{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:GRQFM34DKQUYASKBJMFTGWTAAV","short_pith_number":"pith:GRQFM34D","schema_version":"1.0","canonical_sha256":"3460566f8354298049414b0b335a600576a7a5c1e05e986c2f2ba5a6b227fa25","source":{"kind":"arxiv","id":"2606.01828","version":1},"attestation_state":"computed","paper":{"title":"Dynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.MA","authors_text":"Wanshuang Gou, Zihan Liu","submitted_at":"2026-06-01T07:42:33Z","abstract_excerpt":"Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specialization, and cross-validation. However, existing multi-agent debate and collaboration frameworks typically adopt fully connected communication, causing the number of messages, token costs, and end-to-end latency to grow approximately quadratically with the number of agents; although fixed sparse topologies reduce overhead, they cannot adapt communication relationships to different task instances or intermediate reasoning states, making them prone eith"},"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":"2606.01828","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2026-06-01T07:42:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"95ea93ac6383aaea139ffdfe7eff7009a5134b37107116dd55e5de47d3352ebb","abstract_canon_sha256":"0e7427459a169380d7482d5e5a860d5e0f9b71e1e4b85636eb23b7441c9c13e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:57.963232Z","signature_b64":"/wMIiIQZQRp5EcrJmcphYxqux/Bttx6zAyreGb9aTGh7c8VpdeCHgzJx+fH4RJr+Mjo/4kCn13tRZEvyn/FLDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3460566f8354298049414b0b335a600576a7a5c1e05e986c2f2ba5a6b227fa25","last_reissued_at":"2026-06-02T02:04:57.962895Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:57.962895Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.MA","authors_text":"Wanshuang Gou, Zihan Liu","submitted_at":"2026-06-01T07:42:33Z","abstract_excerpt":"Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specialization, and cross-validation. However, existing multi-agent debate and collaboration frameworks typically adopt fully connected communication, causing the number of messages, token costs, and end-to-end latency to grow approximately quadratically with the number of agents; although fixed sparse topologies reduce overhead, they cannot adapt communication relationships to different task instances or intermediate reasoning states, making them prone eith"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01828","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/2606.01828/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":"2606.01828","created_at":"2026-06-02T02:04:57.962952+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.01828v1","created_at":"2026-06-02T02:04:57.962952+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01828","created_at":"2026-06-02T02:04:57.962952+00:00"},{"alias_kind":"pith_short_12","alias_value":"GRQFM34DKQUY","created_at":"2026-06-02T02:04:57.962952+00:00"},{"alias_kind":"pith_short_16","alias_value":"GRQFM34DKQUYASKB","created_at":"2026-06-02T02:04:57.962952+00:00"},{"alias_kind":"pith_short_8","alias_value":"GRQFM34D","created_at":"2026-06-02T02:04:57.962952+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/GRQFM34DKQUYASKBJMFTGWTAAV","json":"https://pith.science/pith/GRQFM34DKQUYASKBJMFTGWTAAV.json","graph_json":"https://pith.science/api/pith-number/GRQFM34DKQUYASKBJMFTGWTAAV/graph.json","events_json":"https://pith.science/api/pith-number/GRQFM34DKQUYASKBJMFTGWTAAV/events.json","paper":"https://pith.science/paper/GRQFM34D"},"agent_actions":{"view_html":"https://pith.science/pith/GRQFM34DKQUYASKBJMFTGWTAAV","download_json":"https://pith.science/pith/GRQFM34DKQUYASKBJMFTGWTAAV.json","view_paper":"https://pith.science/paper/GRQFM34D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.01828&json=true","fetch_graph":"https://pith.science/api/pith-number/GRQFM34DKQUYASKBJMFTGWTAAV/graph.json","fetch_events":"https://pith.science/api/pith-number/GRQFM34DKQUYASKBJMFTGWTAAV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GRQFM34DKQUYASKBJMFTGWTAAV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GRQFM34DKQUYASKBJMFTGWTAAV/action/storage_attestation","attest_author":"https://pith.science/pith/GRQFM34DKQUYASKBJMFTGWTAAV/action/author_attestation","sign_citation":"https://pith.science/pith/GRQFM34DKQUYASKBJMFTGWTAAV/action/citation_signature","submit_replication":"https://pith.science/pith/GRQFM34DKQUYASKBJMFTGWTAAV/action/replication_record"}},"created_at":"2026-06-02T02:04:57.962952+00:00","updated_at":"2026-06-02T02:04:57.962952+00:00"}