{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IKJLYKXBTL7GPUZKG2S3ZW4Y6P","short_pith_number":"pith:IKJLYKXB","schema_version":"1.0","canonical_sha256":"4292bc2ae19afe67d32a36a5bcdb98f3fa888cd2dff1e64693c88e3c70135883","source":{"kind":"arxiv","id":"2405.00557","version":5},"attestation_state":"computed","paper":{"title":"Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Fei Mi, James T. Kwok, Jiahui Gao, Kai Chen, Lanqing Hong, Qun Liu, Xin Jiang, Yunhao Gou, Yu Zhang, Zhenguo Li, Zhili Liu","submitted_at":"2024-05-01T15:06:05Z","abstract_excerpt":"As the capabilities of large language models (LLMs) continue to expand, aligning these models with human values remains a significant challenge. Recent studies show that reasoning abilities contribute significantly to model safety, while integrating Mixture-of-Experts (MoE) architectures can further enhance alignment. In this work, we address a fundamental question: How to effectively incorporate reasoning abilities and MoE architectures into self-alignment process in LLMs? We propose Mixture of insighTful Experts (MoTE), a novel framework that synergistically combines reasoning chains and exp"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2405.00557","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-05-01T15:06:05Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f663880e21865642e9afba8942170e3343083ec83e6e35ce681e8e189785fb8a","abstract_canon_sha256":"9195bd66740e2bfd9d8a07961f5b63bf4fc6173e70476b4ade82c2b5c0baac06"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:09.357451Z","signature_b64":"Mic58KA907vUshaqoVvDq+gTLCBYpsshpMFNkAjLR+g6cKlqyKdXwSiie9jfqItvfuA+zoZaLJVKMfCDY5rzCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4292bc2ae19afe67d32a36a5bcdb98f3fa888cd2dff1e64693c88e3c70135883","last_reissued_at":"2026-07-05T11:13:09.356871Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:09.356871Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Fei Mi, James T. Kwok, Jiahui Gao, Kai Chen, Lanqing Hong, Qun Liu, Xin Jiang, Yunhao Gou, Yu Zhang, Zhenguo Li, Zhili Liu","submitted_at":"2024-05-01T15:06:05Z","abstract_excerpt":"As the capabilities of large language models (LLMs) continue to expand, aligning these models with human values remains a significant challenge. Recent studies show that reasoning abilities contribute significantly to model safety, while integrating Mixture-of-Experts (MoE) architectures can further enhance alignment. In this work, we address a fundamental question: How to effectively incorporate reasoning abilities and MoE architectures into self-alignment process in LLMs? We propose Mixture of insighTful Experts (MoTE), a novel framework that synergistically combines reasoning chains and exp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.00557","kind":"arxiv","version":5},"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/2405.00557/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2405.00557","created_at":"2026-07-05T11:13:09.356945+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.00557v5","created_at":"2026-07-05T11:13:09.356945+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.00557","created_at":"2026-07-05T11:13:09.356945+00:00"},{"alias_kind":"pith_short_12","alias_value":"IKJLYKXBTL7G","created_at":"2026-07-05T11:13:09.356945+00:00"},{"alias_kind":"pith_short_16","alias_value":"IKJLYKXBTL7GPUZK","created_at":"2026-07-05T11:13:09.356945+00:00"},{"alias_kind":"pith_short_8","alias_value":"IKJLYKXB","created_at":"2026-07-05T11:13:09.356945+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.01735","citing_title":"ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IKJLYKXBTL7GPUZKG2S3ZW4Y6P","json":"https://pith.science/pith/IKJLYKXBTL7GPUZKG2S3ZW4Y6P.json","graph_json":"https://pith.science/api/pith-number/IKJLYKXBTL7GPUZKG2S3ZW4Y6P/graph.json","events_json":"https://pith.science/api/pith-number/IKJLYKXBTL7GPUZKG2S3ZW4Y6P/events.json","paper":"https://pith.science/paper/IKJLYKXB"},"agent_actions":{"view_html":"https://pith.science/pith/IKJLYKXBTL7GPUZKG2S3ZW4Y6P","download_json":"https://pith.science/pith/IKJLYKXBTL7GPUZKG2S3ZW4Y6P.json","view_paper":"https://pith.science/paper/IKJLYKXB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.00557&json=true","fetch_graph":"https://pith.science/api/pith-number/IKJLYKXBTL7GPUZKG2S3ZW4Y6P/graph.json","fetch_events":"https://pith.science/api/pith-number/IKJLYKXBTL7GPUZKG2S3ZW4Y6P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IKJLYKXBTL7GPUZKG2S3ZW4Y6P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IKJLYKXBTL7GPUZKG2S3ZW4Y6P/action/storage_attestation","attest_author":"https://pith.science/pith/IKJLYKXBTL7GPUZKG2S3ZW4Y6P/action/author_attestation","sign_citation":"https://pith.science/pith/IKJLYKXBTL7GPUZKG2S3ZW4Y6P/action/citation_signature","submit_replication":"https://pith.science/pith/IKJLYKXBTL7GPUZKG2S3ZW4Y6P/action/replication_record"}},"created_at":"2026-07-05T11:13:09.356945+00:00","updated_at":"2026-07-05T11:13:09.356945+00:00"}