{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:3EFEB6YT7G7SLHPQ2WRNTTEJCU","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"82185c66330a9db98ffca2a8377aeb8a2faae140dcb219db22a414175d2b88f0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T19:49:42Z","title_canon_sha256":"bb9132438c7e385c52625683f46945af34631bde67924ab6b7142048cd2ff6ed"},"schema_version":"1.0","source":{"id":"2506.06499","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06499","created_at":"2026-07-05T11:22:41Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06499v2","created_at":"2026-07-05T11:22:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06499","created_at":"2026-07-05T11:22:41Z"},{"alias_kind":"pith_short_12","alias_value":"3EFEB6YT7G7S","created_at":"2026-07-05T11:22:41Z"},{"alias_kind":"pith_short_16","alias_value":"3EFEB6YT7G7SLHPQ","created_at":"2026-07-05T11:22:41Z"},{"alias_kind":"pith_short_8","alias_value":"3EFEB6YT","created_at":"2026-07-05T11:22:41Z"}],"graph_snapshots":[{"event_id":"sha256:1de2f29bafd5d862aad7cdb2f4747b4b1073f1f2cca6b1990abf6b631dfc4b1a","target":"graph","created_at":"2026-07-05T11:22:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.06499/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language model (LLM) driven synthetic data generation has emerged as a powerful method for improving model reasoning capabilities. However, most methods either distill large state-of-the-art models into small students or use natural ground-truth problem statements to guarantee problem statement quality. This limits the scalability of these approaches to more complex and diverse problem domains. To address this, we present SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms, a novel approach for generating high-quality and diverse synthetic math problem and ","authors_text":"Alex Havrilla, Edward Hughes, Jacob Abernethy, Mikayel Samvelyan","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T19:49:42Z","title":"SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06499","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d532beb8e60ee354a18a758170af0de20afcb82773ae7d4e32c01b5da00309b0","target":"record","created_at":"2026-07-05T11:22:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"82185c66330a9db98ffca2a8377aeb8a2faae140dcb219db22a414175d2b88f0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T19:49:42Z","title_canon_sha256":"bb9132438c7e385c52625683f46945af34631bde67924ab6b7142048cd2ff6ed"},"schema_version":"1.0","source":{"id":"2506.06499","kind":"arxiv","version":2}},"canonical_sha256":"d90a40fb13f9bf259df0d5a2d9cc89153c759cc7e7a22fd47d9268b918440da0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d90a40fb13f9bf259df0d5a2d9cc89153c759cc7e7a22fd47d9268b918440da0","first_computed_at":"2026-07-05T11:22:41.537882Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:22:41.537882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Lx3elEXSHDPIltskBvKHi3+27+uPFjQriXwywzA2KK30/A/kkqNK+QDhEF63YMRsRlqSO+12+VPuRaiAoWg9AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:22:41.538404Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.06499","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d532beb8e60ee354a18a758170af0de20afcb82773ae7d4e32c01b5da00309b0","sha256:1de2f29bafd5d862aad7cdb2f4747b4b1073f1f2cca6b1990abf6b631dfc4b1a"],"state_sha256":"e3f076e4ed4b55d41dee6e060bc61fea944e90c4fda2d7f3a19b3f88a9dd9a4e"}