{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CVMYZLMZIQHURUDMZWYNDUEWOE","short_pith_number":"pith:CVMYZLMZ","schema_version":"1.0","canonical_sha256":"15598cad99440f48d06ccdb0d1d096712b4489473ff4f1c81ee83742726e87ea","source":{"kind":"arxiv","id":"2406.06580","version":1},"attestation_state":"computed","paper":{"title":"Break the Chain: Large Language Models Can be Shortcut Reasoners","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hanmeng Liu, Jian Song, Mengru Ding, Wenbo Xie, Yue Zhang, Zhizhang Fu","submitted_at":"2024-06-04T14:02:53Z","abstract_excerpt":"Recent advancements in Chain-of-Thought (CoT) reasoning utilize complex modules but are hampered by high token consumption, limited applicability, and challenges in reproducibility. This paper conducts a critical evaluation of CoT prompting, extending beyond arithmetic to include complex logical and commonsense reasoning tasks, areas where standard CoT methods fall short. We propose the integration of human-like heuristics and shortcuts into language models (LMs) through \"break the chain\" strategies. These strategies disrupt traditional CoT processes using controlled variables to assess their "},"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":"2406.06580","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-04T14:02:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a9a61541efb2e1954b480ff012ef63e7ab319056d8947c43378557e0ce83a448","abstract_canon_sha256":"78ed36488ba22bdcf9a06d170f3eb5c962fd340d7ccc6b2b161df2bc6c2ba398"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:29:47.702086Z","signature_b64":"0MVDSF30o6Ucb1jXVMkfKeT2GiKWSj/+jqSRPUsFwcbgMslufEiN1Rh7kbCcGX+DYMUy0ddvazf2xeroK8/dDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15598cad99440f48d06ccdb0d1d096712b4489473ff4f1c81ee83742726e87ea","last_reissued_at":"2026-07-05T08:29:47.701684Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:29:47.701684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Break the Chain: Large Language Models Can be Shortcut Reasoners","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hanmeng Liu, Jian Song, Mengru Ding, Wenbo Xie, Yue Zhang, Zhizhang Fu","submitted_at":"2024-06-04T14:02:53Z","abstract_excerpt":"Recent advancements in Chain-of-Thought (CoT) reasoning utilize complex modules but are hampered by high token consumption, limited applicability, and challenges in reproducibility. This paper conducts a critical evaluation of CoT prompting, extending beyond arithmetic to include complex logical and commonsense reasoning tasks, areas where standard CoT methods fall short. We propose the integration of human-like heuristics and shortcuts into language models (LMs) through \"break the chain\" strategies. These strategies disrupt traditional CoT processes using controlled variables to assess their "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.06580","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/2406.06580/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":"2406.06580","created_at":"2026-07-05T08:29:47.701739+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.06580v1","created_at":"2026-07-05T08:29:47.701739+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.06580","created_at":"2026-07-05T08:29:47.701739+00:00"},{"alias_kind":"pith_short_12","alias_value":"CVMYZLMZIQHU","created_at":"2026-07-05T08:29:47.701739+00:00"},{"alias_kind":"pith_short_16","alias_value":"CVMYZLMZIQHURUDM","created_at":"2026-07-05T08:29:47.701739+00:00"},{"alias_kind":"pith_short_8","alias_value":"CVMYZLMZ","created_at":"2026-07-05T08:29:47.701739+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07108","citing_title":"DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2510.15079","citing_title":"Assessing Coherency and Consistency of Code Execution Reasoning by Large Language Models","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03679","citing_title":"LightThinker++: From Reasoning Compression to Memory Management","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CVMYZLMZIQHURUDMZWYNDUEWOE","json":"https://pith.science/pith/CVMYZLMZIQHURUDMZWYNDUEWOE.json","graph_json":"https://pith.science/api/pith-number/CVMYZLMZIQHURUDMZWYNDUEWOE/graph.json","events_json":"https://pith.science/api/pith-number/CVMYZLMZIQHURUDMZWYNDUEWOE/events.json","paper":"https://pith.science/paper/CVMYZLMZ"},"agent_actions":{"view_html":"https://pith.science/pith/CVMYZLMZIQHURUDMZWYNDUEWOE","download_json":"https://pith.science/pith/CVMYZLMZIQHURUDMZWYNDUEWOE.json","view_paper":"https://pith.science/paper/CVMYZLMZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.06580&json=true","fetch_graph":"https://pith.science/api/pith-number/CVMYZLMZIQHURUDMZWYNDUEWOE/graph.json","fetch_events":"https://pith.science/api/pith-number/CVMYZLMZIQHURUDMZWYNDUEWOE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CVMYZLMZIQHURUDMZWYNDUEWOE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CVMYZLMZIQHURUDMZWYNDUEWOE/action/storage_attestation","attest_author":"https://pith.science/pith/CVMYZLMZIQHURUDMZWYNDUEWOE/action/author_attestation","sign_citation":"https://pith.science/pith/CVMYZLMZIQHURUDMZWYNDUEWOE/action/citation_signature","submit_replication":"https://pith.science/pith/CVMYZLMZIQHURUDMZWYNDUEWOE/action/replication_record"}},"created_at":"2026-07-05T08:29:47.701739+00:00","updated_at":"2026-07-05T08:29:47.701739+00:00"}