{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JRUORNQGQRL4AKG22W2T7GVCCQ","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":"31c926ccc33f0f4c66a2341feb353abbd95df44acbf6951398a7c917c1d18410","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-24T02:08:38Z","title_canon_sha256":"114d43402bed06ec52f144c2de936b979773a983b515370748df8e6298d945ea"},"schema_version":"1.0","source":{"id":"2412.18093","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.18093","created_at":"2026-07-05T09:53:49Z"},{"alias_kind":"arxiv_version","alias_value":"2412.18093v1","created_at":"2026-07-05T09:53:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.18093","created_at":"2026-07-05T09:53:49Z"},{"alias_kind":"pith_short_12","alias_value":"JRUORNQGQRL4","created_at":"2026-07-05T09:53:49Z"},{"alias_kind":"pith_short_16","alias_value":"JRUORNQGQRL4AKG2","created_at":"2026-07-05T09:53:49Z"},{"alias_kind":"pith_short_8","alias_value":"JRUORNQG","created_at":"2026-07-05T09:53:49Z"}],"graph_snapshots":[{"event_id":"sha256:65788dee9ac6acb7590b489d1bb0ad6b56db5286ee481657b40d2944c3809952","target":"graph","created_at":"2026-07-05T09:53:49Z","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/2412.18093/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Applying large language models (LLMs) as teaching assists has attracted much attention as an integral part of intelligent education, particularly in computing courses. To reduce the gap between the LLMs and the computer programming education expert, fine-tuning and retrieval augmented generation (RAG) are the two mainstream methods in existing researches. However, fine-tuning for specific tasks is resource-intensive and may diminish the model`s generalization capabilities. RAG can perform well on reducing the illusion of LLMs, but the generation of irrelevant factual content during reasoning c","authors_text":"Han Wu, Jiong Wang, Lu Han, Na Zong, Rui Xiao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-24T02:08:38Z","title":"Molly: Making Large Language Model Agents Solve Python Problem More Logically"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.18093","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":"sha256:7b0899b31f675b41bb51c91d98fad5db1f7b0264be4202e6ce4ab7ee8e74ddd7","target":"record","created_at":"2026-07-05T09:53:49Z","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":"31c926ccc33f0f4c66a2341feb353abbd95df44acbf6951398a7c917c1d18410","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-24T02:08:38Z","title_canon_sha256":"114d43402bed06ec52f144c2de936b979773a983b515370748df8e6298d945ea"},"schema_version":"1.0","source":{"id":"2412.18093","kind":"arxiv","version":1}},"canonical_sha256":"4c68e8b6068457c028dad5b53f9aa21436f17fa213361cd2082686cba798b4c6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4c68e8b6068457c028dad5b53f9aa21436f17fa213361cd2082686cba798b4c6","first_computed_at":"2026-07-05T09:53:49.644325Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:53:49.644325Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ozvdy4ARIg2zS3/XY+zkIWR0+JuStrc5FdONeKLdrsScb5sz3CrZv8EPJTF+ktwn7FtXDGovYWn625nBRYhJCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:53:49.644869Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.18093","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7b0899b31f675b41bb51c91d98fad5db1f7b0264be4202e6ce4ab7ee8e74ddd7","sha256:65788dee9ac6acb7590b489d1bb0ad6b56db5286ee481657b40d2944c3809952"],"state_sha256":"eaef5e396db5a38260315199b09c2441763677f0a22849339a94f5bf94abf196"}