{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:B7XZR6ZNAFDUSGPQRCN7IUF3Y5","short_pith_number":"pith:B7XZR6ZN","schema_version":"1.0","canonical_sha256":"0fef98fb2d01474919f0889bf450bbc74ffd3ff18c3206caeadd0b610053deac","source":{"kind":"arxiv","id":"2506.14755","version":2},"attestation_state":"computed","paper":{"title":"Optimizing Length Compression in Large Reasoning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Dongping Chen, Mingyang Fu, Tianyi Zhou, Zhengxiang Cheng","submitted_at":"2025-06-17T17:50:16Z","abstract_excerpt":"Large Reasoning Models (LRMs) have achieved remarkable success, yet they often suffer from producing unnecessary and verbose reasoning chains. We identify a core aspect of this issue as \"invalid thinking\" -- models tend to repeatedly double-check their work after having derived the correct answer. To address this specific inefficiency, we move beyond the general principles of Efficacy and Efficiency to propose two new, fine-grained principles: Brevity, which advocates for eliminating redundancy, and Sufficiency, which ensures critical reasoning steps are preserved. Guided by these principles, "},"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":"2506.14755","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-17T17:50:16Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"4c111cbcf82aafbe8c9f07488cb3c1c58b2155d4ec37ee9b18bb43e10363fe61","abstract_canon_sha256":"5d0c37cec840523ceb522f88afe765fcbd8ac989333fdd1a5a79f4b457c2c44e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:20.820477Z","signature_b64":"o3EWxfZZQLtrN+P+xRvQobUgH22XFo/Mz/imcLU7tpxUjoniKuCYAd6oY/eKpA7Hnqfns7dCidLc5J2vilQYCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0fef98fb2d01474919f0889bf450bbc74ffd3ff18c3206caeadd0b610053deac","last_reissued_at":"2026-07-05T12:09:20.819988Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:20.819988Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing Length Compression in Large Reasoning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Dongping Chen, Mingyang Fu, Tianyi Zhou, Zhengxiang Cheng","submitted_at":"2025-06-17T17:50:16Z","abstract_excerpt":"Large Reasoning Models (LRMs) have achieved remarkable success, yet they often suffer from producing unnecessary and verbose reasoning chains. We identify a core aspect of this issue as \"invalid thinking\" -- models tend to repeatedly double-check their work after having derived the correct answer. To address this specific inefficiency, we move beyond the general principles of Efficacy and Efficiency to propose two new, fine-grained principles: Brevity, which advocates for eliminating redundancy, and Sufficiency, which ensures critical reasoning steps are preserved. Guided by these principles, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14755","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/2506.14755/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":"2506.14755","created_at":"2026-07-05T12:09:20.820043+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.14755v2","created_at":"2026-07-05T12:09:20.820043+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14755","created_at":"2026-07-05T12:09:20.820043+00:00"},{"alias_kind":"pith_short_12","alias_value":"B7XZR6ZNAFDU","created_at":"2026-07-05T12:09:20.820043+00:00"},{"alias_kind":"pith_short_16","alias_value":"B7XZR6ZNAFDUSGPQ","created_at":"2026-07-05T12:09:20.820043+00:00"},{"alias_kind":"pith_short_8","alias_value":"B7XZR6ZN","created_at":"2026-07-05T12:09:20.820043+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.02871","citing_title":"Adaptive Latent Agentic Reasoning","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.30832","citing_title":"SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2510.19669","citing_title":"DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2503.09567","citing_title":"Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models","ref_index":125,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07316","citing_title":"Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B7XZR6ZNAFDUSGPQRCN7IUF3Y5","json":"https://pith.science/pith/B7XZR6ZNAFDUSGPQRCN7IUF3Y5.json","graph_json":"https://pith.science/api/pith-number/B7XZR6ZNAFDUSGPQRCN7IUF3Y5/graph.json","events_json":"https://pith.science/api/pith-number/B7XZR6ZNAFDUSGPQRCN7IUF3Y5/events.json","paper":"https://pith.science/paper/B7XZR6ZN"},"agent_actions":{"view_html":"https://pith.science/pith/B7XZR6ZNAFDUSGPQRCN7IUF3Y5","download_json":"https://pith.science/pith/B7XZR6ZNAFDUSGPQRCN7IUF3Y5.json","view_paper":"https://pith.science/paper/B7XZR6ZN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.14755&json=true","fetch_graph":"https://pith.science/api/pith-number/B7XZR6ZNAFDUSGPQRCN7IUF3Y5/graph.json","fetch_events":"https://pith.science/api/pith-number/B7XZR6ZNAFDUSGPQRCN7IUF3Y5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B7XZR6ZNAFDUSGPQRCN7IUF3Y5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B7XZR6ZNAFDUSGPQRCN7IUF3Y5/action/storage_attestation","attest_author":"https://pith.science/pith/B7XZR6ZNAFDUSGPQRCN7IUF3Y5/action/author_attestation","sign_citation":"https://pith.science/pith/B7XZR6ZNAFDUSGPQRCN7IUF3Y5/action/citation_signature","submit_replication":"https://pith.science/pith/B7XZR6ZNAFDUSGPQRCN7IUF3Y5/action/replication_record"}},"created_at":"2026-07-05T12:09:20.820043+00:00","updated_at":"2026-07-05T12:09:20.820043+00:00"}