{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:QRG6SG3NV76ZERY3GFZMZA2OXB","short_pith_number":"pith:QRG6SG3N","canonical_record":{"source":{"id":"2502.08904","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-13T02:40:33Z","cross_cats_sorted":[],"title_canon_sha256":"d61bc53b73ec0d842b51b402be72b6e17b551aaaa1c0865100fd656cb75a826f","abstract_canon_sha256":"6ddb7d15f4135dc6c0cbfed3f6f9973fd694fd530e35b344ec7695275091728c"},"schema_version":"1.0"},"canonical_sha256":"844de91b6daffd92471b3172cc834eb87f4420f4bd1043b6787f70cf2ce0c413","source":{"kind":"arxiv","id":"2502.08904","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.08904","created_at":"2026-07-05T10:20:43Z"},{"alias_kind":"arxiv_version","alias_value":"2502.08904v3","created_at":"2026-07-05T10:20:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.08904","created_at":"2026-07-05T10:20:43Z"},{"alias_kind":"pith_short_12","alias_value":"QRG6SG3NV76Z","created_at":"2026-07-05T10:20:43Z"},{"alias_kind":"pith_short_16","alias_value":"QRG6SG3NV76ZERY3","created_at":"2026-07-05T10:20:43Z"},{"alias_kind":"pith_short_8","alias_value":"QRG6SG3N","created_at":"2026-07-05T10:20:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:QRG6SG3NV76ZERY3GFZMZA2OXB","target":"record","payload":{"canonical_record":{"source":{"id":"2502.08904","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-13T02:40:33Z","cross_cats_sorted":[],"title_canon_sha256":"d61bc53b73ec0d842b51b402be72b6e17b551aaaa1c0865100fd656cb75a826f","abstract_canon_sha256":"6ddb7d15f4135dc6c0cbfed3f6f9973fd694fd530e35b344ec7695275091728c"},"schema_version":"1.0"},"canonical_sha256":"844de91b6daffd92471b3172cc834eb87f4420f4bd1043b6787f70cf2ce0c413","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:20:43.711072Z","signature_b64":"7h9Y1GhGBm6YU6NrUvuv0VPbwYOl5O+Khu5P8qRCYvn3Q7gntm8HabeLJQy0acO05w5kXg2NJI2bMZHM2NaJBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"844de91b6daffd92471b3172cc834eb87f4420f4bd1043b6787f70cf2ce0c413","last_reissued_at":"2026-07-05T10:20:43.710400Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:20:43.710400Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.08904","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:20:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aKifdxd4mpKKS5KwOaePaEogUenz8FqGv0N3ueN2cnIb5yIXUJ6dA7klEAjpPAubYC+ABltHNdueLgYsjfYQCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:56:57.407163Z"},"content_sha256":"b7d1f3d76a18e987e9982e074065a694111ff38cb5bd24f4d377b4f6d656ae5c","schema_version":"1.0","event_id":"sha256:b7d1f3d76a18e987e9982e074065a694111ff38cb5bd24f4d377b4f6d656ae5c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:QRG6SG3NV76ZERY3GFZMZA2OXB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MIH-TCCT: Mitigating Inconsistent Hallucinations in LLMs via Event-Driven Text-Code Cyclic Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Guoping Hu, Huan Zhang, Ji Wu, Kaiyin Zhou, Qixin Sun, Shaohui Liu, Shijin Wang, Si Liu, Xien Liu, Xinxin You","submitted_at":"2025-02-13T02:40:33Z","abstract_excerpt":"Recent methodologies utilizing synthetic datasets have aimed to address inconsistent hallucinations in large language models (LLMs); however,these approaches are primarily tailored to specific tasks, limiting their generalizability. Inspired by the strong performance of code-trained models in logic-intensive domains, we propose a novel framework that leverages event-based text to generate corresponding code and employs cyclic training to transfer the logical consistency of code to natural language effectively. Our method significantly reduces inconsistent hallucinations across three leading LL"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.08904","kind":"arxiv","version":3},"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/2502.08904/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":"2026-07-05T10:20:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aZYeN08O/Pui0uz/Yaa979KemflwlnwC9awTS8f6n9MTvbOKW1IQPUnYkAEG3skTk8PLPuE7sNzutMod/KOvDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:56:57.407697Z"},"content_sha256":"ada2ecebf79fa01c66a77620d05e0e2284c5a66217ced8e37b263cfa3e1ff461","schema_version":"1.0","event_id":"sha256:ada2ecebf79fa01c66a77620d05e0e2284c5a66217ced8e37b263cfa3e1ff461"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QRG6SG3NV76ZERY3GFZMZA2OXB/bundle.json","state_url":"https://pith.science/pith/QRG6SG3NV76ZERY3GFZMZA2OXB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QRG6SG3NV76ZERY3GFZMZA2OXB/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":"2026-08-08T20:56:57Z","links":{"resolver":"https://pith.science/pith/QRG6SG3NV76ZERY3GFZMZA2OXB","bundle":"https://pith.science/pith/QRG6SG3NV76ZERY3GFZMZA2OXB/bundle.json","state":"https://pith.science/pith/QRG6SG3NV76ZERY3GFZMZA2OXB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QRG6SG3NV76ZERY3GFZMZA2OXB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:QRG6SG3NV76ZERY3GFZMZA2OXB","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":"6ddb7d15f4135dc6c0cbfed3f6f9973fd694fd530e35b344ec7695275091728c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-13T02:40:33Z","title_canon_sha256":"d61bc53b73ec0d842b51b402be72b6e17b551aaaa1c0865100fd656cb75a826f"},"schema_version":"1.0","source":{"id":"2502.08904","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.08904","created_at":"2026-07-05T10:20:43Z"},{"alias_kind":"arxiv_version","alias_value":"2502.08904v3","created_at":"2026-07-05T10:20:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.08904","created_at":"2026-07-05T10:20:43Z"},{"alias_kind":"pith_short_12","alias_value":"QRG6SG3NV76Z","created_at":"2026-07-05T10:20:43Z"},{"alias_kind":"pith_short_16","alias_value":"QRG6SG3NV76ZERY3","created_at":"2026-07-05T10:20:43Z"},{"alias_kind":"pith_short_8","alias_value":"QRG6SG3N","created_at":"2026-07-05T10:20:43Z"}],"graph_snapshots":[{"event_id":"sha256:ada2ecebf79fa01c66a77620d05e0e2284c5a66217ced8e37b263cfa3e1ff461","target":"graph","created_at":"2026-07-05T10:20:43Z","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/2502.08904/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent methodologies utilizing synthetic datasets have aimed to address inconsistent hallucinations in large language models (LLMs); however,these approaches are primarily tailored to specific tasks, limiting their generalizability. Inspired by the strong performance of code-trained models in logic-intensive domains, we propose a novel framework that leverages event-based text to generate corresponding code and employs cyclic training to transfer the logical consistency of code to natural language effectively. Our method significantly reduces inconsistent hallucinations across three leading LL","authors_text":"Guoping Hu, Huan Zhang, Ji Wu, Kaiyin Zhou, Qixin Sun, Shaohui Liu, Shijin Wang, Si Liu, Xien Liu, Xinxin You","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-13T02:40:33Z","title":"MIH-TCCT: Mitigating Inconsistent Hallucinations in LLMs via Event-Driven Text-Code Cyclic Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.08904","kind":"arxiv","version":3},"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:b7d1f3d76a18e987e9982e074065a694111ff38cb5bd24f4d377b4f6d656ae5c","target":"record","created_at":"2026-07-05T10:20:43Z","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":"6ddb7d15f4135dc6c0cbfed3f6f9973fd694fd530e35b344ec7695275091728c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-13T02:40:33Z","title_canon_sha256":"d61bc53b73ec0d842b51b402be72b6e17b551aaaa1c0865100fd656cb75a826f"},"schema_version":"1.0","source":{"id":"2502.08904","kind":"arxiv","version":3}},"canonical_sha256":"844de91b6daffd92471b3172cc834eb87f4420f4bd1043b6787f70cf2ce0c413","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"844de91b6daffd92471b3172cc834eb87f4420f4bd1043b6787f70cf2ce0c413","first_computed_at":"2026-07-05T10:20:43.710400Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:20:43.710400Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7h9Y1GhGBm6YU6NrUvuv0VPbwYOl5O+Khu5P8qRCYvn3Q7gntm8HabeLJQy0acO05w5kXg2NJI2bMZHM2NaJBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:20:43.711072Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.08904","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b7d1f3d76a18e987e9982e074065a694111ff38cb5bd24f4d377b4f6d656ae5c","sha256:ada2ecebf79fa01c66a77620d05e0e2284c5a66217ced8e37b263cfa3e1ff461"],"state_sha256":"7817174c56953360cc4186fe5ad13761b3a6e7b0114a1aa6f1c3e486f9fe92a6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oV4gfWb7bWKWhRAni6tfSB/2naOQlSqq/LPCV6/vhvVmyJVcmeo8LD6HZCfhdDdIChaAuJm51cczwr2bXCDhBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T20:56:57.412780Z","bundle_sha256":"a202df694c38f582d1af292691ccf3a65b3e7c727d3133c04f84df1941f10aec"}}