{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:W3PHMNVXZQN3VUKWSQOSCIGBTP","short_pith_number":"pith:W3PHMNVX","schema_version":"1.0","canonical_sha256":"b6de7636b7cc1bbad156941d2120c19bd018be2b8e2fde35861091a028567a66","source":{"kind":"arxiv","id":"2502.12961","version":2},"attestation_state":"computed","paper":{"title":"Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Cong Zhang, Dexun Li, Hao Zhang, Kuicai Dong, Ruiming Tang, Weiwen Liu, Wenjun Li, Yasheng Wang, Yong Liu","submitted_at":"2025-02-18T15:45:01Z","abstract_excerpt":"Large language models (LLMs) have shown remarkable emergent capabilities, transforming the execution of functional tasks by leveraging external tools for complex problems that require specialized processing or up-to-date data. While existing research expands LLMs access to diverse tools (e.g., program interpreters, search engines, calculators), the necessity of using these tools is often overlooked, leading to indiscriminate tool invocation. This naive approach raises two key issues: increased latency due to unnecessary tool calls, and potential errors resulting from faulty interactions with e"},"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":"2502.12961","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-18T15:45:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6681da49ee6225bc05429e53012e90e6fc3f9bb29fa9b4e6d981d7f6b72850cb","abstract_canon_sha256":"97c3826c6101842ba52d16012074df667c9d3bb3f178ee7b1b1572f93dd18567"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:52.980807Z","signature_b64":"NUI05nEwe2DZhIreOpIorNOKBUAb8GZ4UT8hDLhB/00wMAl+Si3o23XYyjoEgDXxmH6mG40MVib4AiLL8WuCDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6de7636b7cc1bbad156941d2120c19bd018be2b8e2fde35861091a028567a66","last_reissued_at":"2026-07-05T11:56:52.979068Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:52.979068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Cong Zhang, Dexun Li, Hao Zhang, Kuicai Dong, Ruiming Tang, Weiwen Liu, Wenjun Li, Yasheng Wang, Yong Liu","submitted_at":"2025-02-18T15:45:01Z","abstract_excerpt":"Large language models (LLMs) have shown remarkable emergent capabilities, transforming the execution of functional tasks by leveraging external tools for complex problems that require specialized processing or up-to-date data. While existing research expands LLMs access to diverse tools (e.g., program interpreters, search engines, calculators), the necessity of using these tools is often overlooked, leading to indiscriminate tool invocation. This naive approach raises two key issues: increased latency due to unnecessary tool calls, and potential errors resulting from faulty interactions with e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.12961","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/2502.12961/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":"2502.12961","created_at":"2026-07-05T11:56:52.980171+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.12961v2","created_at":"2026-07-05T11:56:52.980171+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.12961","created_at":"2026-07-05T11:56:52.980171+00:00"},{"alias_kind":"pith_short_12","alias_value":"W3PHMNVXZQN3","created_at":"2026-07-05T11:56:52.980171+00:00"},{"alias_kind":"pith_short_16","alias_value":"W3PHMNVXZQN3VUKW","created_at":"2026-07-05T11:56:52.980171+00:00"},{"alias_kind":"pith_short_8","alias_value":"W3PHMNVX","created_at":"2026-07-05T11:56:52.980171+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.20120","citing_title":"Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W3PHMNVXZQN3VUKWSQOSCIGBTP","json":"https://pith.science/pith/W3PHMNVXZQN3VUKWSQOSCIGBTP.json","graph_json":"https://pith.science/api/pith-number/W3PHMNVXZQN3VUKWSQOSCIGBTP/graph.json","events_json":"https://pith.science/api/pith-number/W3PHMNVXZQN3VUKWSQOSCIGBTP/events.json","paper":"https://pith.science/paper/W3PHMNVX"},"agent_actions":{"view_html":"https://pith.science/pith/W3PHMNVXZQN3VUKWSQOSCIGBTP","download_json":"https://pith.science/pith/W3PHMNVXZQN3VUKWSQOSCIGBTP.json","view_paper":"https://pith.science/paper/W3PHMNVX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.12961&json=true","fetch_graph":"https://pith.science/api/pith-number/W3PHMNVXZQN3VUKWSQOSCIGBTP/graph.json","fetch_events":"https://pith.science/api/pith-number/W3PHMNVXZQN3VUKWSQOSCIGBTP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W3PHMNVXZQN3VUKWSQOSCIGBTP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W3PHMNVXZQN3VUKWSQOSCIGBTP/action/storage_attestation","attest_author":"https://pith.science/pith/W3PHMNVXZQN3VUKWSQOSCIGBTP/action/author_attestation","sign_citation":"https://pith.science/pith/W3PHMNVXZQN3VUKWSQOSCIGBTP/action/citation_signature","submit_replication":"https://pith.science/pith/W3PHMNVXZQN3VUKWSQOSCIGBTP/action/replication_record"}},"created_at":"2026-07-05T11:56:52.980171+00:00","updated_at":"2026-07-05T11:56:52.980171+00:00"}