{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:X2JH3KM74GI2I26CDQLZH2OVY3","short_pith_number":"pith:X2JH3KM7","canonical_record":{"source":{"id":"2506.06884","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-07T18:26:58Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c74ec85cfe11023459a2089ce5057aab952e69b5928ffa2ebcd6ea2e6a2db626","abstract_canon_sha256":"d6d7f7e38a565dbc1c8c5b287b45481e098ce68dd7aa680548934e5fd2308c21"},"schema_version":"1.0"},"canonical_sha256":"be927da99fe191a46bc21c1793e9d5c6ef1ff0b39ec941cdb7b6f9ad02d4acff","source":{"kind":"arxiv","id":"2506.06884","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06884","created_at":"2026-07-05T11:17:57Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06884v1","created_at":"2026-07-05T11:17:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06884","created_at":"2026-07-05T11:17:57Z"},{"alias_kind":"pith_short_12","alias_value":"X2JH3KM74GI2","created_at":"2026-07-05T11:17:57Z"},{"alias_kind":"pith_short_16","alias_value":"X2JH3KM74GI2I26C","created_at":"2026-07-05T11:17:57Z"},{"alias_kind":"pith_short_8","alias_value":"X2JH3KM7","created_at":"2026-07-05T11:17:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:X2JH3KM74GI2I26CDQLZH2OVY3","target":"record","payload":{"canonical_record":{"source":{"id":"2506.06884","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-07T18:26:58Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c74ec85cfe11023459a2089ce5057aab952e69b5928ffa2ebcd6ea2e6a2db626","abstract_canon_sha256":"d6d7f7e38a565dbc1c8c5b287b45481e098ce68dd7aa680548934e5fd2308c21"},"schema_version":"1.0"},"canonical_sha256":"be927da99fe191a46bc21c1793e9d5c6ef1ff0b39ec941cdb7b6f9ad02d4acff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:57.508008Z","signature_b64":"VXz8J0/BMm1pcBxZfQXwspY2FMTiuT1+riRhkDrKbPtx8aj9zxZYbFMsz0eP3UpKv1WcuWBbjU35aGtiDBPBCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be927da99fe191a46bc21c1793e9d5c6ef1ff0b39ec941cdb7b6f9ad02d4acff","last_reissued_at":"2026-07-05T11:17:57.507241Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:57.507241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.06884","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:17:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9ZJdS3NFmAM69d64YebPpRc/enXLOUOtwOC5U/67c/ME/BpLIFMvRAo8lOwFhId+7jEMJIVcXeKAdsBQWygWCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T17:55:52.094573Z"},"content_sha256":"703c7bf34409580e19373ffff8f4ef57e82447220f71d2a522abb725500d3fb5","schema_version":"1.0","event_id":"sha256:703c7bf34409580e19373ffff8f4ef57e82447220f71d2a522abb725500d3fb5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:X2JH3KM74GI2I26CDQLZH2OVY3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FREE: Fast and Robust Vision Language Models with Early Exits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Divya Jyoti Bajpai, Manjesh Kumar Hanawal","submitted_at":"2025-06-07T18:26:58Z","abstract_excerpt":"In recent years, Vision-Language Models (VLMs) have shown remarkable performance improvements in Vision-Language tasks. However, their large size poses challenges for real-world applications where inference latency is a concern. To tackle this issue, we propose employing Early Exit (EE) strategies in VLMs. However, training exit classifiers in VLMs is challenging, particularly with limited labeled training data. To address this, we introduce FREE, an adversarial training approach within a GAN-based framework. Here, each exit consists of a transformer layer and a classifier. The transformer lay"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06884","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/2506.06884/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:17:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QGRY5dm0EV1HKGN65vPyjukUVCrdi8SkmP4ueCWCpQw8Z8Bj0Dbj0hBhKLVeMPqJG8u7U1trLqjfRulQDrpxDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T17:55:52.095194Z"},"content_sha256":"fb27232de03108bc44d86259859eae39e52b8f0e93399781cde40201792dda2d","schema_version":"1.0","event_id":"sha256:fb27232de03108bc44d86259859eae39e52b8f0e93399781cde40201792dda2d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X2JH3KM74GI2I26CDQLZH2OVY3/bundle.json","state_url":"https://pith.science/pith/X2JH3KM74GI2I26CDQLZH2OVY3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X2JH3KM74GI2I26CDQLZH2OVY3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T17:55:52Z","links":{"resolver":"https://pith.science/pith/X2JH3KM74GI2I26CDQLZH2OVY3","bundle":"https://pith.science/pith/X2JH3KM74GI2I26CDQLZH2OVY3/bundle.json","state":"https://pith.science/pith/X2JH3KM74GI2I26CDQLZH2OVY3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X2JH3KM74GI2I26CDQLZH2OVY3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:X2JH3KM74GI2I26CDQLZH2OVY3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d6d7f7e38a565dbc1c8c5b287b45481e098ce68dd7aa680548934e5fd2308c21","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-07T18:26:58Z","title_canon_sha256":"c74ec85cfe11023459a2089ce5057aab952e69b5928ffa2ebcd6ea2e6a2db626"},"schema_version":"1.0","source":{"id":"2506.06884","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06884","created_at":"2026-07-05T11:17:57Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06884v1","created_at":"2026-07-05T11:17:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06884","created_at":"2026-07-05T11:17:57Z"},{"alias_kind":"pith_short_12","alias_value":"X2JH3KM74GI2","created_at":"2026-07-05T11:17:57Z"},{"alias_kind":"pith_short_16","alias_value":"X2JH3KM74GI2I26C","created_at":"2026-07-05T11:17:57Z"},{"alias_kind":"pith_short_8","alias_value":"X2JH3KM7","created_at":"2026-07-05T11:17:57Z"}],"graph_snapshots":[{"event_id":"sha256:fb27232de03108bc44d86259859eae39e52b8f0e93399781cde40201792dda2d","target":"graph","created_at":"2026-07-05T11:17:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.06884/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, Vision-Language Models (VLMs) have shown remarkable performance improvements in Vision-Language tasks. However, their large size poses challenges for real-world applications where inference latency is a concern. To tackle this issue, we propose employing Early Exit (EE) strategies in VLMs. However, training exit classifiers in VLMs is challenging, particularly with limited labeled training data. To address this, we introduce FREE, an adversarial training approach within a GAN-based framework. Here, each exit consists of a transformer layer and a classifier. The transformer lay","authors_text":"Divya Jyoti Bajpai, Manjesh Kumar Hanawal","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-07T18:26:58Z","title":"FREE: Fast and Robust Vision Language Models with Early Exits"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06884","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:703c7bf34409580e19373ffff8f4ef57e82447220f71d2a522abb725500d3fb5","target":"record","created_at":"2026-07-05T11:17:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d6d7f7e38a565dbc1c8c5b287b45481e098ce68dd7aa680548934e5fd2308c21","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-07T18:26:58Z","title_canon_sha256":"c74ec85cfe11023459a2089ce5057aab952e69b5928ffa2ebcd6ea2e6a2db626"},"schema_version":"1.0","source":{"id":"2506.06884","kind":"arxiv","version":1}},"canonical_sha256":"be927da99fe191a46bc21c1793e9d5c6ef1ff0b39ec941cdb7b6f9ad02d4acff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"be927da99fe191a46bc21c1793e9d5c6ef1ff0b39ec941cdb7b6f9ad02d4acff","first_computed_at":"2026-07-05T11:17:57.507241Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:17:57.507241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VXz8J0/BMm1pcBxZfQXwspY2FMTiuT1+riRhkDrKbPtx8aj9zxZYbFMsz0eP3UpKv1WcuWBbjU35aGtiDBPBCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:17:57.508008Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.06884","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:703c7bf34409580e19373ffff8f4ef57e82447220f71d2a522abb725500d3fb5","sha256:fb27232de03108bc44d86259859eae39e52b8f0e93399781cde40201792dda2d"],"state_sha256":"d5a8a86e9590853737c2f5cce9b231e54ef78be915b2ea352c5e270b55ab3264"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MhaUC0NeZ595Uu2ZqTPVGruBs9G2Ya3UsHICO+paVZFsOuNdyrBc7rfTmpoTglJs1cS2ovXOenIYUxrHQ7wxCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T17:55:52.099498Z","bundle_sha256":"a2243db923e3f5bcd8dfcf492e617155abf1110fa95f127e6ab87ae56063251c"}}