{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VFMDBEOCZ3Y5S5FW27JQKAO6ZY","short_pith_number":"pith:VFMDBEOC","schema_version":"1.0","canonical_sha256":"a9583091c2cef1d974b6d7d30501dece298c82e2c4cddce0dea7fe5cfa63ef71","source":{"kind":"arxiv","id":"2606.00673","version":1},"attestation_state":"computed","paper":{"title":"T-CLIP: Enabling Thermal Perception for Contrastive Language-Image Pretraining","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ayush Maheshwari, Brejesh Lall, Prerana Mukherjee, Tayeba Qazi","submitted_at":"2026-05-30T11:03:58Z","abstract_excerpt":"Thermal imaging offers a powerful alternative to visible-spectrum vision under challenging conditions such as low illumination and adverse weather, yet foundational vision-language models like CLIP fail to align thermal images with textual descriptions due to a fundamental thermal perception gap. We identify three major challenges: the lack of captioned thermal datasets, the inability of standard LLMs to reason about thermal phenomena, and a key representational challenge in thermal imaging where global scene context and object-level heat signatures conflict when learned together in a single e"},"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":"2606.00673","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-30T11:03:58Z","cross_cats_sorted":[],"title_canon_sha256":"1bec748f78c418f2d876a5c73d3832c9312120233cccb52eccfa7ca1dd88957d","abstract_canon_sha256":"ba4bf37f8969335c079761d40ba2fde55dee3c4e8020de7ced983b3bb30d9d76"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:04:02.316519Z","signature_b64":"GvBlaeRxFjlAcOi0TFrVQZjDocEq+AeE80YNuUHdbQVFmYyKMrG/GZ6NWt8BkDa+3ROxRMXBX1rz+HaZj2tfAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9583091c2cef1d974b6d7d30501dece298c82e2c4cddce0dea7fe5cfa63ef71","last_reissued_at":"2026-06-02T01:04:02.316049Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:04:02.316049Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"T-CLIP: Enabling Thermal Perception for Contrastive Language-Image Pretraining","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ayush Maheshwari, Brejesh Lall, Prerana Mukherjee, Tayeba Qazi","submitted_at":"2026-05-30T11:03:58Z","abstract_excerpt":"Thermal imaging offers a powerful alternative to visible-spectrum vision under challenging conditions such as low illumination and adverse weather, yet foundational vision-language models like CLIP fail to align thermal images with textual descriptions due to a fundamental thermal perception gap. We identify three major challenges: the lack of captioned thermal datasets, the inability of standard LLMs to reason about thermal phenomena, and a key representational challenge in thermal imaging where global scene context and object-level heat signatures conflict when learned together in a single e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00673","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/2606.00673/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":"2606.00673","created_at":"2026-06-02T01:04:02.316117+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.00673v1","created_at":"2026-06-02T01:04:02.316117+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00673","created_at":"2026-06-02T01:04:02.316117+00:00"},{"alias_kind":"pith_short_12","alias_value":"VFMDBEOCZ3Y5","created_at":"2026-06-02T01:04:02.316117+00:00"},{"alias_kind":"pith_short_16","alias_value":"VFMDBEOCZ3Y5S5FW","created_at":"2026-06-02T01:04:02.316117+00:00"},{"alias_kind":"pith_short_8","alias_value":"VFMDBEOC","created_at":"2026-06-02T01:04:02.316117+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/VFMDBEOCZ3Y5S5FW27JQKAO6ZY","json":"https://pith.science/pith/VFMDBEOCZ3Y5S5FW27JQKAO6ZY.json","graph_json":"https://pith.science/api/pith-number/VFMDBEOCZ3Y5S5FW27JQKAO6ZY/graph.json","events_json":"https://pith.science/api/pith-number/VFMDBEOCZ3Y5S5FW27JQKAO6ZY/events.json","paper":"https://pith.science/paper/VFMDBEOC"},"agent_actions":{"view_html":"https://pith.science/pith/VFMDBEOCZ3Y5S5FW27JQKAO6ZY","download_json":"https://pith.science/pith/VFMDBEOCZ3Y5S5FW27JQKAO6ZY.json","view_paper":"https://pith.science/paper/VFMDBEOC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.00673&json=true","fetch_graph":"https://pith.science/api/pith-number/VFMDBEOCZ3Y5S5FW27JQKAO6ZY/graph.json","fetch_events":"https://pith.science/api/pith-number/VFMDBEOCZ3Y5S5FW27JQKAO6ZY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VFMDBEOCZ3Y5S5FW27JQKAO6ZY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VFMDBEOCZ3Y5S5FW27JQKAO6ZY/action/storage_attestation","attest_author":"https://pith.science/pith/VFMDBEOCZ3Y5S5FW27JQKAO6ZY/action/author_attestation","sign_citation":"https://pith.science/pith/VFMDBEOCZ3Y5S5FW27JQKAO6ZY/action/citation_signature","submit_replication":"https://pith.science/pith/VFMDBEOCZ3Y5S5FW27JQKAO6ZY/action/replication_record"}},"created_at":"2026-06-02T01:04:02.316117+00:00","updated_at":"2026-06-02T01:04:02.316117+00:00"}