{"_canonicalization":{"envelope_id":"axm_ + sha256(envelope minus {signature, axiom_id, anchors})","envelope_signature":"ed25519(envelope minus {signature, axiom_id})","json":"sort_keys=True, separators=(',',':'), ensure_ascii=False, allow_nan=False, utf-8","leaf_hash":"sha256(0x00 || canonical_json(envelope_full))","seal_signature":"ed25519(seal minus {signature, sig_algorithm})"},"axiom_id":"axm_69644068a45bcf8dd4761f78eba2cae802d0cb6f859e871d9665febd6ba956f2","bitcoin_anchor":{"bitcoin_attestations":[],"calendar_attestations":[],"ots_url":"","stamped_at":"","status":"pending_next_stamp"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_69644068a45bcf8dd4761f78eba2cae802d0cb6f859e871d9665febd6ba956f2","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"74149d909efa75736990e14b9b2691790013dc89dce3e009aa8cb65614b246f6","published":"Mon, 13 Jul 2026 00:00:00 -0400","receipt_hash":"74149d909efa75736990e14b9b2691790013dc89dce3e009aa8cb65614b246f6","schema":"spider.news.vendor_press.v1","spider":"vendor_press","spider_record":{"axiom_subtype":"news.vendor_press.v1","category":"news","decision_hint":"POSITIVE","envelope_target":"AX.OBS","fingerprint":"74149d909efa75736990e14b9b2691790013dc89dce3e009aa8cb65614b246f6","observed_at":"2026-07-13T04:43:08.394955Z","parent_run_hash":"900c1c934245e788564e199a9619f2dc36ec91d9804ddd9c6a40fb42c8a1e1c0","published":"Mon, 13 Jul 2026 00:00:00 -0400","runtime_version":"0.1.0","schema":"spider.news.vendor_press.v1","source_status":200,"source_url":"https://export.arxiv.org/rss/cs.AI","spider":"vendor_press","summary_excerpt":"arXiv:2607.09481v1 Announce Type: cross \nAbstract: Text-guided medical image segmentation leverages clinical semantics to improve lesion delineation, yet many existing models bind cross-modal fusion, supervision, and decoder design into a task-specific architecture. Such tight coupling makes it difficult to reuse language guidance modules across heterogeneous vision and text backbones, and often requires redesigning the network when the encoder pair changes. This paper presents BTHA, a backbone-transferable hierarchical adapter framework for text-guided medical image segmentation. BTHA is built around a stable feature-level interface: given multi-scale visual features and a text representation, it injects semantic guidance through shape-preserving adapters while maintaining the decoder-side tensor contract. To make this interface effective, we introduce a Hierarchical Coarse-to-Fine Supervision Strategy that decomposes learning into global image-text alignment, multi-scale auxiliary lo","title":"Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation","url":"https://arxiv.org/abs/2607.09481","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.09481v1 Announce Type: cross \nAbstract: Text-guided medical image segmentation leverages clinical semantics to improve lesion delineation, yet many existing models bind cross-modal fusion, supervision, and decoder design into a task-specific architecture. Such tight coupling makes it difficult to reuse language guidance modules across heterogeneous vision and text backbones, and often requires redesigning the network when the encoder pair changes. This paper presents BTHA, a backbone-transferable hierarchical adapter framework for text-guided medical image segmentation. BTHA is built around a stable feature-level interface: given multi-scale visual features and a text representation, it injects semantic guidance through shape-preserving adapters while maintaining the decoder-side tensor contract. To make this interface effective, we introduce a Hierarchical Coarse-to-Fine Supervision Strategy that decomposes learning into global image-text alignment, multi-scale auxiliary lo","title":"Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-07-13T04:43:08Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2607.09481"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:8c798004d05ee7566d9637fe983f486dc803bb7afac0d99b758a5f71b5e36a0a48919f0a6d8aac51bdd2d15530a492b3f9d5c40756943457bb1cc8e113734b08","signer":"crovia.substrate","subject":{"observed_at":"2026-07-13T04:43:08Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2607.09481"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"ab7e6ce2a4e1b7ddadc1b5022429cfcff9b1655e8cad12f382dcef973d8c3961","leaf_index":309668,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"a5a3a908da383ac27b42f9610f12f3825c6a81197cac007b756a94e80c98313d","side":"right"},{"sibling":"e31266cd64d31ad445849e9b9b017aa177d057ab1ae06b1603c8e5a1a8d27f1f","side":"right"},{"sibling":"2d0a552102cbe3e1a6f3051e7d911d1b2a12f442ffc3a1aa060475d899fc920b","side":"left"},{"sibling":"cb1cfbda5600884ac1e6a4bc0d2cea36dece7c230a0d124d00d850cefe049f4e","side":"right"},{"sibling":"ade4794d173c42e632d7e185c354b26ce0b5511a29667e60a600752aa4e1b258","side":"right"},{"sibling":"817e14c6ab3a66a77efac57ff4cc63efe5efe647afce47f5aefa03dc77fc1f24","side":"left"},{"sibling":"a06f2db4ff89b8806c463205a6d957aa0950263fff991c2aa1386688c611457f","side":"right"},{"sibling":"49f7764de5dea797b32547b8437f6d371cde9fb33ac6cf784651dcfa196b149a","side":"left"},{"sibling":"6cbb0c4695e74fa8ec17dfde89fa56c1958a1d4dffbbcbe3556da4a69c699ebc","side":"left"},{"sibling":"c4bde3283be97905033d39c1097ac73b82f180de8830384fe6f9f1933f7fa4b9","side":"right"},{"sibling":"3b5f968ebea87e7be458ef7e63c6637988d27ba6d170e1f3794d385cd76ca23f","side":"right"},{"sibling":"5ed534e945b31085c140b50460415e7960b76a4b6266da67d272b853dc94b352","side":"left"},{"sibling":"91010b271bc8eb5253b3549292ef3146e1d85bc9bac7ca36e0862f1204f84e8d","side":"left"},{"sibling":"772fbc112e94d8e574379343387c65503d3cf8fb16ff89090174eccced871a41","side":"left"},{"sibling":"5d50450cae1a230f682b390c8e28ae822ec6c0c04a9bc79b0d27af97306ccccd","side":"right"},{"sibling":"d8b9143917b539c543cf4448cec00131f8b807bd8004979c54ebe09798748c66","side":"left"},{"sibling":"e9ac4b1e71d3f572751b7984e9ca00893d71628137b4634e82df02cf3db9ab68","side":"right"},{"sibling":"99ff86058e045249bf936a629308be31e4cc71328f0282c4a924f4e6718be5f0","side":"right"},{"sibling":"1cecb7f447febd025aac272837c80de218aecc6485d2395a509b2a1f1b9c746e","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":309862,"merkle_root":"f18a76abb66e7cb448986b6541091416ed4ebdde8124f5208a7c7c94bb4165d1","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260713T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-07-13T05:38:23Z","sig_algorithm":"ed25519","signature":"0488e3527b1d559ba55116220f91e2f6c22bb5358a135e8a9b94a65450b50511702044fa993555662ebca09f005b277f5f6ad0d044ec96a5568e9993c09ef007","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_69644068a45bcf8dd4761f78eba2cae802d0cb6f859e871d9665febd6ba956f2"}}