{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SFTMJJTY2FK4IYMAMUZSC2AEEO","short_pith_number":"pith:SFTMJJTY","schema_version":"1.0","canonical_sha256":"9166c4a678d155c46180653321680423996a3d2294630d6bc05a5f58a8109b96","source":{"kind":"arxiv","id":"2607.00544","version":1},"attestation_state":"computed","paper":{"title":"GEAR-Seg: A Grounded Explainable Agent for Reasoning Segmentation and Data Engine","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Wen Li, Yanan Wang, Yibin Ying, Zhenghao Fei","submitted_at":"2026-07-01T07:35:12Z","abstract_excerpt":"Reasoning segmentation requires localizing targets based on complex, implicit queries. Current end-to-end models typically entangle perception and deduction into an opaque black box, severely limiting interpretability and scalability. To address this, we propose GEAR-Seg (Grounded Explainable Agent for Reasoning Segmentation), an explicitly decoupled agent that shifts the paradigm by translating visual pixels into dense, attribute-rich text. By decoupling class-agnostic segmentation, semantic description, and Large Language Model (LLM) deduction, GEAR-Seg transforms implicit reasoning into an "},"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":"2607.00544","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-01T07:35:12Z","cross_cats_sorted":[],"title_canon_sha256":"c2a2b2111301654a55d7f978d385dae1c4691bdb8d57cffd8d5452c0c2f2096d","abstract_canon_sha256":"c1c60728d30c11824155e232ee463586d98d5d1155f557554937a64c86e03371"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:17:47.278666Z","signature_b64":"yrvWq44HIEbfsWxuVQAA7VMLEbhmrc6YA7pX5tIG5WXsN+r5uPifJ1OZOqwIHJi0mbpFD0oN8VhkGJzSwe/bCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9166c4a678d155c46180653321680423996a3d2294630d6bc05a5f58a8109b96","last_reissued_at":"2026-07-02T01:17:47.278254Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:17:47.278254Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GEAR-Seg: A Grounded Explainable Agent for Reasoning Segmentation and Data Engine","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Wen Li, Yanan Wang, Yibin Ying, Zhenghao Fei","submitted_at":"2026-07-01T07:35:12Z","abstract_excerpt":"Reasoning segmentation requires localizing targets based on complex, implicit queries. Current end-to-end models typically entangle perception and deduction into an opaque black box, severely limiting interpretability and scalability. To address this, we propose GEAR-Seg (Grounded Explainable Agent for Reasoning Segmentation), an explicitly decoupled agent that shifts the paradigm by translating visual pixels into dense, attribute-rich text. By decoupling class-agnostic segmentation, semantic description, and Large Language Model (LLM) deduction, GEAR-Seg transforms implicit reasoning into an "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00544","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/2607.00544/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":"2607.00544","created_at":"2026-07-02T01:17:47.278310+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.00544v1","created_at":"2026-07-02T01:17:47.278310+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00544","created_at":"2026-07-02T01:17:47.278310+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFTMJJTY2FK4","created_at":"2026-07-02T01:17:47.278310+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFTMJJTY2FK4IYMA","created_at":"2026-07-02T01:17:47.278310+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFTMJJTY","created_at":"2026-07-02T01:17:47.278310+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/SFTMJJTY2FK4IYMAMUZSC2AEEO","json":"https://pith.science/pith/SFTMJJTY2FK4IYMAMUZSC2AEEO.json","graph_json":"https://pith.science/api/pith-number/SFTMJJTY2FK4IYMAMUZSC2AEEO/graph.json","events_json":"https://pith.science/api/pith-number/SFTMJJTY2FK4IYMAMUZSC2AEEO/events.json","paper":"https://pith.science/paper/SFTMJJTY"},"agent_actions":{"view_html":"https://pith.science/pith/SFTMJJTY2FK4IYMAMUZSC2AEEO","download_json":"https://pith.science/pith/SFTMJJTY2FK4IYMAMUZSC2AEEO.json","view_paper":"https://pith.science/paper/SFTMJJTY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.00544&json=true","fetch_graph":"https://pith.science/api/pith-number/SFTMJJTY2FK4IYMAMUZSC2AEEO/graph.json","fetch_events":"https://pith.science/api/pith-number/SFTMJJTY2FK4IYMAMUZSC2AEEO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFTMJJTY2FK4IYMAMUZSC2AEEO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFTMJJTY2FK4IYMAMUZSC2AEEO/action/storage_attestation","attest_author":"https://pith.science/pith/SFTMJJTY2FK4IYMAMUZSC2AEEO/action/author_attestation","sign_citation":"https://pith.science/pith/SFTMJJTY2FK4IYMAMUZSC2AEEO/action/citation_signature","submit_replication":"https://pith.science/pith/SFTMJJTY2FK4IYMAMUZSC2AEEO/action/replication_record"}},"created_at":"2026-07-02T01:17:47.278310+00:00","updated_at":"2026-07-02T01:17:47.278310+00:00"}