{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WGSVPVIXLFSU3N4HQ66DAASTCL","short_pith_number":"pith:WGSVPVIX","schema_version":"1.0","canonical_sha256":"b1a557d51759654db78787bc30025312ce7d3950560ba61b53b7b9bc9fb090d9","source":{"kind":"arxiv","id":"2508.02988","version":1},"attestation_state":"computed","paper":{"title":"GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Linji Wang, Peter Stone, Xuesu Xiao, Zifan Xu","submitted_at":"2025-08-05T01:32:37Z","abstract_excerpt":"Curriculum learning has emerged as a promising approach for training complex robotics tasks, yet current applications predominantly rely on manually designed curricula, which demand significant engineering effort and can suffer from subjective and suboptimal human design choices. While automated curriculum learning has shown success in simple domains like grid worlds and games where task distributions can be easily specified, robotics tasks present unique challenges: they require handling complex task spaces while maintaining relevance to target domain distributions that are only partially kno"},"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":"2508.02988","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-08-05T01:32:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e33b2668c143c4942ecc7291736676e1b2b7075f27f58f9d39aa99f188ab62e7","abstract_canon_sha256":"61b2a6441c5d9c9cfc539a3cc0827dddae636a83271beb8e17f7b477dd0c046d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:34.258082Z","signature_b64":"vKXxrUTj/gOkRwKFQ0+Vh9lzFi+TmmBLp+FuVnTQ6gpqqNz14Kilo+nafvXnWMGZO/caZcWP8FxtKiAsbryGDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1a557d51759654db78787bc30025312ce7d3950560ba61b53b7b9bc9fb090d9","last_reissued_at":"2026-07-05T11:48:34.257469Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:34.257469Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Linji Wang, Peter Stone, Xuesu Xiao, Zifan Xu","submitted_at":"2025-08-05T01:32:37Z","abstract_excerpt":"Curriculum learning has emerged as a promising approach for training complex robotics tasks, yet current applications predominantly rely on manually designed curricula, which demand significant engineering effort and can suffer from subjective and suboptimal human design choices. While automated curriculum learning has shown success in simple domains like grid worlds and games where task distributions can be easily specified, robotics tasks present unique challenges: they require handling complex task spaces while maintaining relevance to target domain distributions that are only partially kno"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.02988","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/2508.02988/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":"2508.02988","created_at":"2026-07-05T11:48:34.257544+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.02988v1","created_at":"2026-07-05T11:48:34.257544+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.02988","created_at":"2026-07-05T11:48:34.257544+00:00"},{"alias_kind":"pith_short_12","alias_value":"WGSVPVIXLFSU","created_at":"2026-07-05T11:48:34.257544+00:00"},{"alias_kind":"pith_short_16","alias_value":"WGSVPVIXLFSU3N4H","created_at":"2026-07-05T11:48:34.257544+00:00"},{"alias_kind":"pith_short_8","alias_value":"WGSVPVIX","created_at":"2026-07-05T11:48:34.257544+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/WGSVPVIXLFSU3N4HQ66DAASTCL","json":"https://pith.science/pith/WGSVPVIXLFSU3N4HQ66DAASTCL.json","graph_json":"https://pith.science/api/pith-number/WGSVPVIXLFSU3N4HQ66DAASTCL/graph.json","events_json":"https://pith.science/api/pith-number/WGSVPVIXLFSU3N4HQ66DAASTCL/events.json","paper":"https://pith.science/paper/WGSVPVIX"},"agent_actions":{"view_html":"https://pith.science/pith/WGSVPVIXLFSU3N4HQ66DAASTCL","download_json":"https://pith.science/pith/WGSVPVIXLFSU3N4HQ66DAASTCL.json","view_paper":"https://pith.science/paper/WGSVPVIX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.02988&json=true","fetch_graph":"https://pith.science/api/pith-number/WGSVPVIXLFSU3N4HQ66DAASTCL/graph.json","fetch_events":"https://pith.science/api/pith-number/WGSVPVIXLFSU3N4HQ66DAASTCL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WGSVPVIXLFSU3N4HQ66DAASTCL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WGSVPVIXLFSU3N4HQ66DAASTCL/action/storage_attestation","attest_author":"https://pith.science/pith/WGSVPVIXLFSU3N4HQ66DAASTCL/action/author_attestation","sign_citation":"https://pith.science/pith/WGSVPVIXLFSU3N4HQ66DAASTCL/action/citation_signature","submit_replication":"https://pith.science/pith/WGSVPVIXLFSU3N4HQ66DAASTCL/action/replication_record"}},"created_at":"2026-07-05T11:48:34.257544+00:00","updated_at":"2026-07-05T11:48:34.257544+00:00"}