{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HDL3P5FCRCKJLUU2SRFQCGPOXS","short_pith_number":"pith:HDL3P5FC","schema_version":"1.0","canonical_sha256":"38d7b7f4a2889495d29a944b0119eebc96dd55a848dbb25cc21bac12136e821e","source":{"kind":"arxiv","id":"2511.02200","version":2},"attestation_state":"computed","paper":{"title":"Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Haochun Wang, Jingbo Wang, Sendong Zhao, Ting Liu, Yuzheng Fan","submitted_at":"2025-11-04T02:41:14Z","abstract_excerpt":"The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse agents to integrate unique expertise, collaborate flexibly, and address challenges unattainable for individual models. However, the full potential of such systems is hindered by rigid agent scheduling and inefficient coordination strategies that fail to adapt to evolving task requirements. In this paper, we propose STRMAC, a state-aware routing framework designed for efficient collaboration in multi-agent systems. Our method separately encodes intera"},"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":"2511.02200","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-11-04T02:41:14Z","cross_cats_sorted":[],"title_canon_sha256":"213f82076d064b0a4763c8a7c2f12e641faa0d3fc30a238c8d67468fd5bc4f29","abstract_canon_sha256":"f737bc274372e3d086a187c75ece02176fd928e91bfffd82ff6fb2890833e760"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:19:41.968778Z","signature_b64":"kn30INnTQqLEOveBijxSgrH7JP/jAJQbGJDh1aphqF84u+xW1T3I50UEAjZ32+/BA0TgWLs48ybVbGxUSQ9UBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38d7b7f4a2889495d29a944b0119eebc96dd55a848dbb25cc21bac12136e821e","last_reissued_at":"2026-07-07T02:19:41.967882Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:19:41.967882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Haochun Wang, Jingbo Wang, Sendong Zhao, Ting Liu, Yuzheng Fan","submitted_at":"2025-11-04T02:41:14Z","abstract_excerpt":"The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse agents to integrate unique expertise, collaborate flexibly, and address challenges unattainable for individual models. However, the full potential of such systems is hindered by rigid agent scheduling and inefficient coordination strategies that fail to adapt to evolving task requirements. In this paper, we propose STRMAC, a state-aware routing framework designed for efficient collaboration in multi-agent systems. Our method separately encodes intera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.02200","kind":"arxiv","version":2},"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/2511.02200/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":"2511.02200","created_at":"2026-07-07T02:19:41.967999+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.02200v2","created_at":"2026-07-07T02:19:41.967999+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.02200","created_at":"2026-07-07T02:19:41.967999+00:00"},{"alias_kind":"pith_short_12","alias_value":"HDL3P5FCRCKJ","created_at":"2026-07-07T02:19:41.967999+00:00"},{"alias_kind":"pith_short_16","alias_value":"HDL3P5FCRCKJLUU2","created_at":"2026-07-07T02:19:41.967999+00:00"},{"alias_kind":"pith_short_8","alias_value":"HDL3P5FC","created_at":"2026-07-07T02:19:41.967999+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/HDL3P5FCRCKJLUU2SRFQCGPOXS","json":"https://pith.science/pith/HDL3P5FCRCKJLUU2SRFQCGPOXS.json","graph_json":"https://pith.science/api/pith-number/HDL3P5FCRCKJLUU2SRFQCGPOXS/graph.json","events_json":"https://pith.science/api/pith-number/HDL3P5FCRCKJLUU2SRFQCGPOXS/events.json","paper":"https://pith.science/paper/HDL3P5FC"},"agent_actions":{"view_html":"https://pith.science/pith/HDL3P5FCRCKJLUU2SRFQCGPOXS","download_json":"https://pith.science/pith/HDL3P5FCRCKJLUU2SRFQCGPOXS.json","view_paper":"https://pith.science/paper/HDL3P5FC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.02200&json=true","fetch_graph":"https://pith.science/api/pith-number/HDL3P5FCRCKJLUU2SRFQCGPOXS/graph.json","fetch_events":"https://pith.science/api/pith-number/HDL3P5FCRCKJLUU2SRFQCGPOXS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HDL3P5FCRCKJLUU2SRFQCGPOXS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HDL3P5FCRCKJLUU2SRFQCGPOXS/action/storage_attestation","attest_author":"https://pith.science/pith/HDL3P5FCRCKJLUU2SRFQCGPOXS/action/author_attestation","sign_citation":"https://pith.science/pith/HDL3P5FCRCKJLUU2SRFQCGPOXS/action/citation_signature","submit_replication":"https://pith.science/pith/HDL3P5FCRCKJLUU2SRFQCGPOXS/action/replication_record"}},"created_at":"2026-07-07T02:19:41.967999+00:00","updated_at":"2026-07-07T02:19:41.967999+00:00"}