{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RSSSQ4WXWWA4QHVCGNAXN53LQO","short_pith_number":"pith:RSSSQ4WX","schema_version":"1.0","canonical_sha256":"8ca52872d7b581c81ea2334176f76b838c14333cca0911a69e8616fdbc83cc9e","source":{"kind":"arxiv","id":"2607.15660","version":1},"attestation_state":"computed","paper":{"title":"ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Cao Liu, Chenyang Zhang, Feng Hong, Fengpeng Yue, Ke Zeng, Shuaiyu Zhou, Yuanzhe Shen, Zengjie Hu","submitted_at":"2026-07-17T06:12:04Z","abstract_excerpt":"While LLM agents demonstrate strong reasoning abilities in compact and well-defined scenarios, they struggle to maintain robustness and effectiveness when faced with large-scale, diverse, and dynamic real-world environments that demand seamless tool integration. To address this gap, we introduce ToolVerse, a comprehensive framework that scales up agentic RL environments and enables agents to perform complex long-horizon reasoning in Tool-Integrated Reasoning (TIR) tasks. First, ToolVerse automatically builds the massive executable agent training environments from nearly 400 real-world Model Co"},"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":"2607.15660","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-17T06:12:04Z","cross_cats_sorted":[],"title_canon_sha256":"9d181925dc434896353df039b11856d0f15ca7284cfb5b9b28aae74c3931d422","abstract_canon_sha256":"347e1f8c59cc37256dd8b274eee84874af176800fa11a7b3763b7684c9487a69"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-20T01:19:02.796135Z","signature_b64":"IUjQ7cTz9fNMlSxKiibGlyWr8tzGo75QM65cTQhJNNFOgx0zzcyxgOkY8zhd+kJkJCfuOkBpLHv+TE9Y/uDWAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ca52872d7b581c81ea2334176f76b838c14333cca0911a69e8616fdbc83cc9e","last_reissued_at":"2026-07-20T01:19:02.795142Z","signature_status":"signed_v1","first_computed_at":"2026-07-20T01:19:02.795142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Cao Liu, Chenyang Zhang, Feng Hong, Fengpeng Yue, Ke Zeng, Shuaiyu Zhou, Yuanzhe Shen, Zengjie Hu","submitted_at":"2026-07-17T06:12:04Z","abstract_excerpt":"While LLM agents demonstrate strong reasoning abilities in compact and well-defined scenarios, they struggle to maintain robustness and effectiveness when faced with large-scale, diverse, and dynamic real-world environments that demand seamless tool integration. To address this gap, we introduce ToolVerse, a comprehensive framework that scales up agentic RL environments and enables agents to perform complex long-horizon reasoning in Tool-Integrated Reasoning (TIR) tasks. First, ToolVerse automatically builds the massive executable agent training environments from nearly 400 real-world Model Co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.15660","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/2607.15660/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":"2607.15660","created_at":"2026-07-20T01:19:02.795725+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.15660v1","created_at":"2026-07-20T01:19:02.795725+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.15660","created_at":"2026-07-20T01:19:02.795725+00:00"},{"alias_kind":"pith_short_12","alias_value":"RSSSQ4WXWWA4","created_at":"2026-07-20T01:19:02.795725+00:00"},{"alias_kind":"pith_short_16","alias_value":"RSSSQ4WXWWA4QHVC","created_at":"2026-07-20T01:19:02.795725+00:00"},{"alias_kind":"pith_short_8","alias_value":"RSSSQ4WX","created_at":"2026-07-20T01:19:02.795725+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/RSSSQ4WXWWA4QHVCGNAXN53LQO","json":"https://pith.science/pith/RSSSQ4WXWWA4QHVCGNAXN53LQO.json","graph_json":"https://pith.science/api/pith-number/RSSSQ4WXWWA4QHVCGNAXN53LQO/graph.json","events_json":"https://pith.science/api/pith-number/RSSSQ4WXWWA4QHVCGNAXN53LQO/events.json","paper":"https://pith.science/paper/RSSSQ4WX"},"agent_actions":{"view_html":"https://pith.science/pith/RSSSQ4WXWWA4QHVCGNAXN53LQO","download_json":"https://pith.science/pith/RSSSQ4WXWWA4QHVCGNAXN53LQO.json","view_paper":"https://pith.science/paper/RSSSQ4WX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.15660&json=true","fetch_graph":"https://pith.science/api/pith-number/RSSSQ4WXWWA4QHVCGNAXN53LQO/graph.json","fetch_events":"https://pith.science/api/pith-number/RSSSQ4WXWWA4QHVCGNAXN53LQO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RSSSQ4WXWWA4QHVCGNAXN53LQO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RSSSQ4WXWWA4QHVCGNAXN53LQO/action/storage_attestation","attest_author":"https://pith.science/pith/RSSSQ4WXWWA4QHVCGNAXN53LQO/action/author_attestation","sign_citation":"https://pith.science/pith/RSSSQ4WXWWA4QHVCGNAXN53LQO/action/citation_signature","submit_replication":"https://pith.science/pith/RSSSQ4WXWWA4QHVCGNAXN53LQO/action/replication_record"}},"created_at":"2026-07-20T01:19:02.795725+00:00","updated_at":"2026-07-20T01:19:02.795725+00:00"}