{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MTB6XFE73YFFCACDEMY2ZBFKJK","short_pith_number":"pith:MTB6XFE7","schema_version":"1.0","canonical_sha256":"64c3eb949fde0a5100432331ac84aa4a972144d2da8217081f7bec987e6a5b08","source":{"kind":"arxiv","id":"2605.30227","version":1},"attestation_state":"computed","paper":{"title":"Unifying Temporal and Structural Credit Assignment in LLM-Based Multi-Agent Prompt Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.MA","authors_text":"Bo Jin, Mingze Zhao, Wenhao Li, Wenwu Li, Yuran Song","submitted_at":"2026-05-28T16:57:57Z","abstract_excerpt":"While Multi-Agent Systems (MAS) empower Large Language Models to tackle complex reasoning tasks through collaborative interaction, optimizing their dynamics remains a formidable challenge due to the discrete, non-differentiable nature of the computation graph and the sparsity of global supervisory signals. Existing black-box optimizers struggle to attribute trajectory-level failure to specific local components, resulting in inefficient, high-variance exploration. We argue that tractable MAS optimization needs structural inductive biases to disentangle error signals. We propose temporal and str"},"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":"2605.30227","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2026-05-28T16:57:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"066f2a323353991214128e5389f2ab1d9164263f060bfa432d454b5097dad13a","abstract_canon_sha256":"79f315063a0c32970adb6f414b81dafc1202a88f6782f0567339b6f9c7eac176"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T02:06:13.391445Z","signature_b64":"59xCcpPtI00em7yIOoNnCHmNXSMPjNmrDqExxruRxekJVVxsdS7PBLJwcLDQtOngAs+vJweBfRRzGwx5VuFzCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64c3eb949fde0a5100432331ac84aa4a972144d2da8217081f7bec987e6a5b08","last_reissued_at":"2026-05-29T02:06:13.391088Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T02:06:13.391088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unifying Temporal and Structural Credit Assignment in LLM-Based Multi-Agent Prompt Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.MA","authors_text":"Bo Jin, Mingze Zhao, Wenhao Li, Wenwu Li, Yuran Song","submitted_at":"2026-05-28T16:57:57Z","abstract_excerpt":"While Multi-Agent Systems (MAS) empower Large Language Models to tackle complex reasoning tasks through collaborative interaction, optimizing their dynamics remains a formidable challenge due to the discrete, non-differentiable nature of the computation graph and the sparsity of global supervisory signals. Existing black-box optimizers struggle to attribute trajectory-level failure to specific local components, resulting in inefficient, high-variance exploration. We argue that tractable MAS optimization needs structural inductive biases to disentangle error signals. We propose temporal and str"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30227","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/2605.30227/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":"2605.30227","created_at":"2026-05-29T02:06:13.391149+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.30227v1","created_at":"2026-05-29T02:06:13.391149+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30227","created_at":"2026-05-29T02:06:13.391149+00:00"},{"alias_kind":"pith_short_12","alias_value":"MTB6XFE73YFF","created_at":"2026-05-29T02:06:13.391149+00:00"},{"alias_kind":"pith_short_16","alias_value":"MTB6XFE73YFFCACD","created_at":"2026-05-29T02:06:13.391149+00:00"},{"alias_kind":"pith_short_8","alias_value":"MTB6XFE7","created_at":"2026-05-29T02:06:13.391149+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/MTB6XFE73YFFCACDEMY2ZBFKJK","json":"https://pith.science/pith/MTB6XFE73YFFCACDEMY2ZBFKJK.json","graph_json":"https://pith.science/api/pith-number/MTB6XFE73YFFCACDEMY2ZBFKJK/graph.json","events_json":"https://pith.science/api/pith-number/MTB6XFE73YFFCACDEMY2ZBFKJK/events.json","paper":"https://pith.science/paper/MTB6XFE7"},"agent_actions":{"view_html":"https://pith.science/pith/MTB6XFE73YFFCACDEMY2ZBFKJK","download_json":"https://pith.science/pith/MTB6XFE73YFFCACDEMY2ZBFKJK.json","view_paper":"https://pith.science/paper/MTB6XFE7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.30227&json=true","fetch_graph":"https://pith.science/api/pith-number/MTB6XFE73YFFCACDEMY2ZBFKJK/graph.json","fetch_events":"https://pith.science/api/pith-number/MTB6XFE73YFFCACDEMY2ZBFKJK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MTB6XFE73YFFCACDEMY2ZBFKJK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MTB6XFE73YFFCACDEMY2ZBFKJK/action/storage_attestation","attest_author":"https://pith.science/pith/MTB6XFE73YFFCACDEMY2ZBFKJK/action/author_attestation","sign_citation":"https://pith.science/pith/MTB6XFE73YFFCACDEMY2ZBFKJK/action/citation_signature","submit_replication":"https://pith.science/pith/MTB6XFE73YFFCACDEMY2ZBFKJK/action/replication_record"}},"created_at":"2026-05-29T02:06:13.391149+00:00","updated_at":"2026-05-29T02:06:13.391149+00:00"}