{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:GZHYWA3LFJ6CHBBRRQZCTLO4JS","short_pith_number":"pith:GZHYWA3L","schema_version":"1.0","canonical_sha256":"364f8b036b2a7c2384318c3229addc4c94a2888f79fa63b5f3b8d4264165c7b2","source":{"kind":"arxiv","id":"2605.29163","version":1},"attestation_state":"computed","paper":{"title":"BCER Agent: Reliable Long-Horizon MRI Workflow Execution via Compilation, Artifact Binding, and Bounded Local Recovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Debiao Li, Hsin-Jung Yang, Junzhou Chen, XinQi Li, Yifan Gao, Ziyang Long","submitted_at":"2026-05-27T22:56:19Z","abstract_excerpt":"Many recent medical VLM and agent studies are benchmarked on 2D images or comparatively short tool-calling exchanges, whereas real MRI analysis typically demands long, interdependent pipelines that operate on 3D/4D volumetric data. Under these conditions, reactive tool-calling agents are prone to cascading breakdowns triggered by faulty intermediate references, mismatched tool arguments, and limited control over cross-step dependencies. To address this, we introduce BCER (Brain-Cerebellum-Extremity-Reflector), a controller architecture aimed at dependable long-horizon MRI workflow execution. B"},"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":"2605.29163","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2026-05-27T22:56:19Z","cross_cats_sorted":[],"title_canon_sha256":"ddd2dc2b3d9782c280dec72b09f426406c7303d78ff95425b8eb235bfa15b489","abstract_canon_sha256":"38e45eb043f4d62ad88454e406b2d180d14f1670f37281b8cbd098d293c2b8ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:05:21.902441Z","signature_b64":"fYuloQb06H4YIkuGJ6nue0UBJA/zyCaIyEs+15V56fGeTif7X1A/qrOJufYfNouKScVQYvbDgAGtNWYsf3oBDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"364f8b036b2a7c2384318c3229addc4c94a2888f79fa63b5f3b8d4264165c7b2","last_reissued_at":"2026-05-29T01:05:21.901783Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:05:21.901783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BCER Agent: Reliable Long-Horizon MRI Workflow Execution via Compilation, Artifact Binding, and Bounded Local Recovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Debiao Li, Hsin-Jung Yang, Junzhou Chen, XinQi Li, Yifan Gao, Ziyang Long","submitted_at":"2026-05-27T22:56:19Z","abstract_excerpt":"Many recent medical VLM and agent studies are benchmarked on 2D images or comparatively short tool-calling exchanges, whereas real MRI analysis typically demands long, interdependent pipelines that operate on 3D/4D volumetric data. Under these conditions, reactive tool-calling agents are prone to cascading breakdowns triggered by faulty intermediate references, mismatched tool arguments, and limited control over cross-step dependencies. To address this, we introduce BCER (Brain-Cerebellum-Extremity-Reflector), a controller architecture aimed at dependable long-horizon MRI workflow execution. B"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29163","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/2605.29163/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":"2605.29163","created_at":"2026-05-29T01:05:21.901898+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.29163v1","created_at":"2026-05-29T01:05:21.901898+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29163","created_at":"2026-05-29T01:05:21.901898+00:00"},{"alias_kind":"pith_short_12","alias_value":"GZHYWA3LFJ6C","created_at":"2026-05-29T01:05:21.901898+00:00"},{"alias_kind":"pith_short_16","alias_value":"GZHYWA3LFJ6CHBBR","created_at":"2026-05-29T01:05:21.901898+00:00"},{"alias_kind":"pith_short_8","alias_value":"GZHYWA3L","created_at":"2026-05-29T01:05:21.901898+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/GZHYWA3LFJ6CHBBRRQZCTLO4JS","json":"https://pith.science/pith/GZHYWA3LFJ6CHBBRRQZCTLO4JS.json","graph_json":"https://pith.science/api/pith-number/GZHYWA3LFJ6CHBBRRQZCTLO4JS/graph.json","events_json":"https://pith.science/api/pith-number/GZHYWA3LFJ6CHBBRRQZCTLO4JS/events.json","paper":"https://pith.science/paper/GZHYWA3L"},"agent_actions":{"view_html":"https://pith.science/pith/GZHYWA3LFJ6CHBBRRQZCTLO4JS","download_json":"https://pith.science/pith/GZHYWA3LFJ6CHBBRRQZCTLO4JS.json","view_paper":"https://pith.science/paper/GZHYWA3L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.29163&json=true","fetch_graph":"https://pith.science/api/pith-number/GZHYWA3LFJ6CHBBRRQZCTLO4JS/graph.json","fetch_events":"https://pith.science/api/pith-number/GZHYWA3LFJ6CHBBRRQZCTLO4JS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GZHYWA3LFJ6CHBBRRQZCTLO4JS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GZHYWA3LFJ6CHBBRRQZCTLO4JS/action/storage_attestation","attest_author":"https://pith.science/pith/GZHYWA3LFJ6CHBBRRQZCTLO4JS/action/author_attestation","sign_citation":"https://pith.science/pith/GZHYWA3LFJ6CHBBRRQZCTLO4JS/action/citation_signature","submit_replication":"https://pith.science/pith/GZHYWA3LFJ6CHBBRRQZCTLO4JS/action/replication_record"}},"created_at":"2026-05-29T01:05:21.901898+00:00","updated_at":"2026-05-29T01:05:21.901898+00:00"}