{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:GFFAOKNPXTN3WSH6O3DZXH3QMP","short_pith_number":"pith:GFFAOKNP","schema_version":"1.0","canonical_sha256":"314a0729afbcdbbb48fe76c79b9f7063c11039c5e04d30d7eededdb2f16a6ab2","source":{"kind":"arxiv","id":"2606.04691","version":1},"attestation_state":"computed","paper":{"title":"SMADE-IE: Sparse Multi-Agent Framework with Evidence-Driven Debate for Zero-Shot Information Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Kenfeng Huang, Li Yuan, Xin Wu, Yi Cai, Zikun Deng","submitted_at":"2026-06-03T10:18:34Z","abstract_excerpt":"Zero-shot information extraction (IE) with large language models (LLMs) has attracted increasing attention due to its flexibility in adapting to new schemas and domains without task-specific training. Existing approaches mainly rely on monolithic prompting, each-type prompting, or multi-agent debate. However, monolithic prompting often suffers from boundary and type errors, while each-type prompting and multi-agent debate introduce cross-type conflicts, redundant agent interactions, and substantial token overhead. To address these challenges, we propose SMADE-IE, a sparse and evidence-driven m"},"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":"2606.04691","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-06-03T10:18:34Z","cross_cats_sorted":[],"title_canon_sha256":"fcbeecefcfc63c2313bbc4d30cab5fbaa9efca07af8520b591ab23f6d2bba3a3","abstract_canon_sha256":"e8d97ece4078f4ed0bebe568205fa8c2103ac5562c8a8da3839ff15763496daa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:09:25.179859Z","signature_b64":"ZWUCDWEtfY6r5wo4UDi/IoaYex8kgRUXLV2wCbF794dMXYyNb7SUhfEz63RmImurzYDT02r7vjo8JPUWxRFZAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"314a0729afbcdbbb48fe76c79b9f7063c11039c5e04d30d7eededdb2f16a6ab2","last_reissued_at":"2026-06-04T01:09:25.179063Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:09:25.179063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SMADE-IE: Sparse Multi-Agent Framework with Evidence-Driven Debate for Zero-Shot Information Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Kenfeng Huang, Li Yuan, Xin Wu, Yi Cai, Zikun Deng","submitted_at":"2026-06-03T10:18:34Z","abstract_excerpt":"Zero-shot information extraction (IE) with large language models (LLMs) has attracted increasing attention due to its flexibility in adapting to new schemas and domains without task-specific training. Existing approaches mainly rely on monolithic prompting, each-type prompting, or multi-agent debate. However, monolithic prompting often suffers from boundary and type errors, while each-type prompting and multi-agent debate introduce cross-type conflicts, redundant agent interactions, and substantial token overhead. To address these challenges, we propose SMADE-IE, a sparse and evidence-driven m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04691","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/2606.04691/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":"2606.04691","created_at":"2026-06-04T01:09:25.179180+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.04691v1","created_at":"2026-06-04T01:09:25.179180+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04691","created_at":"2026-06-04T01:09:25.179180+00:00"},{"alias_kind":"pith_short_12","alias_value":"GFFAOKNPXTN3","created_at":"2026-06-04T01:09:25.179180+00:00"},{"alias_kind":"pith_short_16","alias_value":"GFFAOKNPXTN3WSH6","created_at":"2026-06-04T01:09:25.179180+00:00"},{"alias_kind":"pith_short_8","alias_value":"GFFAOKNP","created_at":"2026-06-04T01:09:25.179180+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/GFFAOKNPXTN3WSH6O3DZXH3QMP","json":"https://pith.science/pith/GFFAOKNPXTN3WSH6O3DZXH3QMP.json","graph_json":"https://pith.science/api/pith-number/GFFAOKNPXTN3WSH6O3DZXH3QMP/graph.json","events_json":"https://pith.science/api/pith-number/GFFAOKNPXTN3WSH6O3DZXH3QMP/events.json","paper":"https://pith.science/paper/GFFAOKNP"},"agent_actions":{"view_html":"https://pith.science/pith/GFFAOKNPXTN3WSH6O3DZXH3QMP","download_json":"https://pith.science/pith/GFFAOKNPXTN3WSH6O3DZXH3QMP.json","view_paper":"https://pith.science/paper/GFFAOKNP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.04691&json=true","fetch_graph":"https://pith.science/api/pith-number/GFFAOKNPXTN3WSH6O3DZXH3QMP/graph.json","fetch_events":"https://pith.science/api/pith-number/GFFAOKNPXTN3WSH6O3DZXH3QMP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GFFAOKNPXTN3WSH6O3DZXH3QMP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GFFAOKNPXTN3WSH6O3DZXH3QMP/action/storage_attestation","attest_author":"https://pith.science/pith/GFFAOKNPXTN3WSH6O3DZXH3QMP/action/author_attestation","sign_citation":"https://pith.science/pith/GFFAOKNPXTN3WSH6O3DZXH3QMP/action/citation_signature","submit_replication":"https://pith.science/pith/GFFAOKNPXTN3WSH6O3DZXH3QMP/action/replication_record"}},"created_at":"2026-06-04T01:09:25.179180+00:00","updated_at":"2026-06-04T01:09:25.179180+00:00"}