{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:R6IEIHSTC7CW3CXDWYWQQSUS3C","short_pith_number":"pith:R6IEIHST","canonical_record":{"source":{"id":"2606.30610","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-06-29T17:45:36Z","cross_cats_sorted":[],"title_canon_sha256":"209733249c2f997c1949d52528b964b37505b2cead85d4a1797808c17c4f6843","abstract_canon_sha256":"2c37a136e9967ef924138aa15a0336bc8f5c9925d8e55769c3c9e38d5dac6263"},"schema_version":"1.0"},"canonical_sha256":"8f90441e5317c56d8ae3b62d084a92d88fbe4063d37b0704dd295ef2955e330c","source":{"kind":"arxiv","id":"2606.30610","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.30610","created_at":"2026-06-30T02:18:22Z"},{"alias_kind":"arxiv_version","alias_value":"2606.30610v1","created_at":"2026-06-30T02:18:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30610","created_at":"2026-06-30T02:18:22Z"},{"alias_kind":"pith_short_12","alias_value":"R6IEIHSTC7CW","created_at":"2026-06-30T02:18:22Z"},{"alias_kind":"pith_short_16","alias_value":"R6IEIHSTC7CW3CXD","created_at":"2026-06-30T02:18:22Z"},{"alias_kind":"pith_short_8","alias_value":"R6IEIHST","created_at":"2026-06-30T02:18:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:R6IEIHSTC7CW3CXDWYWQQSUS3C","target":"record","payload":{"canonical_record":{"source":{"id":"2606.30610","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-06-29T17:45:36Z","cross_cats_sorted":[],"title_canon_sha256":"209733249c2f997c1949d52528b964b37505b2cead85d4a1797808c17c4f6843","abstract_canon_sha256":"2c37a136e9967ef924138aa15a0336bc8f5c9925d8e55769c3c9e38d5dac6263"},"schema_version":"1.0"},"canonical_sha256":"8f90441e5317c56d8ae3b62d084a92d88fbe4063d37b0704dd295ef2955e330c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:18:22.568225Z","signature_b64":"BLyH828ePgoY+MJXohJRQD6dNlIemVpnNYo5wsHHubafwIDnu/S/0O2kVD1dlhDlC0DO7aOk1mFcoqwgeaEzDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f90441e5317c56d8ae3b62d084a92d88fbe4063d37b0704dd295ef2955e330c","last_reissued_at":"2026-06-30T02:18:22.567769Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:18:22.567769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.30610","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":"2026-06-30T02:18:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LrNHEg603qN57Nr5qZQBVy8BLzGQPC1Ebtf5Zbv8qTVn1E5eT/8NcnExarhQz94IMVYyEdFYXLJtBVK1ENdhBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T16:20:02.935190Z"},"content_sha256":"020f9b35ed93ba4fb96ffc17e8df64b30ff142db3fe5a40d83f0354b47fa7abd","schema_version":"1.0","event_id":"sha256:020f9b35ed93ba4fb96ffc17e8df64b30ff142db3fe5a40d83f0354b47fa7abd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:R6IEIHSTC7CW3CXDWYWQQSUS3C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PyMETA: A Benchmark Dataset for Hierarchical Student Code Error Classification with Python-Interpreter-Based Labels","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Chuyue Li, Jingyi Wang, Kazuma Hashimoto, Lingyu Gao, Yu Wu, Ziqi Tang","submitted_at":"2026-06-29T17:45:36Z","abstract_excerpt":"With the advancement of Large Language Models (LLMs), code error detection has extended beyond traditional IDE diagnostics to context-sensitive debugging in educational scenarios. However, existing approaches lack large-scale datasets, multi-error analysis, and unified error taxonomies. To address this, we introduce PyMETA, a large-scale Python code error classification dataset of 48,646 student submissions, with single-error labels for all samples and a diagnostic subset of 97 expert-annotated multi-error samples. The dataset uses a three-level hierarchical taxonomy, from a binary error/no-er"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30610","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/2606.30610/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":"2026-06-30T02:18:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e0bBWlRLZdC6BLyvcoE+w8gnG0GHmr/xuWqkwZYUdgwwN6vIR7eKeCV1yHPBY9ptHVZHqs1/2HqLMYbOiHmHAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T16:20:02.935570Z"},"content_sha256":"707743d3d932654316f42487d73de1e709361a9a1d0c92c51cc169a85fe1c6bd","schema_version":"1.0","event_id":"sha256:707743d3d932654316f42487d73de1e709361a9a1d0c92c51cc169a85fe1c6bd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R6IEIHSTC7CW3CXDWYWQQSUS3C/bundle.json","state_url":"https://pith.science/pith/R6IEIHSTC7CW3CXDWYWQQSUS3C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R6IEIHSTC7CW3CXDWYWQQSUS3C/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":"2026-06-30T16:20:02Z","links":{"resolver":"https://pith.science/pith/R6IEIHSTC7CW3CXDWYWQQSUS3C","bundle":"https://pith.science/pith/R6IEIHSTC7CW3CXDWYWQQSUS3C/bundle.json","state":"https://pith.science/pith/R6IEIHSTC7CW3CXDWYWQQSUS3C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R6IEIHSTC7CW3CXDWYWQQSUS3C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:R6IEIHSTC7CW3CXDWYWQQSUS3C","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":"2c37a136e9967ef924138aa15a0336bc8f5c9925d8e55769c3c9e38d5dac6263","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-06-29T17:45:36Z","title_canon_sha256":"209733249c2f997c1949d52528b964b37505b2cead85d4a1797808c17c4f6843"},"schema_version":"1.0","source":{"id":"2606.30610","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.30610","created_at":"2026-06-30T02:18:22Z"},{"alias_kind":"arxiv_version","alias_value":"2606.30610v1","created_at":"2026-06-30T02:18:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30610","created_at":"2026-06-30T02:18:22Z"},{"alias_kind":"pith_short_12","alias_value":"R6IEIHSTC7CW","created_at":"2026-06-30T02:18:22Z"},{"alias_kind":"pith_short_16","alias_value":"R6IEIHSTC7CW3CXD","created_at":"2026-06-30T02:18:22Z"},{"alias_kind":"pith_short_8","alias_value":"R6IEIHST","created_at":"2026-06-30T02:18:22Z"}],"graph_snapshots":[{"event_id":"sha256:707743d3d932654316f42487d73de1e709361a9a1d0c92c51cc169a85fe1c6bd","target":"graph","created_at":"2026-06-30T02:18:22Z","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/2606.30610/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the advancement of Large Language Models (LLMs), code error detection has extended beyond traditional IDE diagnostics to context-sensitive debugging in educational scenarios. However, existing approaches lack large-scale datasets, multi-error analysis, and unified error taxonomies. To address this, we introduce PyMETA, a large-scale Python code error classification dataset of 48,646 student submissions, with single-error labels for all samples and a diagnostic subset of 97 expert-annotated multi-error samples. The dataset uses a three-level hierarchical taxonomy, from a binary error/no-er","authors_text":"Chuyue Li, Jingyi Wang, Kazuma Hashimoto, Lingyu Gao, Yu Wu, Ziqi Tang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-06-29T17:45:36Z","title":"PyMETA: A Benchmark Dataset for Hierarchical Student Code Error Classification with Python-Interpreter-Based Labels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30610","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":"sha256:020f9b35ed93ba4fb96ffc17e8df64b30ff142db3fe5a40d83f0354b47fa7abd","target":"record","created_at":"2026-06-30T02:18:22Z","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":"2c37a136e9967ef924138aa15a0336bc8f5c9925d8e55769c3c9e38d5dac6263","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-06-29T17:45:36Z","title_canon_sha256":"209733249c2f997c1949d52528b964b37505b2cead85d4a1797808c17c4f6843"},"schema_version":"1.0","source":{"id":"2606.30610","kind":"arxiv","version":1}},"canonical_sha256":"8f90441e5317c56d8ae3b62d084a92d88fbe4063d37b0704dd295ef2955e330c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8f90441e5317c56d8ae3b62d084a92d88fbe4063d37b0704dd295ef2955e330c","first_computed_at":"2026-06-30T02:18:22.567769Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T02:18:22.567769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BLyH828ePgoY+MJXohJRQD6dNlIemVpnNYo5wsHHubafwIDnu/S/0O2kVD1dlhDlC0DO7aOk1mFcoqwgeaEzDw==","signature_status":"signed_v1","signed_at":"2026-06-30T02:18:22.568225Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.30610","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:020f9b35ed93ba4fb96ffc17e8df64b30ff142db3fe5a40d83f0354b47fa7abd","sha256:707743d3d932654316f42487d73de1e709361a9a1d0c92c51cc169a85fe1c6bd"],"state_sha256":"72820169ea3994467b88b6fa5901784ffbc950cb8ae033f1889d12c3ef3dd491"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uwx1okFzJmyrOYt9Tc6DzpEmak52z+m/PBzgjbTZ1HxPDLU3s5Zjv6PdMMW7kaz5DWXgG+wjUng2jKbi3JdnBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T16:20:02.937504Z","bundle_sha256":"c38f42374257e2d64a661a642e13e7d8e8826013a5f498bbbddf4a3b98eacad1"}}