{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:UMFTMXB75KAGGIDQIROCMUMVTK","short_pith_number":"pith:UMFTMXB7","schema_version":"1.0","canonical_sha256":"a30b365c3fea80632070445c2651959aac46ebd39927b4a13331c3549ba24792","source":{"kind":"arxiv","id":"2607.08839","version":1},"attestation_state":"computed","paper":{"title":"Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Dominick Reilly, Hiromi Wakaki, Qiyu Wu, Srijan Das, Yuki Mistufuji","submitted_at":"2026-07-09T18:00:47Z","abstract_excerpt":"Multimodal Large Language Models (MLLMs) are typically designed under the assumption that all modalities available during training will also be accessible at inference. However, many real-world settings violate this assumption, requiring models to operate under a privileged modality setting, where auxiliary modalities are available only during training. While these modalities contain valuable information, existing MLLMs largely fail to leverage them effectively, as they treat modalities as interchangeable inputs rather than sources of complementary supervision. We propose Mixture of Probes (Mo"},"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":"2607.08839","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-09T18:00:47Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"16b97e0801f6dde4d26afc8e92092dec027bc5b95bb22a48af798c07659409e5","abstract_canon_sha256":"12ecd1eb6a1e69a5a703e042186b52bce8939b25e4c6b9445d44381c00d28801"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-13T00:17:21.211934Z","signature_b64":"Dkfo5o0XrWIkfNg2HR6nOyhPuON9ICLISjE1V3E+8E/t0Z5bhYNvLCQZ1D7tLJRwprkePPZ2olrkQOX4UnKQAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a30b365c3fea80632070445c2651959aac46ebd39927b4a13331c3549ba24792","last_reissued_at":"2026-07-13T00:17:21.210885Z","signature_status":"signed_v1","first_computed_at":"2026-07-13T00:17:21.210885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Dominick Reilly, Hiromi Wakaki, Qiyu Wu, Srijan Das, Yuki Mistufuji","submitted_at":"2026-07-09T18:00:47Z","abstract_excerpt":"Multimodal Large Language Models (MLLMs) are typically designed under the assumption that all modalities available during training will also be accessible at inference. However, many real-world settings violate this assumption, requiring models to operate under a privileged modality setting, where auxiliary modalities are available only during training. While these modalities contain valuable information, existing MLLMs largely fail to leverage them effectively, as they treat modalities as interchangeable inputs rather than sources of complementary supervision. We propose Mixture of Probes (Mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.08839","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/2607.08839/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":"2607.08839","created_at":"2026-07-13T00:17:21.211436+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.08839v1","created_at":"2026-07-13T00:17:21.211436+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.08839","created_at":"2026-07-13T00:17:21.211436+00:00"},{"alias_kind":"pith_short_12","alias_value":"UMFTMXB75KAG","created_at":"2026-07-13T00:17:21.211436+00:00"},{"alias_kind":"pith_short_16","alias_value":"UMFTMXB75KAGGIDQ","created_at":"2026-07-13T00:17:21.211436+00:00"},{"alias_kind":"pith_short_8","alias_value":"UMFTMXB7","created_at":"2026-07-13T00:17:21.211436+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/UMFTMXB75KAGGIDQIROCMUMVTK","json":"https://pith.science/pith/UMFTMXB75KAGGIDQIROCMUMVTK.json","graph_json":"https://pith.science/api/pith-number/UMFTMXB75KAGGIDQIROCMUMVTK/graph.json","events_json":"https://pith.science/api/pith-number/UMFTMXB75KAGGIDQIROCMUMVTK/events.json","paper":"https://pith.science/paper/UMFTMXB7"},"agent_actions":{"view_html":"https://pith.science/pith/UMFTMXB75KAGGIDQIROCMUMVTK","download_json":"https://pith.science/pith/UMFTMXB75KAGGIDQIROCMUMVTK.json","view_paper":"https://pith.science/paper/UMFTMXB7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.08839&json=true","fetch_graph":"https://pith.science/api/pith-number/UMFTMXB75KAGGIDQIROCMUMVTK/graph.json","fetch_events":"https://pith.science/api/pith-number/UMFTMXB75KAGGIDQIROCMUMVTK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UMFTMXB75KAGGIDQIROCMUMVTK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UMFTMXB75KAGGIDQIROCMUMVTK/action/storage_attestation","attest_author":"https://pith.science/pith/UMFTMXB75KAGGIDQIROCMUMVTK/action/author_attestation","sign_citation":"https://pith.science/pith/UMFTMXB75KAGGIDQIROCMUMVTK/action/citation_signature","submit_replication":"https://pith.science/pith/UMFTMXB75KAGGIDQIROCMUMVTK/action/replication_record"}},"created_at":"2026-07-13T00:17:21.211436+00:00","updated_at":"2026-07-13T00:17:21.211436+00:00"}