{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7RIBYGD3GI4YSF2TVJPQMAJDWN","short_pith_number":"pith:7RIBYGD3","schema_version":"1.0","canonical_sha256":"fc501c187b3239891753aa5f060123b341ed351785ee18d507bd5774d0d528be","source":{"kind":"arxiv","id":"2606.18733","version":1},"attestation_state":"computed","paper":{"title":"SWE-Future: Forecast-Conditioned Data Synthesis for Future-Oriented Software Engineering Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Hanwen Du, JianYing Qu, Jun Zhang, Qiao Zhao, Yehua Yang, Zhongkai Sun","submitted_at":"2026-06-17T06:22:28Z","abstract_excerpt":"Realistic coding-agent benchmarks often replay public GitHub issues and pull requests, making them vulnerable to overlap with model pretraining, fine-tuning, synthetic-data generation, or benchmark-driven model selection. Fully synthetic tasks avoid direct historical replay, but can drift away from real repository needs. We propose SWE-Future, a forecast-conditioned data synthesis method for future-oriented coding tasks. Given a forecast snapshot at time $T_0$, the method uses only pre-$T_0$ repository evidence to forecast future feature implementation/enhancement, bugfix, and refactor task fa"},"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":"2606.18733","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-06-17T06:22:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"53c466e248e3deb02c889e79f9dc470b5f4014ca87b15b58957ee3620b54706a","abstract_canon_sha256":"66b99c8994e34fb3f6b044c1b8ddeedab83b23744f81112a9a5a44e06ebf6080"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:11:45.996344Z","signature_b64":"GZUHFe/ArbTEpwM0GC+jSJf4Ikt3EEe3F9QvMDeZtfTJgjzzKr4K1P1hyTSu+rVmbc50XMggEBQHkhI6KbKMDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc501c187b3239891753aa5f060123b341ed351785ee18d507bd5774d0d528be","last_reissued_at":"2026-06-19T16:11:45.995978Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:11:45.995978Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SWE-Future: Forecast-Conditioned Data Synthesis for Future-Oriented Software Engineering Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Hanwen Du, JianYing Qu, Jun Zhang, Qiao Zhao, Yehua Yang, Zhongkai Sun","submitted_at":"2026-06-17T06:22:28Z","abstract_excerpt":"Realistic coding-agent benchmarks often replay public GitHub issues and pull requests, making them vulnerable to overlap with model pretraining, fine-tuning, synthetic-data generation, or benchmark-driven model selection. Fully synthetic tasks avoid direct historical replay, but can drift away from real repository needs. We propose SWE-Future, a forecast-conditioned data synthesis method for future-oriented coding tasks. Given a forecast snapshot at time $T_0$, the method uses only pre-$T_0$ repository evidence to forecast future feature implementation/enhancement, bugfix, and refactor task fa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.18733","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/2606.18733/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":"2606.18733","created_at":"2026-06-19T16:11:45.996040+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.18733v1","created_at":"2026-06-19T16:11:45.996040+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.18733","created_at":"2026-06-19T16:11:45.996040+00:00"},{"alias_kind":"pith_short_12","alias_value":"7RIBYGD3GI4Y","created_at":"2026-06-19T16:11:45.996040+00:00"},{"alias_kind":"pith_short_16","alias_value":"7RIBYGD3GI4YSF2T","created_at":"2026-06-19T16:11:45.996040+00:00"},{"alias_kind":"pith_short_8","alias_value":"7RIBYGD3","created_at":"2026-06-19T16:11:45.996040+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/7RIBYGD3GI4YSF2TVJPQMAJDWN","json":"https://pith.science/pith/7RIBYGD3GI4YSF2TVJPQMAJDWN.json","graph_json":"https://pith.science/api/pith-number/7RIBYGD3GI4YSF2TVJPQMAJDWN/graph.json","events_json":"https://pith.science/api/pith-number/7RIBYGD3GI4YSF2TVJPQMAJDWN/events.json","paper":"https://pith.science/paper/7RIBYGD3"},"agent_actions":{"view_html":"https://pith.science/pith/7RIBYGD3GI4YSF2TVJPQMAJDWN","download_json":"https://pith.science/pith/7RIBYGD3GI4YSF2TVJPQMAJDWN.json","view_paper":"https://pith.science/paper/7RIBYGD3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.18733&json=true","fetch_graph":"https://pith.science/api/pith-number/7RIBYGD3GI4YSF2TVJPQMAJDWN/graph.json","fetch_events":"https://pith.science/api/pith-number/7RIBYGD3GI4YSF2TVJPQMAJDWN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7RIBYGD3GI4YSF2TVJPQMAJDWN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7RIBYGD3GI4YSF2TVJPQMAJDWN/action/storage_attestation","attest_author":"https://pith.science/pith/7RIBYGD3GI4YSF2TVJPQMAJDWN/action/author_attestation","sign_citation":"https://pith.science/pith/7RIBYGD3GI4YSF2TVJPQMAJDWN/action/citation_signature","submit_replication":"https://pith.science/pith/7RIBYGD3GI4YSF2TVJPQMAJDWN/action/replication_record"}},"created_at":"2026-06-19T16:11:45.996040+00:00","updated_at":"2026-06-19T16:11:45.996040+00:00"}