{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:4GXGQHFZUHZMO5AF4SS3NHHKBC","short_pith_number":"pith:4GXGQHFZ","canonical_record":{"source":{"id":"2508.20181","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-27T18:02:04Z","cross_cats_sorted":["cs.AI","cs.CL","cs.MM"],"title_canon_sha256":"8161a305888a56126c9420d24e95601c22d31f3a7fa123b1fe0eee397ec9b5f3","abstract_canon_sha256":"127b2ea7543b9b2a5e77bca1e089f5bd8ee5986b3f94147c6741a721e0d6d02d"},"schema_version":"1.0"},"canonical_sha256":"e1ae681cb9a1f2c77405e4a5b69cea08a1546f7d2b5e190d313be4ebadbae1f0","source":{"kind":"arxiv","id":"2508.20181","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.20181","created_at":"2026-07-05T12:00:30Z"},{"alias_kind":"arxiv_version","alias_value":"2508.20181v1","created_at":"2026-07-05T12:00:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.20181","created_at":"2026-07-05T12:00:30Z"},{"alias_kind":"pith_short_12","alias_value":"4GXGQHFZUHZM","created_at":"2026-07-05T12:00:30Z"},{"alias_kind":"pith_short_16","alias_value":"4GXGQHFZUHZMO5AF","created_at":"2026-07-05T12:00:30Z"},{"alias_kind":"pith_short_8","alias_value":"4GXGQHFZ","created_at":"2026-07-05T12:00:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:4GXGQHFZUHZMO5AF4SS3NHHKBC","target":"record","payload":{"canonical_record":{"source":{"id":"2508.20181","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-27T18:02:04Z","cross_cats_sorted":["cs.AI","cs.CL","cs.MM"],"title_canon_sha256":"8161a305888a56126c9420d24e95601c22d31f3a7fa123b1fe0eee397ec9b5f3","abstract_canon_sha256":"127b2ea7543b9b2a5e77bca1e089f5bd8ee5986b3f94147c6741a721e0d6d02d"},"schema_version":"1.0"},"canonical_sha256":"e1ae681cb9a1f2c77405e4a5b69cea08a1546f7d2b5e190d313be4ebadbae1f0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:30.893163Z","signature_b64":"W7ZbCzELRt90vsOvU/8KV2e+2GdalyqnmG6AntLDRcqN+O2g+2J16sJpC0cgJtmsmh4fQzecp0Vpzy3Gka4wDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1ae681cb9a1f2c77405e4a5b69cea08a1546f7d2b5e190d313be4ebadbae1f0","last_reissued_at":"2026-07-05T12:00:30.892687Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:30.892687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.20181","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":"2026-07-05T12:00:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UV6kN+GkD4be2cKvXTVS33Ro7qgwMVUJPXLl3vEsXkLUoeT/nl2uRxTU1olFuZPKxuhFyY+uriysOycQD2A7DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T12:47:24.913259Z"},"content_sha256":"8c286bfacfe03f6b1efce0605c270d943a492533cd2ba2f3d282925ac54b63df","schema_version":"1.0","event_id":"sha256:8c286bfacfe03f6b1efce0605c270d943a492533cd2ba2f3d282925ac54b63df"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:4GXGQHFZUHZMO5AF4SS3NHHKBC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mitigating Hallucinations in Multimodal LLMs via Object-aware Preference Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.MM"],"primary_cat":"cs.CV","authors_text":"Alberto Compagnoni, Davide Caffagni, Lorenzo Baraldi, Marcella Cornia, Nicholas Moratelli, Rita Cucchiara","submitted_at":"2025-08-27T18:02:04Z","abstract_excerpt":"Multimodal Large Language Models (MLLMs) emerge as a unified interface to address a multitude of tasks, ranging from NLP to computer vision. Despite showcasing state-of-the-art results in many benchmarks, a long-standing issue is the tendency of MLLMs to hallucinate, that is to generate answers to the user's query that are not reflected in the visual input. In this paper, we address the problem of hallucinations as an alignment problem, seeking to steer the MLLM so that it prefers generating content without hallucinations. In contrast to recent approaches that require complicated pipelines to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.20181","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/2508.20181/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":"2026-07-05T12:00:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2GTthPW3x0nQu2hfnezWWQXGbbjvuASvVkX/Q+3Bvhvlppi+jgCx1XvxLNXfWsrUGqA6Pd/CLCC9YouaAX84Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T12:47:24.913780Z"},"content_sha256":"649e291161f4651cd43247e8b14541e977fb45c804a0d05baa918ec6e07fdbe2","schema_version":"1.0","event_id":"sha256:649e291161f4651cd43247e8b14541e977fb45c804a0d05baa918ec6e07fdbe2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4GXGQHFZUHZMO5AF4SS3NHHKBC/bundle.json","state_url":"https://pith.science/pith/4GXGQHFZUHZMO5AF4SS3NHHKBC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4GXGQHFZUHZMO5AF4SS3NHHKBC/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":"2026-08-06T12:47:24Z","links":{"resolver":"https://pith.science/pith/4GXGQHFZUHZMO5AF4SS3NHHKBC","bundle":"https://pith.science/pith/4GXGQHFZUHZMO5AF4SS3NHHKBC/bundle.json","state":"https://pith.science/pith/4GXGQHFZUHZMO5AF4SS3NHHKBC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4GXGQHFZUHZMO5AF4SS3NHHKBC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4GXGQHFZUHZMO5AF4SS3NHHKBC","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":"127b2ea7543b9b2a5e77bca1e089f5bd8ee5986b3f94147c6741a721e0d6d02d","cross_cats_sorted":["cs.AI","cs.CL","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-27T18:02:04Z","title_canon_sha256":"8161a305888a56126c9420d24e95601c22d31f3a7fa123b1fe0eee397ec9b5f3"},"schema_version":"1.0","source":{"id":"2508.20181","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.20181","created_at":"2026-07-05T12:00:30Z"},{"alias_kind":"arxiv_version","alias_value":"2508.20181v1","created_at":"2026-07-05T12:00:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.20181","created_at":"2026-07-05T12:00:30Z"},{"alias_kind":"pith_short_12","alias_value":"4GXGQHFZUHZM","created_at":"2026-07-05T12:00:30Z"},{"alias_kind":"pith_short_16","alias_value":"4GXGQHFZUHZMO5AF","created_at":"2026-07-05T12:00:30Z"},{"alias_kind":"pith_short_8","alias_value":"4GXGQHFZ","created_at":"2026-07-05T12:00:30Z"}],"graph_snapshots":[{"event_id":"sha256:649e291161f4651cd43247e8b14541e977fb45c804a0d05baa918ec6e07fdbe2","target":"graph","created_at":"2026-07-05T12:00:30Z","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/2508.20181/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multimodal Large Language Models (MLLMs) emerge as a unified interface to address a multitude of tasks, ranging from NLP to computer vision. Despite showcasing state-of-the-art results in many benchmarks, a long-standing issue is the tendency of MLLMs to hallucinate, that is to generate answers to the user's query that are not reflected in the visual input. In this paper, we address the problem of hallucinations as an alignment problem, seeking to steer the MLLM so that it prefers generating content without hallucinations. In contrast to recent approaches that require complicated pipelines to ","authors_text":"Alberto Compagnoni, Davide Caffagni, Lorenzo Baraldi, Marcella Cornia, Nicholas Moratelli, Rita Cucchiara","cross_cats":["cs.AI","cs.CL","cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-27T18:02:04Z","title":"Mitigating Hallucinations in Multimodal LLMs via Object-aware Preference Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.20181","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":"sha256:8c286bfacfe03f6b1efce0605c270d943a492533cd2ba2f3d282925ac54b63df","target":"record","created_at":"2026-07-05T12:00:30Z","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":"127b2ea7543b9b2a5e77bca1e089f5bd8ee5986b3f94147c6741a721e0d6d02d","cross_cats_sorted":["cs.AI","cs.CL","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-27T18:02:04Z","title_canon_sha256":"8161a305888a56126c9420d24e95601c22d31f3a7fa123b1fe0eee397ec9b5f3"},"schema_version":"1.0","source":{"id":"2508.20181","kind":"arxiv","version":1}},"canonical_sha256":"e1ae681cb9a1f2c77405e4a5b69cea08a1546f7d2b5e190d313be4ebadbae1f0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1ae681cb9a1f2c77405e4a5b69cea08a1546f7d2b5e190d313be4ebadbae1f0","first_computed_at":"2026-07-05T12:00:30.892687Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:00:30.892687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"W7ZbCzELRt90vsOvU/8KV2e+2GdalyqnmG6AntLDRcqN+O2g+2J16sJpC0cgJtmsmh4fQzecp0Vpzy3Gka4wDA==","signature_status":"signed_v1","signed_at":"2026-07-05T12:00:30.893163Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.20181","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8c286bfacfe03f6b1efce0605c270d943a492533cd2ba2f3d282925ac54b63df","sha256:649e291161f4651cd43247e8b14541e977fb45c804a0d05baa918ec6e07fdbe2"],"state_sha256":"cb8324ccaeb454b4dadf9b5074389a7fdade50b3b70a91eb92bbf8c3d553b9e3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2+tsxAwnoOcBBlhRGD73aokpd2bF13qutshgABZqIS7LcmMh7aPhv9aOiax50HpTXgfmP7NrOUs26lnFZu/vCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T12:47:24.919099Z","bundle_sha256":"d9029ebf303f35534ee0e0938883e8924d072fc22b07377b1a7d9e2365b0fffe"}}