{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AL7NS336JP5MITTFW2BJRUBN6Q","short_pith_number":"pith:AL7NS336","schema_version":"1.0","canonical_sha256":"02fed96f7e4bfac44e65b68298d02df43a255001e6b9ebf09a904aaf43e90961","source":{"kind":"arxiv","id":"2508.20019","version":1},"attestation_state":"computed","paper":{"title":"Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.MA"],"primary_cat":"cs.LG","authors_text":"Bill Shi, Eric Yang, Ji Wang, Kashing Chen, Ke Zhang, Lynn Ai, Xinyuan Song","submitted_at":"2025-08-27T16:27:57Z","abstract_excerpt":"Most existing Large Language Model (LLM)-based agent frameworks rely on centralized orchestration, incurring high deployment costs, rigid communication topologies, and limited adaptability. To address these challenges, we introduce Symphony, a decentralized multi-agent system which enables lightweight LLMs on consumer-grade GPUs to coordinate. Symphony introduces three key mechanisms: (1) a decentralized ledger that records capabilities, (2) a Beacon-selection protocol for dynamic task allocation, and (3) weighted result voting based on CoTs. This design forms a privacy-saving, scalable, and f"},"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":"2508.20019","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T16:27:57Z","cross_cats_sorted":["cs.AI","cs.CL","cs.MA"],"title_canon_sha256":"3c89a8477c463edd8b7421ac94d4a5cfdb996930406bb912500083e17e0add3b","abstract_canon_sha256":"5dc111402ad8c3b298d7e165f03ffea3ba989d85b3e2ade9f52e97a2174e37f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:22.318831Z","signature_b64":"vCGMYxPz5GeQttA5CX6HRKHit6r4aal9UaL56zrXtO9m/bQ64L52UdNGhKhmWy2TKAVn7wNCYknraW+/++YEDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02fed96f7e4bfac44e65b68298d02df43a255001e6b9ebf09a904aaf43e90961","last_reissued_at":"2026-07-05T12:00:22.318231Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:22.318231Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.MA"],"primary_cat":"cs.LG","authors_text":"Bill Shi, Eric Yang, Ji Wang, Kashing Chen, Ke Zhang, Lynn Ai, Xinyuan Song","submitted_at":"2025-08-27T16:27:57Z","abstract_excerpt":"Most existing Large Language Model (LLM)-based agent frameworks rely on centralized orchestration, incurring high deployment costs, rigid communication topologies, and limited adaptability. To address these challenges, we introduce Symphony, a decentralized multi-agent system which enables lightweight LLMs on consumer-grade GPUs to coordinate. Symphony introduces three key mechanisms: (1) a decentralized ledger that records capabilities, (2) a Beacon-selection protocol for dynamic task allocation, and (3) weighted result voting based on CoTs. This design forms a privacy-saving, scalable, and f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.20019","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/2508.20019/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":"2508.20019","created_at":"2026-07-05T12:00:22.318295+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.20019v1","created_at":"2026-07-05T12:00:22.318295+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.20019","created_at":"2026-07-05T12:00:22.318295+00:00"},{"alias_kind":"pith_short_12","alias_value":"AL7NS336JP5M","created_at":"2026-07-05T12:00:22.318295+00:00"},{"alias_kind":"pith_short_16","alias_value":"AL7NS336JP5MITTF","created_at":"2026-07-05T12:00:22.318295+00:00"},{"alias_kind":"pith_short_8","alias_value":"AL7NS336","created_at":"2026-07-05T12:00:22.318295+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.10718","citing_title":"An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14892","citing_title":"Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems","ref_index":257,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14892","citing_title":"Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems","ref_index":256,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10718","citing_title":"An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AL7NS336JP5MITTFW2BJRUBN6Q","json":"https://pith.science/pith/AL7NS336JP5MITTFW2BJRUBN6Q.json","graph_json":"https://pith.science/api/pith-number/AL7NS336JP5MITTFW2BJRUBN6Q/graph.json","events_json":"https://pith.science/api/pith-number/AL7NS336JP5MITTFW2BJRUBN6Q/events.json","paper":"https://pith.science/paper/AL7NS336"},"agent_actions":{"view_html":"https://pith.science/pith/AL7NS336JP5MITTFW2BJRUBN6Q","download_json":"https://pith.science/pith/AL7NS336JP5MITTFW2BJRUBN6Q.json","view_paper":"https://pith.science/paper/AL7NS336","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.20019&json=true","fetch_graph":"https://pith.science/api/pith-number/AL7NS336JP5MITTFW2BJRUBN6Q/graph.json","fetch_events":"https://pith.science/api/pith-number/AL7NS336JP5MITTFW2BJRUBN6Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AL7NS336JP5MITTFW2BJRUBN6Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AL7NS336JP5MITTFW2BJRUBN6Q/action/storage_attestation","attest_author":"https://pith.science/pith/AL7NS336JP5MITTFW2BJRUBN6Q/action/author_attestation","sign_citation":"https://pith.science/pith/AL7NS336JP5MITTFW2BJRUBN6Q/action/citation_signature","submit_replication":"https://pith.science/pith/AL7NS336JP5MITTFW2BJRUBN6Q/action/replication_record"}},"created_at":"2026-07-05T12:00:22.318295+00:00","updated_at":"2026-07-05T12:00:22.318295+00:00"}