{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:6PDVPSG5KNCD3O4JWS425K22D6","short_pith_number":"pith:6PDVPSG5","canonical_record":{"source":{"id":"2606.12633","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-10T19:42:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"772df7faf8f6deb5d920c8bc28199df9c6e1586cb5d4844f0d542220262af77b","abstract_canon_sha256":"26e846dccc093341bbfbd4f8b38bd3b51bb35447bcbb65b145f5e4e8ebdd032a"},"schema_version":"1.0"},"canonical_sha256":"f3c757c8dd53443dbb89b4b9aeab5a1f8558c974e401cbf3b1fe1b5e76a88cb0","source":{"kind":"arxiv","id":"2606.12633","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.12633","created_at":"2026-06-12T01:08:41Z"},{"alias_kind":"arxiv_version","alias_value":"2606.12633v1","created_at":"2026-06-12T01:08:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12633","created_at":"2026-06-12T01:08:41Z"},{"alias_kind":"pith_short_12","alias_value":"6PDVPSG5KNCD","created_at":"2026-06-12T01:08:41Z"},{"alias_kind":"pith_short_16","alias_value":"6PDVPSG5KNCD3O4J","created_at":"2026-06-12T01:08:41Z"},{"alias_kind":"pith_short_8","alias_value":"6PDVPSG5","created_at":"2026-06-12T01:08:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:6PDVPSG5KNCD3O4JWS425K22D6","target":"record","payload":{"canonical_record":{"source":{"id":"2606.12633","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-10T19:42:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"772df7faf8f6deb5d920c8bc28199df9c6e1586cb5d4844f0d542220262af77b","abstract_canon_sha256":"26e846dccc093341bbfbd4f8b38bd3b51bb35447bcbb65b145f5e4e8ebdd032a"},"schema_version":"1.0"},"canonical_sha256":"f3c757c8dd53443dbb89b4b9aeab5a1f8558c974e401cbf3b1fe1b5e76a88cb0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:08:41.724076Z","signature_b64":"XhLytmvsiDbCl6QBkWTRgP9YQTNmZ9GB0be/6KwRo7Xz+m4I7MsexlFoqr2ZbUZ+A7if40sL8XqUS2HNL11GDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f3c757c8dd53443dbb89b4b9aeab5a1f8558c974e401cbf3b1fe1b5e76a88cb0","last_reissued_at":"2026-06-12T01:08:41.722885Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:08:41.722885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.12633","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":"2026-06-12T01:08:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rmSQXtM/Cuwl45j9h16IDoVWyt8o3XA3+z7ylrBoG464Pzo0ZVXjMOFvBeleOsRrv46EMVdlLnVJjMp3zEuECQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T09:33:43.919565Z"},"content_sha256":"33714bc4fc4741716d8f24881d7f9e7374429974f1e8af0965fb29f31a04170b","schema_version":"1.0","event_id":"sha256:33714bc4fc4741716d8f24881d7f9e7374429974f1e8af0965fb29f31a04170b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:6PDVPSG5KNCD3O4JWS425K22D6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chun-Fu Chen, Huajie Shao, Jiangtao Kong, Peijun Zhao, Shaohan Hu, Tianyi Zhou, Youngwook Do","submitted_at":"2026-06-10T19:42:03Z","abstract_excerpt":"Incremental Learning (IL) for Open-ended Image-to-Text Generation (OpenITG) enables models to continuously generate accurate, contextually relevant text for new images while preserving previously acquired knowledge. Unlike prior studies, this paper addresses a more practical scenario in which the predominant category of visual data shifts over time as environments evolve. In this context, we introduce a new notion of continual alignment, which incrementally adapts the alignment module within pre-trained VLMs to preserve high-quality cross-modal representations. Based on this idea, we propose E"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12633","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/2606.12633/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":"2026-06-12T01:08:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"43FrgidkLFuk/R0elP5IubySobcoFkrhFmkX1lmVhW6AEKQ7PtzasgXGLgrkv26EG25XPAVHiyUyrU4zvN9ZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T09:33:43.919945Z"},"content_sha256":"8d115b671155dd42b781fbd07e664c999ddc49dd90a1959acf622be202f214a5","schema_version":"1.0","event_id":"sha256:8d115b671155dd42b781fbd07e664c999ddc49dd90a1959acf622be202f214a5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6PDVPSG5KNCD3O4JWS425K22D6/bundle.json","state_url":"https://pith.science/pith/6PDVPSG5KNCD3O4JWS425K22D6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6PDVPSG5KNCD3O4JWS425K22D6/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":"2026-06-30T09:33:43Z","links":{"resolver":"https://pith.science/pith/6PDVPSG5KNCD3O4JWS425K22D6","bundle":"https://pith.science/pith/6PDVPSG5KNCD3O4JWS425K22D6/bundle.json","state":"https://pith.science/pith/6PDVPSG5KNCD3O4JWS425K22D6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6PDVPSG5KNCD3O4JWS425K22D6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:6PDVPSG5KNCD3O4JWS425K22D6","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":"26e846dccc093341bbfbd4f8b38bd3b51bb35447bcbb65b145f5e4e8ebdd032a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-10T19:42:03Z","title_canon_sha256":"772df7faf8f6deb5d920c8bc28199df9c6e1586cb5d4844f0d542220262af77b"},"schema_version":"1.0","source":{"id":"2606.12633","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.12633","created_at":"2026-06-12T01:08:41Z"},{"alias_kind":"arxiv_version","alias_value":"2606.12633v1","created_at":"2026-06-12T01:08:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12633","created_at":"2026-06-12T01:08:41Z"},{"alias_kind":"pith_short_12","alias_value":"6PDVPSG5KNCD","created_at":"2026-06-12T01:08:41Z"},{"alias_kind":"pith_short_16","alias_value":"6PDVPSG5KNCD3O4J","created_at":"2026-06-12T01:08:41Z"},{"alias_kind":"pith_short_8","alias_value":"6PDVPSG5","created_at":"2026-06-12T01:08:41Z"}],"graph_snapshots":[{"event_id":"sha256:8d115b671155dd42b781fbd07e664c999ddc49dd90a1959acf622be202f214a5","target":"graph","created_at":"2026-06-12T01:08:41Z","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/2606.12633/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Incremental Learning (IL) for Open-ended Image-to-Text Generation (OpenITG) enables models to continuously generate accurate, contextually relevant text for new images while preserving previously acquired knowledge. Unlike prior studies, this paper addresses a more practical scenario in which the predominant category of visual data shifts over time as environments evolve. In this context, we introduce a new notion of continual alignment, which incrementally adapts the alignment module within pre-trained VLMs to preserve high-quality cross-modal representations. Based on this idea, we propose E","authors_text":"Chun-Fu Chen, Huajie Shao, Jiangtao Kong, Peijun Zhao, Shaohan Hu, Tianyi Zhou, Youngwook Do","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-10T19:42:03Z","title":"ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12633","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":"sha256:33714bc4fc4741716d8f24881d7f9e7374429974f1e8af0965fb29f31a04170b","target":"record","created_at":"2026-06-12T01:08:41Z","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":"26e846dccc093341bbfbd4f8b38bd3b51bb35447bcbb65b145f5e4e8ebdd032a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-10T19:42:03Z","title_canon_sha256":"772df7faf8f6deb5d920c8bc28199df9c6e1586cb5d4844f0d542220262af77b"},"schema_version":"1.0","source":{"id":"2606.12633","kind":"arxiv","version":1}},"canonical_sha256":"f3c757c8dd53443dbb89b4b9aeab5a1f8558c974e401cbf3b1fe1b5e76a88cb0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f3c757c8dd53443dbb89b4b9aeab5a1f8558c974e401cbf3b1fe1b5e76a88cb0","first_computed_at":"2026-06-12T01:08:41.722885Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-12T01:08:41.722885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XhLytmvsiDbCl6QBkWTRgP9YQTNmZ9GB0be/6KwRo7Xz+m4I7MsexlFoqr2ZbUZ+A7if40sL8XqUS2HNL11GDA==","signature_status":"signed_v1","signed_at":"2026-06-12T01:08:41.724076Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.12633","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:33714bc4fc4741716d8f24881d7f9e7374429974f1e8af0965fb29f31a04170b","sha256:8d115b671155dd42b781fbd07e664c999ddc49dd90a1959acf622be202f214a5"],"state_sha256":"05f8d57b3c544e513dad8f81c2f033f00ad2da46bf441a92806f9a0003750bbb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3XN6G1WeUywjtVMCZkmFF7zwM4sIPgi7NCQolPBaPxa5gFCmo5Zc9AWqnl6wUc65Q3uKz9oQcI5xtctdH10DBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T09:33:43.924095Z","bundle_sha256":"bb0027ecf022cdfdbaffa18cc59909ad330c2d75adbf0b824c95fef859441d7f"}}