{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GLV4RTCIOOCBXVXPHAY2HCKLD5","short_pith_number":"pith:GLV4RTCI","schema_version":"1.0","canonical_sha256":"32ebc8cc4873841bd6ef3831a3894b1f6ffd794d2eb62f8d4dfe3f380f551a5e","source":{"kind":"arxiv","id":"2505.13949","version":1},"attestation_state":"computed","paper":{"title":"FlashThink: An Early Exit Method For Efficient Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dixuan Wang, Guochao Jiang, Guofeng Quan, Zepeng Ding, Zheng Hu, Ziqin Luo","submitted_at":"2025-05-20T05:28:21Z","abstract_excerpt":"Large Language Models (LLMs) have shown impressive performance in reasoning tasks. However, LLMs tend to generate excessively long reasoning content, leading to significant computational overhead. Our observations indicate that even on simple problems, LLMs tend to produce unnecessarily lengthy reasoning content, which is against intuitive expectations. Preliminary experiments show that at a certain point during the generation process, the model is already capable of producing the correct solution without completing the full reasoning content. Therefore, we consider that the reasoning process "},"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":"2505.13949","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-20T05:28:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f23e02d6fbbccb01fe66acfcc0b2373c721f53010c57880e214ceeb694da781c","abstract_canon_sha256":"36920d839ad5677806c52e7d21d57199cb21574f0ed16706731f79b4961d2187"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:53.542027Z","signature_b64":"9/VfkVLlYo1TPPMxvjaSbEG/jwFPvFsUjigDd7/8+g9GemjaagliEMWYYmvwGlKqbKC2WxZHxFiu5X97LhGODw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"32ebc8cc4873841bd6ef3831a3894b1f6ffd794d2eb62f8d4dfe3f380f551a5e","last_reissued_at":"2026-07-05T11:05:53.541546Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:53.541546Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FlashThink: An Early Exit Method For Efficient Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dixuan Wang, Guochao Jiang, Guofeng Quan, Zepeng Ding, Zheng Hu, Ziqin Luo","submitted_at":"2025-05-20T05:28:21Z","abstract_excerpt":"Large Language Models (LLMs) have shown impressive performance in reasoning tasks. However, LLMs tend to generate excessively long reasoning content, leading to significant computational overhead. Our observations indicate that even on simple problems, LLMs tend to produce unnecessarily lengthy reasoning content, which is against intuitive expectations. Preliminary experiments show that at a certain point during the generation process, the model is already capable of producing the correct solution without completing the full reasoning content. Therefore, we consider that the reasoning process "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13949","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/2505.13949/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":"2505.13949","created_at":"2026-07-05T11:05:53.541600+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.13949v1","created_at":"2026-07-05T11:05:53.541600+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13949","created_at":"2026-07-05T11:05:53.541600+00:00"},{"alias_kind":"pith_short_12","alias_value":"GLV4RTCIOOCB","created_at":"2026-07-05T11:05:53.541600+00:00"},{"alias_kind":"pith_short_16","alias_value":"GLV4RTCIOOCBXVXP","created_at":"2026-07-05T11:05:53.541600+00:00"},{"alias_kind":"pith_short_8","alias_value":"GLV4RTCI","created_at":"2026-07-05T11:05:53.541600+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07108","citing_title":"DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2509.21743","citing_title":"Retrieval-of-Thought: Efficient Reasoning via Reusing Thoughts","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14062","citing_title":"Know When To Fold 'Em: Token-Efficient LLM Synthetic Data Generation via Multi-Stage In-Flight Rejection","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2503.16419","citing_title":"Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models","ref_index":73,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09104","citing_title":"Token Economics for LLM Agents: A Dual-View Study from Computing and Economics","ref_index":49,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06165","citing_title":"Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost","ref_index":239,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GLV4RTCIOOCBXVXPHAY2HCKLD5","json":"https://pith.science/pith/GLV4RTCIOOCBXVXPHAY2HCKLD5.json","graph_json":"https://pith.science/api/pith-number/GLV4RTCIOOCBXVXPHAY2HCKLD5/graph.json","events_json":"https://pith.science/api/pith-number/GLV4RTCIOOCBXVXPHAY2HCKLD5/events.json","paper":"https://pith.science/paper/GLV4RTCI"},"agent_actions":{"view_html":"https://pith.science/pith/GLV4RTCIOOCBXVXPHAY2HCKLD5","download_json":"https://pith.science/pith/GLV4RTCIOOCBXVXPHAY2HCKLD5.json","view_paper":"https://pith.science/paper/GLV4RTCI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.13949&json=true","fetch_graph":"https://pith.science/api/pith-number/GLV4RTCIOOCBXVXPHAY2HCKLD5/graph.json","fetch_events":"https://pith.science/api/pith-number/GLV4RTCIOOCBXVXPHAY2HCKLD5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GLV4RTCIOOCBXVXPHAY2HCKLD5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GLV4RTCIOOCBXVXPHAY2HCKLD5/action/storage_attestation","attest_author":"https://pith.science/pith/GLV4RTCIOOCBXVXPHAY2HCKLD5/action/author_attestation","sign_citation":"https://pith.science/pith/GLV4RTCIOOCBXVXPHAY2HCKLD5/action/citation_signature","submit_replication":"https://pith.science/pith/GLV4RTCIOOCBXVXPHAY2HCKLD5/action/replication_record"}},"created_at":"2026-07-05T11:05:53.541600+00:00","updated_at":"2026-07-05T11:05:53.541600+00:00"}