{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IZRMHBR6PUYMU2CLAA5EEMBQHC","short_pith_number":"pith:IZRMHBR6","schema_version":"1.0","canonical_sha256":"4662c3863e7d30ca684b003a4230303898b08684627784175bd4a64704f5695a","source":{"kind":"arxiv","id":"2606.11609","version":1},"attestation_state":"computed","paper":{"title":"Multi-Agent Reasoning with Adaptive Worker Allocation for Stance Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Arman Zareian Jahromi, Doina Caragea, Meysam Sabbaghan","submitted_at":"2026-06-10T03:20:55Z","abstract_excerpt":"Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorically framed. Although large language models (LLMs) achieve strong performance on this task, single-pass prompting can be brittle when multiple interpretations are plausible. Existing aggregation strategies, such as majority voting or self-consistency, improve robustness by combining labels, but they discard the intermediate reasoning needed to resolve conflicting interpretations.\n  We introduce a multi-agent reasoning framework with adaptive wor"},"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":"2606.11609","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-10T03:20:55Z","cross_cats_sorted":[],"title_canon_sha256":"e9b35351c33ada04c67d9cdd44b4a62897dabe1cbfbf8504339186fb830b7292","abstract_canon_sha256":"6c50985f869ee6ddd9a1903f355820b82abe6754d272be12c553b7f78ca9e711"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:09:58.870080Z","signature_b64":"1grEO3YGTkBJu0/IpEcP5GaiPna0Guu8zFXYb/XofoS1q8j1x/GxowtLG+veIOIC7cFm6dd2xJJUzz1eicK7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4662c3863e7d30ca684b003a4230303898b08684627784175bd4a64704f5695a","last_reissued_at":"2026-06-11T01:09:58.869223Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:09:58.869223Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Agent Reasoning with Adaptive Worker Allocation for Stance Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Arman Zareian Jahromi, Doina Caragea, Meysam Sabbaghan","submitted_at":"2026-06-10T03:20:55Z","abstract_excerpt":"Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorically framed. Although large language models (LLMs) achieve strong performance on this task, single-pass prompting can be brittle when multiple interpretations are plausible. Existing aggregation strategies, such as majority voting or self-consistency, improve robustness by combining labels, but they discard the intermediate reasoning needed to resolve conflicting interpretations.\n  We introduce a multi-agent reasoning framework with adaptive wor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11609","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/2606.11609/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":"2606.11609","created_at":"2026-06-11T01:09:58.869347+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.11609v1","created_at":"2026-06-11T01:09:58.869347+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11609","created_at":"2026-06-11T01:09:58.869347+00:00"},{"alias_kind":"pith_short_12","alias_value":"IZRMHBR6PUYM","created_at":"2026-06-11T01:09:58.869347+00:00"},{"alias_kind":"pith_short_16","alias_value":"IZRMHBR6PUYMU2CL","created_at":"2026-06-11T01:09:58.869347+00:00"},{"alias_kind":"pith_short_8","alias_value":"IZRMHBR6","created_at":"2026-06-11T01:09:58.869347+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/IZRMHBR6PUYMU2CLAA5EEMBQHC","json":"https://pith.science/pith/IZRMHBR6PUYMU2CLAA5EEMBQHC.json","graph_json":"https://pith.science/api/pith-number/IZRMHBR6PUYMU2CLAA5EEMBQHC/graph.json","events_json":"https://pith.science/api/pith-number/IZRMHBR6PUYMU2CLAA5EEMBQHC/events.json","paper":"https://pith.science/paper/IZRMHBR6"},"agent_actions":{"view_html":"https://pith.science/pith/IZRMHBR6PUYMU2CLAA5EEMBQHC","download_json":"https://pith.science/pith/IZRMHBR6PUYMU2CLAA5EEMBQHC.json","view_paper":"https://pith.science/paper/IZRMHBR6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.11609&json=true","fetch_graph":"https://pith.science/api/pith-number/IZRMHBR6PUYMU2CLAA5EEMBQHC/graph.json","fetch_events":"https://pith.science/api/pith-number/IZRMHBR6PUYMU2CLAA5EEMBQHC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IZRMHBR6PUYMU2CLAA5EEMBQHC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IZRMHBR6PUYMU2CLAA5EEMBQHC/action/storage_attestation","attest_author":"https://pith.science/pith/IZRMHBR6PUYMU2CLAA5EEMBQHC/action/author_attestation","sign_citation":"https://pith.science/pith/IZRMHBR6PUYMU2CLAA5EEMBQHC/action/citation_signature","submit_replication":"https://pith.science/pith/IZRMHBR6PUYMU2CLAA5EEMBQHC/action/replication_record"}},"created_at":"2026-06-11T01:09:58.869347+00:00","updated_at":"2026-06-11T01:09:58.869347+00:00"}