{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:A6G7TRL7SKK57KCSH27POC4ETH","short_pith_number":"pith:A6G7TRL7","canonical_record":{"source":{"id":"2511.11440","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-14T16:07:18Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"eca200f0bf3800888181f69ed229af3f6c63e56095403892d3944c9d4a687269","abstract_canon_sha256":"72634096b3a174a96c2b35a3656da4b761c5f52a5ca98c8ceee814685ab13c90"},"schema_version":"1.0"},"canonical_sha256":"078df9c57f9295dfa8523ebef70b8499d54d758741462ef6441c6cf243516a2b","source":{"kind":"arxiv","id":"2511.11440","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.11440","created_at":"2026-06-01T01:03:46Z"},{"alias_kind":"arxiv_version","alias_value":"2511.11440v3","created_at":"2026-06-01T01:03:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.11440","created_at":"2026-06-01T01:03:46Z"},{"alias_kind":"pith_short_12","alias_value":"A6G7TRL7SKK5","created_at":"2026-06-01T01:03:46Z"},{"alias_kind":"pith_short_16","alias_value":"A6G7TRL7SKK57KCS","created_at":"2026-06-01T01:03:46Z"},{"alias_kind":"pith_short_8","alias_value":"A6G7TRL7","created_at":"2026-06-01T01:03:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:A6G7TRL7SKK57KCSH27POC4ETH","target":"record","payload":{"canonical_record":{"source":{"id":"2511.11440","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-14T16:07:18Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"eca200f0bf3800888181f69ed229af3f6c63e56095403892d3944c9d4a687269","abstract_canon_sha256":"72634096b3a174a96c2b35a3656da4b761c5f52a5ca98c8ceee814685ab13c90"},"schema_version":"1.0"},"canonical_sha256":"078df9c57f9295dfa8523ebef70b8499d54d758741462ef6441c6cf243516a2b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:46.562783Z","signature_b64":"lidZyLZNEj0QxgxgoH1hWYVjK4hTHME1rqugojJofETM4hcSMmaPoYAtCBTkt3/8wEGZOLyuOQf4XMnFek9GAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"078df9c57f9295dfa8523ebef70b8499d54d758741462ef6441c6cf243516a2b","last_reissued_at":"2026-06-01T01:03:46.561767Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:46.561767Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2511.11440","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-01T01:03:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j7yf5J/+o6gPwbHl8x15kfWLMTBCuIw/wlsCOKEZqNXBJXkNyvdbWbsRQD+19mqeeVqULxiI9hl92KVJGyxVDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:59:24.265147Z"},"content_sha256":"355d391ea01519f37a111fa73aa49d44df350285283c8e4a7e25acaad864747d","schema_version":"1.0","event_id":"sha256:355d391ea01519f37a111fa73aa49d44df350285283c8e4a7e25acaad864747d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:A6G7TRL7SKK57KCSH27POC4ETH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Giuseppe Riccardi, Massimo Rizzoli, Seyed Mahed Mousavi, Simone Alghisi","submitted_at":"2025-11-14T16:07:18Z","abstract_excerpt":"Performance gains of Vision Language Models (VLMs) obtained by fine-tuning are generally based on ad hoc data collection and annotation of real-world scenes. Despite the improvements, this process is often prone to biases, errors, and distribution imbalance, resulting in overfitting and imbalanced performance. Although a few studies have explored synthetic data generation, they typically lack control over data distribution and annotation quality. In this work, we re-evaluate the potential of model fine-tuning by exploring a fully controlled data generation and annotation pipeline, obtaining bi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.11440","kind":"arxiv","version":3},"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/2511.11440/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":"2026-06-01T01:03:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fs85IgmxPNWrDuhZoi2HVzjyeuq0t8b8poGm4tRPXZs8rJ5PJEvFqbUOWMYr1rzMt+JUz6JJStLcOrkozjLlBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:59:24.265927Z"},"content_sha256":"3418652473c63dca66f1efb29a1f99a2019d21d6121bb6c1f68a6e853c85008b","schema_version":"1.0","event_id":"sha256:3418652473c63dca66f1efb29a1f99a2019d21d6121bb6c1f68a6e853c85008b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A6G7TRL7SKK57KCSH27POC4ETH/bundle.json","state_url":"https://pith.science/pith/A6G7TRL7SKK57KCSH27POC4ETH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A6G7TRL7SKK57KCSH27POC4ETH/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":"2026-06-06T06:59:24Z","links":{"resolver":"https://pith.science/pith/A6G7TRL7SKK57KCSH27POC4ETH","bundle":"https://pith.science/pith/A6G7TRL7SKK57KCSH27POC4ETH/bundle.json","state":"https://pith.science/pith/A6G7TRL7SKK57KCSH27POC4ETH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A6G7TRL7SKK57KCSH27POC4ETH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:A6G7TRL7SKK57KCSH27POC4ETH","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":"72634096b3a174a96c2b35a3656da4b761c5f52a5ca98c8ceee814685ab13c90","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-14T16:07:18Z","title_canon_sha256":"eca200f0bf3800888181f69ed229af3f6c63e56095403892d3944c9d4a687269"},"schema_version":"1.0","source":{"id":"2511.11440","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.11440","created_at":"2026-06-01T01:03:46Z"},{"alias_kind":"arxiv_version","alias_value":"2511.11440v3","created_at":"2026-06-01T01:03:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.11440","created_at":"2026-06-01T01:03:46Z"},{"alias_kind":"pith_short_12","alias_value":"A6G7TRL7SKK5","created_at":"2026-06-01T01:03:46Z"},{"alias_kind":"pith_short_16","alias_value":"A6G7TRL7SKK57KCS","created_at":"2026-06-01T01:03:46Z"},{"alias_kind":"pith_short_8","alias_value":"A6G7TRL7","created_at":"2026-06-01T01:03:46Z"}],"graph_snapshots":[{"event_id":"sha256:3418652473c63dca66f1efb29a1f99a2019d21d6121bb6c1f68a6e853c85008b","target":"graph","created_at":"2026-06-01T01:03:46Z","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/2511.11440/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Performance gains of Vision Language Models (VLMs) obtained by fine-tuning are generally based on ad hoc data collection and annotation of real-world scenes. Despite the improvements, this process is often prone to biases, errors, and distribution imbalance, resulting in overfitting and imbalanced performance. Although a few studies have explored synthetic data generation, they typically lack control over data distribution and annotation quality. In this work, we re-evaluate the potential of model fine-tuning by exploring a fully controlled data generation and annotation pipeline, obtaining bi","authors_text":"Giuseppe Riccardi, Massimo Rizzoli, Seyed Mahed Mousavi, Simone Alghisi","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-14T16:07:18Z","title":"Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.11440","kind":"arxiv","version":3},"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":"sha256:355d391ea01519f37a111fa73aa49d44df350285283c8e4a7e25acaad864747d","target":"record","created_at":"2026-06-01T01:03:46Z","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":"72634096b3a174a96c2b35a3656da4b761c5f52a5ca98c8ceee814685ab13c90","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-11-14T16:07:18Z","title_canon_sha256":"eca200f0bf3800888181f69ed229af3f6c63e56095403892d3944c9d4a687269"},"schema_version":"1.0","source":{"id":"2511.11440","kind":"arxiv","version":3}},"canonical_sha256":"078df9c57f9295dfa8523ebef70b8499d54d758741462ef6441c6cf243516a2b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"078df9c57f9295dfa8523ebef70b8499d54d758741462ef6441c6cf243516a2b","first_computed_at":"2026-06-01T01:03:46.561767Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-01T01:03:46.561767Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lidZyLZNEj0QxgxgoH1hWYVjK4hTHME1rqugojJofETM4hcSMmaPoYAtCBTkt3/8wEGZOLyuOQf4XMnFek9GAQ==","signature_status":"signed_v1","signed_at":"2026-06-01T01:03:46.562783Z","signed_message":"canonical_sha256_bytes"},"source_id":"2511.11440","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:355d391ea01519f37a111fa73aa49d44df350285283c8e4a7e25acaad864747d","sha256:3418652473c63dca66f1efb29a1f99a2019d21d6121bb6c1f68a6e853c85008b"],"state_sha256":"340a4d187377634f1a91bd915983f927ecf4799242d7f1723ae6e9792a07ebda"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iktK1PY4AI/epDcZsx/FYSoClQgmEM/FMx8MhSnhdH2GdOad6kHLUMaTdXCnxWc46LTifcS4FbVb5r8p1FrqAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T06:59:24.269609Z","bundle_sha256":"706c07952eb4e57e07c7373e8c91a923104b1f73760c7129e9f3d603fa98be48"}}