{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QLTLL2BGFAFWEPFBVEPJTCA75Z","short_pith_number":"pith:QLTLL2BG","schema_version":"1.0","canonical_sha256":"82e6b5e826280b623ca1a91e99881fee5cb86218030fb255b642c808eb1a8fef","source":{"kind":"arxiv","id":"2502.09061","version":4},"attestation_state":"computed","paper":{"title":"CRANE: Reasoning with constrained LLM generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.PL","authors_text":"Debangshu Banerjee, Gagandeep Singh, Sasa Misailovic, Shubham Ugare, Tarun Suresh","submitted_at":"2025-02-13T08:23:42Z","abstract_excerpt":"Code generation, symbolic math reasoning, and other tasks require LLMs to produce outputs that are both syntactically and semantically correct. Constrained LLM generation is a promising direction to enforce adherence to formal grammar, but prior works have empirically observed that strict enforcement of formal constraints often diminishes the reasoning capabilities of LLMs. In this work, we first provide a theoretical explanation for why constraining LLM outputs to very restrictive grammars that only allow syntactically valid final answers reduces the reasoning capabilities of the model. Secon"},"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":"2502.09061","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.PL","submitted_at":"2025-02-13T08:23:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0f2d2aa150fc1b4678d9d7e61c436aac455b14a9ef9418cc074c8ee15d8ae1b7","abstract_canon_sha256":"b6864ee1af7e33221a05330f8719634d251f82bcd45a5cbb61957a1d08e58c41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:05:13.566412Z","signature_b64":"Y8J9wMQH/FXLoGgiJde8zJ8KToG7oLUw9FgTVZ9ciPGafWJ++r/5qwURZtHABYTEs1FuiIHGfnewcVRwAYbtCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82e6b5e826280b623ca1a91e99881fee5cb86218030fb255b642c808eb1a8fef","last_reissued_at":"2026-07-05T12:05:13.565798Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:05:13.565798Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CRANE: Reasoning with constrained LLM generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.PL","authors_text":"Debangshu Banerjee, Gagandeep Singh, Sasa Misailovic, Shubham Ugare, Tarun Suresh","submitted_at":"2025-02-13T08:23:42Z","abstract_excerpt":"Code generation, symbolic math reasoning, and other tasks require LLMs to produce outputs that are both syntactically and semantically correct. Constrained LLM generation is a promising direction to enforce adherence to formal grammar, but prior works have empirically observed that strict enforcement of formal constraints often diminishes the reasoning capabilities of LLMs. In this work, we first provide a theoretical explanation for why constraining LLM outputs to very restrictive grammars that only allow syntactically valid final answers reduces the reasoning capabilities of the model. Secon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.09061","kind":"arxiv","version":4},"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/2502.09061/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":"2502.09061","created_at":"2026-07-05T12:05:13.565866+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.09061v4","created_at":"2026-07-05T12:05:13.565866+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.09061","created_at":"2026-07-05T12:05:13.565866+00:00"},{"alias_kind":"pith_short_12","alias_value":"QLTLL2BGFAFW","created_at":"2026-07-05T12:05:13.565866+00:00"},{"alias_kind":"pith_short_16","alias_value":"QLTLL2BGFAFWEPFB","created_at":"2026-07-05T12:05:13.565866+00:00"},{"alias_kind":"pith_short_8","alias_value":"QLTLL2BG","created_at":"2026-07-05T12:05:13.565866+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04535","citing_title":"Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20193","citing_title":"Improving Quantized Model Performance in Qualitative Analysis with Multi-Pass Prompt Verification","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2509.01082","citing_title":"RefineStat: Efficient Exploration for Probabilistic Program Synthesis","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2511.22277","citing_title":"TreeCoder: Systematic Exploration and Optimisation of Decoding and Constraints for LLM Code Generation","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2512.05439","citing_title":"BEAVER: An Efficient Deterministic LLM Verifier","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02699","citing_title":"Trivial Vocabulary Bans Improve LLM Reasoning More Than Deep Linguistic Constraints","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14862","citing_title":"Schema Key Wording as an Instruction Channel in Structured Generation under Constrained Decoding","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QLTLL2BGFAFWEPFBVEPJTCA75Z","json":"https://pith.science/pith/QLTLL2BGFAFWEPFBVEPJTCA75Z.json","graph_json":"https://pith.science/api/pith-number/QLTLL2BGFAFWEPFBVEPJTCA75Z/graph.json","events_json":"https://pith.science/api/pith-number/QLTLL2BGFAFWEPFBVEPJTCA75Z/events.json","paper":"https://pith.science/paper/QLTLL2BG"},"agent_actions":{"view_html":"https://pith.science/pith/QLTLL2BGFAFWEPFBVEPJTCA75Z","download_json":"https://pith.science/pith/QLTLL2BGFAFWEPFBVEPJTCA75Z.json","view_paper":"https://pith.science/paper/QLTLL2BG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.09061&json=true","fetch_graph":"https://pith.science/api/pith-number/QLTLL2BGFAFWEPFBVEPJTCA75Z/graph.json","fetch_events":"https://pith.science/api/pith-number/QLTLL2BGFAFWEPFBVEPJTCA75Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QLTLL2BGFAFWEPFBVEPJTCA75Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QLTLL2BGFAFWEPFBVEPJTCA75Z/action/storage_attestation","attest_author":"https://pith.science/pith/QLTLL2BGFAFWEPFBVEPJTCA75Z/action/author_attestation","sign_citation":"https://pith.science/pith/QLTLL2BGFAFWEPFBVEPJTCA75Z/action/citation_signature","submit_replication":"https://pith.science/pith/QLTLL2BGFAFWEPFBVEPJTCA75Z/action/replication_record"}},"created_at":"2026-07-05T12:05:13.565866+00:00","updated_at":"2026-07-05T12:05:13.565866+00:00"}