{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2BH6UINYAGEH42BOS5LYJSAI3L","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":"44258610f27c5fd336f8c3fb20b54fcb29a7ee582e45965312ab8ab27cf770b5","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-27T21:23:56Z","title_canon_sha256":"6d19bf3095bc0d4736298913604d5ebc030b29a4804497075c7b817f2bc59068"},"schema_version":"1.0","source":{"id":"2502.20527","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.20527","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"arxiv_version","alias_value":"2502.20527v1","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.20527","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"pith_short_12","alias_value":"2BH6UINYAGEH","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"pith_short_16","alias_value":"2BH6UINYAGEH42BO","created_at":"2026-07-05T10:21:34Z"},{"alias_kind":"pith_short_8","alias_value":"2BH6UINY","created_at":"2026-07-05T10:21:34Z"}],"graph_snapshots":[{"event_id":"sha256:e54e8b8d46fa5d65e7d7a0eeb79f8a89f248ac3041fe41649b6881429729ff79","target":"graph","created_at":"2026-07-05T10:21:34Z","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/2502.20527/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) are increasingly being explored in higher education, yet their effectiveness as teaching agents remains underexamined. In this paper, we present the development of GuideLM, a fine-tuned LLM designed for programming education. GuideLM has been integrated into the Debugging C Compiler (DCC), an educational C compiler that leverages LLMs to generate pedagogically sound error explanations. Previously, DCC relied on off-the-shelf OpenAI models, which, while accurate, often over-assisted students by directly providing solutions despite contrary prompting.\n  To address th","authors_text":"Alexandra Vassar, Andrew Taylor, Emily Ross, Jake Renzella, Yuval Kansal","cross_cats":["cs.CY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-27T21:23:56Z","title":"Supervised Fine-Tuning LLMs to Behave as Pedagogical Agents in Programming Education"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.20527","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":"sha256:55b8b0bdd2d7b9031f0cbc61cb4252ba0b6517cf6d64abece4a85d5e95d2d86a","target":"record","created_at":"2026-07-05T10:21:34Z","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":"44258610f27c5fd336f8c3fb20b54fcb29a7ee582e45965312ab8ab27cf770b5","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-27T21:23:56Z","title_canon_sha256":"6d19bf3095bc0d4736298913604d5ebc030b29a4804497075c7b817f2bc59068"},"schema_version":"1.0","source":{"id":"2502.20527","kind":"arxiv","version":1}},"canonical_sha256":"d04fea21b801887e682e975784c808daf1b7e8b98466fd68332cafa7fd0dc3be","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d04fea21b801887e682e975784c808daf1b7e8b98466fd68332cafa7fd0dc3be","first_computed_at":"2026-07-05T10:21:34.690712Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:21:34.690712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OGgOpWbIIRJ4q+c0ifjwUbBYxuiHJbpzHOWcOtgDYBoTjRvgjOUCsyoI0hDMLWqVNF8JCO+qpwk1LKfvDc2jDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:21:34.691300Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.20527","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:55b8b0bdd2d7b9031f0cbc61cb4252ba0b6517cf6d64abece4a85d5e95d2d86a","sha256:e54e8b8d46fa5d65e7d7a0eeb79f8a89f248ac3041fe41649b6881429729ff79"],"state_sha256":"932ab6c536fa8bfbb8f232f68c63c3cb7e6364c70c9d6579ce2a5ba66f9297ba"}