{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:P2FOP3TC527F7U6PMQ5M4O64X4","short_pith_number":"pith:P2FOP3TC","schema_version":"1.0","canonical_sha256":"7e8ae7ee62eebe5fd3cf643ace3bdcbf009ae9fa1add3314012bfb16b5461b45","source":{"kind":"arxiv","id":"2505.20664","version":1},"attestation_state":"computed","paper":{"title":"Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bibo Cai, Bing Qin, Kai Xiong, Ting Liu, Xiao Ding, Yang He, Yufei Zhang, Zhouhao Sun","submitted_at":"2025-05-27T03:18:31Z","abstract_excerpt":"While reasoning-augmented large language models (RLLMs) significantly enhance complex task performance through extended reasoning chains, they inevitably introduce substantial unnecessary token consumption, particularly for simpler problems where Short Chain-of-Thought (Short CoT) suffices. This overthinking phenomenon leads to inefficient resource usage without proportional accuracy gains. To address this issue, we propose Self-Route, a dynamic reasoning framework that automatically selects between general and reasoning modes based on model capability estimation. Our approach introduces a lig"},"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":"2505.20664","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-27T03:18:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9efe9c5e011d5ff8d768ead999478895d11dc0fed68d01544d9ede0fa0f9ac89","abstract_canon_sha256":"9a3557bb007e3b8d8d932ad38026fb700c0220d89beca0386a7ab6548fdf8579"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:16.264164Z","signature_b64":"MXptpGBYIU9VgfePvcYk0v3/0UZ4w9dXwcnD3RF8Es/0h/2yZxXye6HiGsuvy/ZS3XTT6Al2RSsvJj5o1TxhDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e8ae7ee62eebe5fd3cf643ace3bdcbf009ae9fa1add3314012bfb16b5461b45","last_reissued_at":"2026-07-05T11:10:16.263630Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:16.263630Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bibo Cai, Bing Qin, Kai Xiong, Ting Liu, Xiao Ding, Yang He, Yufei Zhang, Zhouhao Sun","submitted_at":"2025-05-27T03:18:31Z","abstract_excerpt":"While reasoning-augmented large language models (RLLMs) significantly enhance complex task performance through extended reasoning chains, they inevitably introduce substantial unnecessary token consumption, particularly for simpler problems where Short Chain-of-Thought (Short CoT) suffices. This overthinking phenomenon leads to inefficient resource usage without proportional accuracy gains. To address this issue, we propose Self-Route, a dynamic reasoning framework that automatically selects between general and reasoning modes based on model capability estimation. Our approach introduces a lig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.20664","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/2505.20664/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":"2505.20664","created_at":"2026-07-05T11:10:16.263696+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.20664v1","created_at":"2026-07-05T11:10:16.263696+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.20664","created_at":"2026-07-05T11:10:16.263696+00:00"},{"alias_kind":"pith_short_12","alias_value":"P2FOP3TC527F","created_at":"2026-07-05T11:10:16.263696+00:00"},{"alias_kind":"pith_short_16","alias_value":"P2FOP3TC527F7U6P","created_at":"2026-07-05T11:10:16.263696+00:00"},{"alias_kind":"pith_short_8","alias_value":"P2FOP3TC","created_at":"2026-07-05T11:10:16.263696+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2503.09567","citing_title":"Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models","ref_index":256,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P2FOP3TC527F7U6PMQ5M4O64X4","json":"https://pith.science/pith/P2FOP3TC527F7U6PMQ5M4O64X4.json","graph_json":"https://pith.science/api/pith-number/P2FOP3TC527F7U6PMQ5M4O64X4/graph.json","events_json":"https://pith.science/api/pith-number/P2FOP3TC527F7U6PMQ5M4O64X4/events.json","paper":"https://pith.science/paper/P2FOP3TC"},"agent_actions":{"view_html":"https://pith.science/pith/P2FOP3TC527F7U6PMQ5M4O64X4","download_json":"https://pith.science/pith/P2FOP3TC527F7U6PMQ5M4O64X4.json","view_paper":"https://pith.science/paper/P2FOP3TC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.20664&json=true","fetch_graph":"https://pith.science/api/pith-number/P2FOP3TC527F7U6PMQ5M4O64X4/graph.json","fetch_events":"https://pith.science/api/pith-number/P2FOP3TC527F7U6PMQ5M4O64X4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P2FOP3TC527F7U6PMQ5M4O64X4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P2FOP3TC527F7U6PMQ5M4O64X4/action/storage_attestation","attest_author":"https://pith.science/pith/P2FOP3TC527F7U6PMQ5M4O64X4/action/author_attestation","sign_citation":"https://pith.science/pith/P2FOP3TC527F7U6PMQ5M4O64X4/action/citation_signature","submit_replication":"https://pith.science/pith/P2FOP3TC527F7U6PMQ5M4O64X4/action/replication_record"}},"created_at":"2026-07-05T11:10:16.263696+00:00","updated_at":"2026-07-05T11:10:16.263696+00:00"}