheme, Jake Renzella, Juho Leinonen, Lorenzo Lee Solano","cross_cats":["cs.AI","cs.CL","cs.SE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2025-07-07T08:03:49Z","title":"Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.05305","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":"sha256:255783c0f2b61cb02eb779f921111d0b49521389a00c23e6c190b364e3741da5","target":"record","created_at":"2026-07-05T11:33:27Z","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":"419e629dc68a64a316b4afa5a0411b5063b565295bd78e5980f7692fa52a4f6a","cross_cats_sorted":["cs.AI","cs.CL","cs.SE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2025-07-07T08:03:49Z","title_canon_sha256":"39e0abfb10ce6965c17c46df57e2291347d782307e7ea7c4e48f4242a956a432"},"schema_version":"1.0","source":{"id":"2507.05305","kind":"arxiv","version":1}},"canonical_sha256":"a0bdfba4c8ab1e221ae6d29f6126e9e12dba5fc0cb4e126a3d80ae5fc45d4303","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a0bdfba4c8ab1e221ae6d29f6126e9e12dba5fc0cb4e126a3d80ae5fc45d4303","first_computed_at":"2026-07-05T11:33:27.978345Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:33:27.978345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CG6mIy614FbeVKT5eliUnJYUQk76vf4xBTiumN97jJxr+ryUI3VkJ3tVBmem8KPayttfZH2Qa8BbC3szxgr4CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:33:27.978751Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.05305","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:255783c0f2b61cb02eb779f921111d0b49521389a00c23e6c190b364e3741da5","sha256:e32479443c223230393677b69d5eca7309bc1b0b01a48fd410fbb10a8f0b8b9c"],"state_sha256":"190a00d47f9ceb5e56dab7cac273fcbd963df448fb891f9faab94876ceb11fd0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TA6D0BIIbT7ogDqL2MmYraSQ2u8YJ9MYqO5ZMGQuJTJB/f+50XAuMi/HmyOg0dHlYhcVJzGhl77f1RkmEbHdBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T13:55:57.489733Z","bundle_sha256":"f6a3a1f9308c57ff7eb16fa9e17730c4e2c5d39eeb0788b8054dd541a9dcc8be"}}