{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UFZJWNEF2ZWJZRGD4AWKJDACJV","short_pith_number":"pith:UFZJWNEF","schema_version":"1.0","canonical_sha256":"a1729b3485d66c9cc4c3e02ca48c024d4db0ff4dcb30e5bdb23c5eacf24b2c0f","source":{"kind":"arxiv","id":"2310.12342","version":2},"attestation_state":"computed","paper":{"title":"Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dawei Li, Jingbo Shang, Simeng Han, Sizhe Wang, Yifan Wang, Yongqi Tong, Zi Lin","submitted_at":"2023-10-18T21:42:16Z","abstract_excerpt":"Chain-of-Thought(CoT) prompting and its variants explore equipping large language models (LLMs) with high-level reasoning abilities by emulating human-like linear cognition and logic. However, the human mind is complicated and mixed with both linear and nonlinear thinking. In this work, we propose \\textbf{I}nferential \\textbf{E}xclusion \\textbf{P}rompting (IEP), a novel prompting that combines the principles of elimination and inference in order to guide LLMs to think non-linearly. IEP guides LLMs to plan and then utilize Natural Language Inference (NLI) to deduce each possible solution's enta"},"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":"2310.12342","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-18T21:42:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"79ca6e8b565e81c4e196e7d66fb6627b3ce29fc923ab40e4b9ddee18c6c79517","abstract_canon_sha256":"3c562b7548e8325351f479d9051edd09a4b02b75e4911f0a5a808f925ca4f30f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:12:54.500823Z","signature_b64":"wwZaKwSO5+qv+y7PtzyNA15m/TvJRoBqgSRvZWfYsOpba1O6GiAlIebFj5iyBpRdbzdEkfzqvDyC9Q1ZHYQUCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1729b3485d66c9cc4c3e02ca48c024d4db0ff4dcb30e5bdb23c5eacf24b2c0f","last_reissued_at":"2026-07-05T07:12:54.500368Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:12:54.500368Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dawei Li, Jingbo Shang, Simeng Han, Sizhe Wang, Yifan Wang, Yongqi Tong, Zi Lin","submitted_at":"2023-10-18T21:42:16Z","abstract_excerpt":"Chain-of-Thought(CoT) prompting and its variants explore equipping large language models (LLMs) with high-level reasoning abilities by emulating human-like linear cognition and logic. However, the human mind is complicated and mixed with both linear and nonlinear thinking. In this work, we propose \\textbf{I}nferential \\textbf{E}xclusion \\textbf{P}rompting (IEP), a novel prompting that combines the principles of elimination and inference in order to guide LLMs to think non-linearly. IEP guides LLMs to plan and then utilize Natural Language Inference (NLI) to deduce each possible solution's enta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.12342","kind":"arxiv","version":2},"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/2310.12342/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":"2310.12342","created_at":"2026-07-05T07:12:54.500426+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.12342v2","created_at":"2026-07-05T07:12:54.500426+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.12342","created_at":"2026-07-05T07:12:54.500426+00:00"},{"alias_kind":"pith_short_12","alias_value":"UFZJWNEF2ZWJ","created_at":"2026-07-05T07:12:54.500426+00:00"},{"alias_kind":"pith_short_16","alias_value":"UFZJWNEF2ZWJZRGD","created_at":"2026-07-05T07:12:54.500426+00:00"},{"alias_kind":"pith_short_8","alias_value":"UFZJWNEF","created_at":"2026-07-05T07:12:54.500426+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2405.19088","citing_title":"Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2412.14368","citing_title":"Beyond Math: Stories as a Testbed for Memorization-Constrained Reasoning in LLMs","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2503.23137","citing_title":"When 'YES' Meets 'BUT': Can Large Models Comprehend Contradictory Humor Through Comparative Reasoning?","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UFZJWNEF2ZWJZRGD4AWKJDACJV","json":"https://pith.science/pith/UFZJWNEF2ZWJZRGD4AWKJDACJV.json","graph_json":"https://pith.science/api/pith-number/UFZJWNEF2ZWJZRGD4AWKJDACJV/graph.json","events_json":"https://pith.science/api/pith-number/UFZJWNEF2ZWJZRGD4AWKJDACJV/events.json","paper":"https://pith.science/paper/UFZJWNEF"},"agent_actions":{"view_html":"https://pith.science/pith/UFZJWNEF2ZWJZRGD4AWKJDACJV","download_json":"https://pith.science/pith/UFZJWNEF2ZWJZRGD4AWKJDACJV.json","view_paper":"https://pith.science/paper/UFZJWNEF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.12342&json=true","fetch_graph":"https://pith.science/api/pith-number/UFZJWNEF2ZWJZRGD4AWKJDACJV/graph.json","fetch_events":"https://pith.science/api/pith-number/UFZJWNEF2ZWJZRGD4AWKJDACJV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UFZJWNEF2ZWJZRGD4AWKJDACJV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UFZJWNEF2ZWJZRGD4AWKJDACJV/action/storage_attestation","attest_author":"https://pith.science/pith/UFZJWNEF2ZWJZRGD4AWKJDACJV/action/author_attestation","sign_citation":"https://pith.science/pith/UFZJWNEF2ZWJZRGD4AWKJDACJV/action/citation_signature","submit_replication":"https://pith.science/pith/UFZJWNEF2ZWJZRGD4AWKJDACJV/action/replication_record"}},"created_at":"2026-07-05T07:12:54.500426+00:00","updated_at":"2026-07-05T07:12:54.500426+00:00"}