{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:SKZPINNYRCB2JVMH7JXGYTNIFN","short_pith_number":"pith:SKZPINNY","schema_version":"1.0","canonical_sha256":"92b2f435b88883a4d587fa6e6c4da82b57f7cfdc932bb09bf6fc0fddd16a9778","source":{"kind":"arxiv","id":"2208.14271","version":1},"attestation_state":"computed","paper":{"title":"Faithful Reasoning Using Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Antonia Creswell, Murray Shanahan","submitted_at":"2022-08-30T13:44:41Z","abstract_excerpt":"Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model. This entails an unwelcome degree of opacity and compromises performance, especially on problems that are inherently multi-step. To address these limitations, we show how LMs can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem. Our approach works by chaining together reasoning steps, where each step results from calls to two fin"},"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":"2208.14271","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-08-30T13:44:41Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"6c4682bda3be0d3d040d7c923de7f3a8da25bdc741ddf5d875368e3e32512332","abstract_canon_sha256":"b1c6981759fc8a468b036c8ae677db377ccc1b2b3e98ff001364952db6ff2e9d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:53:10.541482Z","signature_b64":"gBrBtYqYZlgzKSjPTH9v8Q9ZvUPrWHwh53w/oYeI8d2sMoZI11xy09z7ZCe2ggkG6H95+Dtyh0im3LASmTANAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92b2f435b88883a4d587fa6e6c4da82b57f7cfdc932bb09bf6fc0fddd16a9778","last_reissued_at":"2026-07-05T04:53:10.541033Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:53:10.541033Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Faithful Reasoning Using Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Antonia Creswell, Murray Shanahan","submitted_at":"2022-08-30T13:44:41Z","abstract_excerpt":"Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model. This entails an unwelcome degree of opacity and compromises performance, especially on problems that are inherently multi-step. To address these limitations, we show how LMs can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem. Our approach works by chaining together reasoning steps, where each step results from calls to two fin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.14271","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/2208.14271/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":"2208.14271","created_at":"2026-07-05T04:53:10.541088+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.14271v1","created_at":"2026-07-05T04:53:10.541088+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.14271","created_at":"2026-07-05T04:53:10.541088+00:00"},{"alias_kind":"pith_short_12","alias_value":"SKZPINNYRCB2","created_at":"2026-07-05T04:53:10.541088+00:00"},{"alias_kind":"pith_short_16","alias_value":"SKZPINNYRCB2JVMH","created_at":"2026-07-05T04:53:10.541088+00:00"},{"alias_kind":"pith_short_8","alias_value":"SKZPINNY","created_at":"2026-07-05T04:53:10.541088+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":12,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07507","citing_title":"HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2606.16118","citing_title":"Know Your Limits : On the Faithfulness of LLMs as Solvers and Autoformalizers in Legal Reasoning","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27628","citing_title":"Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2410.04047","citing_title":"TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2506.06211","citing_title":"PuzzleWorld: A Benchmark for Multimodal, Open-Ended Reasoning in Puzzlehunts","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2303.17491","citing_title":"Language Models can Solve Computer Tasks","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11746","citing_title":"When Reasoning Traces Become Performative: Step-Level Evidence that Chain-of-Thought Is an Imperfect Oversight Channel","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2307.13702","citing_title":"Measuring Faithfulness in Chain-of-Thought Reasoning","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2305.10601","citing_title":"Tree of Thoughts: Deliberate Problem Solving with Large Language Models","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07897","citing_title":"Visual Perceptual to Conceptual First-Order Rule Learning Networks","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14641","citing_title":"Learning to Draw ASCII Improves Spatial Reasoning in Language Models","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15726","citing_title":"LLM Reasoning Is Latent, Not the Chain of Thought","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SKZPINNYRCB2JVMH7JXGYTNIFN","json":"https://pith.science/pith/SKZPINNYRCB2JVMH7JXGYTNIFN.json","graph_json":"https://pith.science/api/pith-number/SKZPINNYRCB2JVMH7JXGYTNIFN/graph.json","events_json":"https://pith.science/api/pith-number/SKZPINNYRCB2JVMH7JXGYTNIFN/events.json","paper":"https://pith.science/paper/SKZPINNY"},"agent_actions":{"view_html":"https://pith.science/pith/SKZPINNYRCB2JVMH7JXGYTNIFN","download_json":"https://pith.science/pith/SKZPINNYRCB2JVMH7JXGYTNIFN.json","view_paper":"https://pith.science/paper/SKZPINNY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.14271&json=true","fetch_graph":"https://pith.science/api/pith-number/SKZPINNYRCB2JVMH7JXGYTNIFN/graph.json","fetch_events":"https://pith.science/api/pith-number/SKZPINNYRCB2JVMH7JXGYTNIFN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SKZPINNYRCB2JVMH7JXGYTNIFN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SKZPINNYRCB2JVMH7JXGYTNIFN/action/storage_attestation","attest_author":"https://pith.science/pith/SKZPINNYRCB2JVMH7JXGYTNIFN/action/author_attestation","sign_citation":"https://pith.science/pith/SKZPINNYRCB2JVMH7JXGYTNIFN/action/citation_signature","submit_replication":"https://pith.science/pith/SKZPINNYRCB2JVMH7JXGYTNIFN/action/replication_record"}},"created_at":"2026-07-05T04:53:10.541088+00:00","updated_at":"2026-07-05T04:53:10.541088+00:00"}