{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2AKC3TY65JNUPCNANWZJS3NQIY","short_pith_number":"pith:2AKC3TY6","schema_version":"1.0","canonical_sha256":"d0142dcf1eea5b4789a06db2996db0462794cddcd61515731f01980bb9fe0150","source":{"kind":"arxiv","id":"2503.23308","version":1},"attestation_state":"computed","paper":{"title":"Reinforcement Learning for Active Matter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO","physics.bio-ph"],"primary_cat":"cond-mat.soft","authors_text":"Gongyi Wang, Wenjie Cai, Xiang Qu, Yu Zhang, Zihan Huang","submitted_at":"2025-03-30T04:27:17Z","abstract_excerpt":"Active matter refers to systems composed of self-propelled entities that consume energy to produce motion, exhibiting complex non-equilibrium dynamics that challenge traditional models. With the rapid advancements in machine learning, reinforcement learning (RL) has emerged as a promising framework for addressing the complexities of active matter. This review systematically introduces the integration of RL for guiding and controlling active matter systems, focusing on two key aspects: optimal motion strategies for individual active particles and the regulation of collective dynamics in active "},"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":"2503.23308","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.soft","submitted_at":"2025-03-30T04:27:17Z","cross_cats_sorted":["cs.LG","cs.RO","physics.bio-ph"],"title_canon_sha256":"b09243f396768189a9b10275be322c90328da1762f33f251b278094c84cd42c2","abstract_canon_sha256":"5c46399d1b25a5b48c0cdda6f5b76aa8cd86c69a3514858398b6ca5157e9b9d7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:46.981648Z","signature_b64":"GkUxEE0d1+1Z4RlkvN9cbISffsFnVVjZGdf0eP6v4fTbqdErrILmXiVfAovPiASulIRs/PrUjG0GPTp+UN0YCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0142dcf1eea5b4789a06db2996db0462794cddcd61515731f01980bb9fe0150","last_reissued_at":"2026-07-05T12:03:46.981068Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:46.981068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reinforcement Learning for Active Matter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO","physics.bio-ph"],"primary_cat":"cond-mat.soft","authors_text":"Gongyi Wang, Wenjie Cai, Xiang Qu, Yu Zhang, Zihan Huang","submitted_at":"2025-03-30T04:27:17Z","abstract_excerpt":"Active matter refers to systems composed of self-propelled entities that consume energy to produce motion, exhibiting complex non-equilibrium dynamics that challenge traditional models. With the rapid advancements in machine learning, reinforcement learning (RL) has emerged as a promising framework for addressing the complexities of active matter. This review systematically introduces the integration of RL for guiding and controlling active matter systems, focusing on two key aspects: optimal motion strategies for individual active particles and the regulation of collective dynamics in active "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.23308","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/2503.23308/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":"2503.23308","created_at":"2026-07-05T12:03:46.981151+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.23308v1","created_at":"2026-07-05T12:03:46.981151+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.23308","created_at":"2026-07-05T12:03:46.981151+00:00"},{"alias_kind":"pith_short_12","alias_value":"2AKC3TY65JNU","created_at":"2026-07-05T12:03:46.981151+00:00"},{"alias_kind":"pith_short_16","alias_value":"2AKC3TY65JNUPCNA","created_at":"2026-07-05T12:03:46.981151+00:00"},{"alias_kind":"pith_short_8","alias_value":"2AKC3TY6","created_at":"2026-07-05T12:03:46.981151+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.17709","citing_title":"Ant swarm functional control via stigmergic Reinforcement Learning agents","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2AKC3TY65JNUPCNANWZJS3NQIY","json":"https://pith.science/pith/2AKC3TY65JNUPCNANWZJS3NQIY.json","graph_json":"https://pith.science/api/pith-number/2AKC3TY65JNUPCNANWZJS3NQIY/graph.json","events_json":"https://pith.science/api/pith-number/2AKC3TY65JNUPCNANWZJS3NQIY/events.json","paper":"https://pith.science/paper/2AKC3TY6"},"agent_actions":{"view_html":"https://pith.science/pith/2AKC3TY65JNUPCNANWZJS3NQIY","download_json":"https://pith.science/pith/2AKC3TY65JNUPCNANWZJS3NQIY.json","view_paper":"https://pith.science/paper/2AKC3TY6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.23308&json=true","fetch_graph":"https://pith.science/api/pith-number/2AKC3TY65JNUPCNANWZJS3NQIY/graph.json","fetch_events":"https://pith.science/api/pith-number/2AKC3TY65JNUPCNANWZJS3NQIY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2AKC3TY65JNUPCNANWZJS3NQIY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2AKC3TY65JNUPCNANWZJS3NQIY/action/storage_attestation","attest_author":"https://pith.science/pith/2AKC3TY65JNUPCNANWZJS3NQIY/action/author_attestation","sign_citation":"https://pith.science/pith/2AKC3TY65JNUPCNANWZJS3NQIY/action/citation_signature","submit_replication":"https://pith.science/pith/2AKC3TY65JNUPCNANWZJS3NQIY/action/replication_record"}},"created_at":"2026-07-05T12:03:46.981151+00:00","updated_at":"2026-07-05T12:03:46.981151+00:00"}