{"paper":{"title":"Multi-channel Optical Vision Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.app-ph"],"primary_cat":"physics.optics","authors_text":"Ali Momeni, Guillaume Noetinger, Romain Fleury, Tim Tuuva","submitted_at":"2026-06-08T23:40:15Z","abstract_excerpt":"Spatial multiplexing is one of the natural strengths of optics, yet in optical neural networks, it is often used mainly as parallel throughput. Here, we show that spatial multiplexing in an optical neural network can be used not only to process multiple inputs in parallel, but also to define a trainable representational coordinate of the model. In three implemented scenarios, parallel-input processing, class-code readout and channel-mixed feature interaction, spatial channels act as independent learners, structured code dimensions, and interacting feature groups. The programmable free-space op"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10253","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/2606.10253/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"}