{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SRI7TKFOPD2PYJ7GFQ3DQNJ74Y","short_pith_number":"pith:SRI7TKFO","schema_version":"1.0","canonical_sha256":"9451f9a8ae78f4fc27e62c3638353fe636394311bf857d4321187c698576d6c1","source":{"kind":"arxiv","id":"2605.17774","version":1},"attestation_state":"computed","paper":{"title":"Internalizing Tool Knowledge in Small Language Models via QLoRA Fine-Tuning","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ayal Yakobe, Tanmay Agarwal, Yuval Shemla","submitted_at":"2026-05-18T02:48:46Z","abstract_excerpt":"Large language models are increasingly used as planning components in agentic systems, but current tool-use pipelines often require full tool schemas to be included in every prompt, creating substantial token overhead and limiting the practicality of smaller models. This paper investigates whether tool-use knowledge can be internalized into small language models through parameter-efficient fine-tuning, enabling structured planning without explicit tool descriptions at inference time. Using AssetOpsBench as the primary benchmark, we fine-tune Gemma 4 E4B and Qwen3-4B with 8-bit QLoRA on approxi"},"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":"2605.17774","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-18T02:48:46Z","cross_cats_sorted":[],"title_canon_sha256":"c276b1a066b1129466f5155b6cd0f4d3c1b4545628a6765081c0af0cec77f27c","abstract_canon_sha256":"c385509bcf879bd7924036b799a42748a40877a5ccf81a3f787cb862d4499c9e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:57.534477Z","signature_b64":"dBguzuSVfagOaDhp4UNn2WuGyl3CV2A0llaqtQdiCdc1bF8/1n5dLCFx+FkAHfFgEJh2SzRZcZlDWL6PHSnnDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9451f9a8ae78f4fc27e62c3638353fe636394311bf857d4321187c698576d6c1","last_reissued_at":"2026-05-20T00:04:57.533715Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:57.533715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Internalizing Tool Knowledge in Small Language Models via QLoRA Fine-Tuning","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ayal Yakobe, Tanmay Agarwal, Yuval Shemla","submitted_at":"2026-05-18T02:48:46Z","abstract_excerpt":"Large language models are increasingly used as planning components in agentic systems, but current tool-use pipelines often require full tool schemas to be included in every prompt, creating substantial token overhead and limiting the practicality of smaller models. This paper investigates whether tool-use knowledge can be internalized into small language models through parameter-efficient fine-tuning, enabling structured planning without explicit tool descriptions at inference time. Using AssetOpsBench as the primary benchmark, we fine-tune Gemma 4 E4B and Qwen3-4B with 8-bit QLoRA on approxi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17774","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/2605.17774/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":"2605.17774","created_at":"2026-05-20T00:04:57.533847+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17774v1","created_at":"2026-05-20T00:04:57.533847+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17774","created_at":"2026-05-20T00:04:57.533847+00:00"},{"alias_kind":"pith_short_12","alias_value":"SRI7TKFOPD2P","created_at":"2026-05-20T00:04:57.533847+00:00"},{"alias_kind":"pith_short_16","alias_value":"SRI7TKFOPD2PYJ7G","created_at":"2026-05-20T00:04:57.533847+00:00"},{"alias_kind":"pith_short_8","alias_value":"SRI7TKFO","created_at":"2026-05-20T00:04:57.533847+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y","json":"https://pith.science/pith/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y.json","graph_json":"https://pith.science/api/pith-number/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y/graph.json","events_json":"https://pith.science/api/pith-number/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y/events.json","paper":"https://pith.science/paper/SRI7TKFO"},"agent_actions":{"view_html":"https://pith.science/pith/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y","download_json":"https://pith.science/pith/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y.json","view_paper":"https://pith.science/paper/SRI7TKFO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17774&json=true","fetch_graph":"https://pith.science/api/pith-number/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y/graph.json","fetch_events":"https://pith.science/api/pith-number/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y/action/storage_attestation","attest_author":"https://pith.science/pith/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y/action/author_attestation","sign_citation":"https://pith.science/pith/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y/action/citation_signature","submit_replication":"https://pith.science/pith/SRI7TKFOPD2PYJ7GFQ3DQNJ74Y/action/replication_record"}},"created_at":"2026-05-20T00:04:57.533847+00:00","updated_at":"2026-05-20T00:04:57.533847+00:00"}