{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UP3CPV47LYZGWX3ELEQZUCDDOK","short_pith_number":"pith:UP3CPV47","schema_version":"1.0","canonical_sha256":"a3f627d79f5e326b5f6459219a08637293ad665f8cb15aec8adb1d1d619b8ef7","source":{"kind":"arxiv","id":"2505.13778","version":1},"attestation_state":"computed","paper":{"title":"CoIn: Counting the Invisible Reasoning Tokens in Commercial Opaque LLM APIs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ang Li, Bowei Tian, Guoheng Sun, Meng Liu, Shwai He, Wanghao Ye, Yexiao He, Yiting Wang, Zheyu Shen, Ziyao Wang","submitted_at":"2025-05-19T23:39:23Z","abstract_excerpt":"As post-training techniques evolve, large language models (LLMs) are increasingly augmented with structured multi-step reasoning abilities, often optimized through reinforcement learning. These reasoning-enhanced models outperform standard LLMs on complex tasks and now underpin many commercial LLM APIs. However, to protect proprietary behavior and reduce verbosity, providers typically conceal the reasoning traces while returning only the final answer. This opacity introduces a critical transparency gap: users are billed for invisible reasoning tokens, which often account for the majority of th"},"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":"2505.13778","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-19T23:39:23Z","cross_cats_sorted":[],"title_canon_sha256":"e7da36c8464cf566388ce737b737fb9771cde4a5f960cb96b47405fb8b16e692","abstract_canon_sha256":"93c7b0e23f618b857e14f3a209980883e44fc9302c1072c11f326f34952374e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:50.883369Z","signature_b64":"YvSoMZZ6HRXUnveIHtpci44GhsoHmuvf5a4Ovz3W/lbImp3YDdGv9jSVxLU6rwgR3uHIPEv8XsRcpy5ttzFOBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3f627d79f5e326b5f6459219a08637293ad665f8cb15aec8adb1d1d619b8ef7","last_reissued_at":"2026-07-05T11:05:50.882875Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:50.882875Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CoIn: Counting the Invisible Reasoning Tokens in Commercial Opaque LLM APIs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ang Li, Bowei Tian, Guoheng Sun, Meng Liu, Shwai He, Wanghao Ye, Yexiao He, Yiting Wang, Zheyu Shen, Ziyao Wang","submitted_at":"2025-05-19T23:39:23Z","abstract_excerpt":"As post-training techniques evolve, large language models (LLMs) are increasingly augmented with structured multi-step reasoning abilities, often optimized through reinforcement learning. These reasoning-enhanced models outperform standard LLMs on complex tasks and now underpin many commercial LLM APIs. However, to protect proprietary behavior and reduce verbosity, providers typically conceal the reasoning traces while returning only the final answer. This opacity introduces a critical transparency gap: users are billed for invisible reasoning tokens, which often account for the majority of th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13778","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/2505.13778/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":"2505.13778","created_at":"2026-07-05T11:05:50.882934+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.13778v1","created_at":"2026-07-05T11:05:50.882934+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13778","created_at":"2026-07-05T11:05:50.882934+00:00"},{"alias_kind":"pith_short_12","alias_value":"UP3CPV47LYZG","created_at":"2026-07-05T11:05:50.882934+00:00"},{"alias_kind":"pith_short_16","alias_value":"UP3CPV47LYZGWX3E","created_at":"2026-07-05T11:05:50.882934+00:00"},{"alias_kind":"pith_short_8","alias_value":"UP3CPV47","created_at":"2026-07-05T11:05:50.882934+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.17410","citing_title":"Computational Challenges in Token Economics: Bridging Economic Theory and AI System Design","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UP3CPV47LYZGWX3ELEQZUCDDOK","json":"https://pith.science/pith/UP3CPV47LYZGWX3ELEQZUCDDOK.json","graph_json":"https://pith.science/api/pith-number/UP3CPV47LYZGWX3ELEQZUCDDOK/graph.json","events_json":"https://pith.science/api/pith-number/UP3CPV47LYZGWX3ELEQZUCDDOK/events.json","paper":"https://pith.science/paper/UP3CPV47"},"agent_actions":{"view_html":"https://pith.science/pith/UP3CPV47LYZGWX3ELEQZUCDDOK","download_json":"https://pith.science/pith/UP3CPV47LYZGWX3ELEQZUCDDOK.json","view_paper":"https://pith.science/paper/UP3CPV47","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.13778&json=true","fetch_graph":"https://pith.science/api/pith-number/UP3CPV47LYZGWX3ELEQZUCDDOK/graph.json","fetch_events":"https://pith.science/api/pith-number/UP3CPV47LYZGWX3ELEQZUCDDOK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UP3CPV47LYZGWX3ELEQZUCDDOK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UP3CPV47LYZGWX3ELEQZUCDDOK/action/storage_attestation","attest_author":"https://pith.science/pith/UP3CPV47LYZGWX3ELEQZUCDDOK/action/author_attestation","sign_citation":"https://pith.science/pith/UP3CPV47LYZGWX3ELEQZUCDDOK/action/citation_signature","submit_replication":"https://pith.science/pith/UP3CPV47LYZGWX3ELEQZUCDDOK/action/replication_record"}},"created_at":"2026-07-05T11:05:50.882934+00:00","updated_at":"2026-07-05T11:05:50.882934+00:00"}