{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:5F2MVLYBJWBVPJKHGJZZNU2GN5","short_pith_number":"pith:5F2MVLYB","canonical_record":{"source":{"id":"2509.25787","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-30T04:57:26Z","cross_cats_sorted":[],"title_canon_sha256":"77826a429ddbaf5f7bcf83140e806618759bda3ca871950af1b47888ae8111e9","abstract_canon_sha256":"990bbd396033268e67c164ed7e75a7894741804863f567fde39c17ea3ed7a162"},"schema_version":"1.0"},"canonical_sha256":"e974caaf014d8357a547327396d3466f4e637adac2c469580a80df883e3f5f85","source":{"kind":"arxiv","id":"2509.25787","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.25787","created_at":"2026-06-12T01:09:14Z"},{"alias_kind":"arxiv_version","alias_value":"2509.25787v5","created_at":"2026-06-12T01:09:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.25787","created_at":"2026-06-12T01:09:14Z"},{"alias_kind":"pith_short_12","alias_value":"5F2MVLYBJWBV","created_at":"2026-06-12T01:09:14Z"},{"alias_kind":"pith_short_16","alias_value":"5F2MVLYBJWBVPJKH","created_at":"2026-06-12T01:09:14Z"},{"alias_kind":"pith_short_8","alias_value":"5F2MVLYB","created_at":"2026-06-12T01:09:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:5F2MVLYBJWBVPJKHGJZZNU2GN5","target":"record","payload":{"canonical_record":{"source":{"id":"2509.25787","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-30T04:57:26Z","cross_cats_sorted":[],"title_canon_sha256":"77826a429ddbaf5f7bcf83140e806618759bda3ca871950af1b47888ae8111e9","abstract_canon_sha256":"990bbd396033268e67c164ed7e75a7894741804863f567fde39c17ea3ed7a162"},"schema_version":"1.0"},"canonical_sha256":"e974caaf014d8357a547327396d3466f4e637adac2c469580a80df883e3f5f85","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:09:14.521375Z","signature_b64":"5OPLHZmRQkUtgXScYaBZo37FRrUqJ9U0TNUipf3wriUl/sN+9/VxiSoWXQZKRiZCKNB/uExd+RITj/AjyPqNAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e974caaf014d8357a547327396d3466f4e637adac2c469580a80df883e3f5f85","last_reissued_at":"2026-06-12T01:09:14.520594Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:09:14.520594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.25787","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-12T01:09:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wrYBn9YBwRgxJQGNKMiNWpAi1xzx2J1HXXt3BJYDi181Yy1XTigF70rMi8/c8zD1VIWYVX3/1rPaaRLzzCVJDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T12:20:31.641842Z"},"content_sha256":"1b018df4a60624ad86fa372da29038ac18be3050624b2377ca18401cd057eeaa","schema_version":"1.0","event_id":"sha256:1b018df4a60624ad86fa372da29038ac18be3050624b2377ca18401cd057eeaa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:5F2MVLYBJWBVPJKHGJZZNU2GN5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-Evolving Vision-Language Models for Image Quality Assessment via Voting and Ranking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junlin Li, Kanglong Fan, Li Zhang, Tianwu Zhi, Wen Wen, Xinge Peng, Yabin Zhang, Yang Li, Yiting Liao","submitted_at":"2025-09-30T04:57:26Z","abstract_excerpt":"Improving vision-language models (VLMs) in the post-training stage typically relies on supervised fine-tuning or reinforcement learning, methods that necessitate costly, human-annotated data. While self-supervised techniques have proven effective for enhancing reasoning capabilities, their application to perceptual domains such as image quality assessment (IQA) remains largely unexplored. In this work, we introduce EvoQuality, a novel framework that enables a VLM to autonomously refine its quality perception capabilities without any ground-truth labels. EvoQuality adapts the principle of self-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.25787","kind":"arxiv","version":5},"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/2509.25787/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":"2026-06-12T01:09:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NZs2Tlc6dxQ9xDddq+XZ5rmBdlF5x6qU06QuSFVIHtdMw2kBK68ZFISQ4wYfcjeT5KVoGMWom7KPuO0GsjsxDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T12:20:31.642212Z"},"content_sha256":"28c6326e92995b6efe577b0dfd97f3433db577704b8a4bec166995e1fba7c186","schema_version":"1.0","event_id":"sha256:28c6326e92995b6efe577b0dfd97f3433db577704b8a4bec166995e1fba7c186"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5F2MVLYBJWBVPJKHGJZZNU2GN5/bundle.json","state_url":"https://pith.science/pith/5F2MVLYBJWBVPJKHGJZZNU2GN5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5F2MVLYBJWBVPJKHGJZZNU2GN5/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":"2026-07-03T12:20:31Z","links":{"resolver":"https://pith.science/pith/5F2MVLYBJWBVPJKHGJZZNU2GN5","bundle":"https://pith.science/pith/5F2MVLYBJWBVPJKHGJZZNU2GN5/bundle.json","state":"https://pith.science/pith/5F2MVLYBJWBVPJKHGJZZNU2GN5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5F2MVLYBJWBVPJKHGJZZNU2GN5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:5F2MVLYBJWBVPJKHGJZZNU2GN5","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":"990bbd396033268e67c164ed7e75a7894741804863f567fde39c17ea3ed7a162","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-30T04:57:26Z","title_canon_sha256":"77826a429ddbaf5f7bcf83140e806618759bda3ca871950af1b47888ae8111e9"},"schema_version":"1.0","source":{"id":"2509.25787","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.25787","created_at":"2026-06-12T01:09:14Z"},{"alias_kind":"arxiv_version","alias_value":"2509.25787v5","created_at":"2026-06-12T01:09:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.25787","created_at":"2026-06-12T01:09:14Z"},{"alias_kind":"pith_short_12","alias_value":"5F2MVLYBJWBV","created_at":"2026-06-12T01:09:14Z"},{"alias_kind":"pith_short_16","alias_value":"5F2MVLYBJWBVPJKH","created_at":"2026-06-12T01:09:14Z"},{"alias_kind":"pith_short_8","alias_value":"5F2MVLYB","created_at":"2026-06-12T01:09:14Z"}],"graph_snapshots":[{"event_id":"sha256:28c6326e92995b6efe577b0dfd97f3433db577704b8a4bec166995e1fba7c186","target":"graph","created_at":"2026-06-12T01:09:14Z","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/2509.25787/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Improving vision-language models (VLMs) in the post-training stage typically relies on supervised fine-tuning or reinforcement learning, methods that necessitate costly, human-annotated data. While self-supervised techniques have proven effective for enhancing reasoning capabilities, their application to perceptual domains such as image quality assessment (IQA) remains largely unexplored. In this work, we introduce EvoQuality, a novel framework that enables a VLM to autonomously refine its quality perception capabilities without any ground-truth labels. EvoQuality adapts the principle of self-","authors_text":"Junlin Li, Kanglong Fan, Li Zhang, Tianwu Zhi, Wen Wen, Xinge Peng, Yabin Zhang, Yang Li, Yiting Liao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-30T04:57:26Z","title":"Self-Evolving Vision-Language Models for Image Quality Assessment via Voting and Ranking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.25787","kind":"arxiv","version":5},"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":"sha256:1b018df4a60624ad86fa372da29038ac18be3050624b2377ca18401cd057eeaa","target":"record","created_at":"2026-06-12T01:09:14Z","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":"990bbd396033268e67c164ed7e75a7894741804863f567fde39c17ea3ed7a162","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-30T04:57:26Z","title_canon_sha256":"77826a429ddbaf5f7bcf83140e806618759bda3ca871950af1b47888ae8111e9"},"schema_version":"1.0","source":{"id":"2509.25787","kind":"arxiv","version":5}},"canonical_sha256":"e974caaf014d8357a547327396d3466f4e637adac2c469580a80df883e3f5f85","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e974caaf014d8357a547327396d3466f4e637adac2c469580a80df883e3f5f85","first_computed_at":"2026-06-12T01:09:14.520594Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-12T01:09:14.520594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5OPLHZmRQkUtgXScYaBZo37FRrUqJ9U0TNUipf3wriUl/sN+9/VxiSoWXQZKRiZCKNB/uExd+RITj/AjyPqNAw==","signature_status":"signed_v1","signed_at":"2026-06-12T01:09:14.521375Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.25787","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1b018df4a60624ad86fa372da29038ac18be3050624b2377ca18401cd057eeaa","sha256:28c6326e92995b6efe577b0dfd97f3433db577704b8a4bec166995e1fba7c186"],"state_sha256":"ad47f97ce2021530c068c6a7381b9b4c324b760ecd3c70a1ea0faa5de09f6ef4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"auzPCh8/nO2SP/xWvagnU57itOim1D57Lk0jnBgREKi0DqBWYLUC+RBzCWpibSr3n1UghWokjB57FyDjnSnQCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T12:20:31.644133Z","bundle_sha256":"b969a100422ca730ec354b05d5330c837fb516aecfa37e13bda434f65b9f35db"}}