{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FN5EBS45CWQHIOEBA2JYMVZEIM","short_pith_number":"pith:FN5EBS45","schema_version":"1.0","canonical_sha256":"2b7a40cb9d15a074388106938657244323dbda6e7215d50460d9e101f1bb16fe","source":{"kind":"arxiv","id":"2605.23344","version":1},"attestation_state":"computed","paper":{"title":"CHASD: Language Increment-Calibrated Contrastive Decoding against Hallucination in LVLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Kejia Zhang, Xiaoyi Huang, Zhiming Luo","submitted_at":"2026-05-22T08:03:12Z","abstract_excerpt":"Large Vision-Language Models have shown strong multimodal reasoning capabilities, yet they remain susceptible to object hallucinations when language priors dominate insufficient or misaligned visual evidence. Training-free contrastive decoding methods mitigate this issue by comparing predictions from original and perturbed visual inputs, but existing approaches either apply global perturbations that may alter useful visual evidence or invoke an additional negative branch at every decoding step. In this paper, we observe that hallucination risks are transient and token-specific: visual attentio"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.23344","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-22T08:03:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"382fcc4e410f1541dcbb76276af70cd83056906da767b566079e3f52e05422a1","abstract_canon_sha256":"4ab04be6ff9712d9ff6bd468505bf097a7fc7cbfd0857c2518545a56ff855f46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:49.855403Z","signature_b64":"hqe77+mGk58L86K6y0bN7zxAEwpHDUco6favuwBdN0DPMsu5ghUN0Yd+ilnj5Ut6dZyEwsHVsllej6UoJugaCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b7a40cb9d15a074388106938657244323dbda6e7215d50460d9e101f1bb16fe","last_reissued_at":"2026-05-25T02:01:49.854704Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:49.854704Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CHASD: Language Increment-Calibrated Contrastive Decoding against Hallucination in LVLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Kejia Zhang, Xiaoyi Huang, Zhiming Luo","submitted_at":"2026-05-22T08:03:12Z","abstract_excerpt":"Large Vision-Language Models have shown strong multimodal reasoning capabilities, yet they remain susceptible to object hallucinations when language priors dominate insufficient or misaligned visual evidence. Training-free contrastive decoding methods mitigate this issue by comparing predictions from original and perturbed visual inputs, but existing approaches either apply global perturbations that may alter useful visual evidence or invoke an additional negative branch at every decoding step. In this paper, we observe that hallucination risks are transient and token-specific: visual attentio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23344","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/2605.23344/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.23344","created_at":"2026-05-25T02:01:49.854811+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.23344v1","created_at":"2026-05-25T02:01:49.854811+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.23344","created_at":"2026-05-25T02:01:49.854811+00:00"},{"alias_kind":"pith_short_12","alias_value":"FN5EBS45CWQH","created_at":"2026-05-25T02:01:49.854811+00:00"},{"alias_kind":"pith_short_16","alias_value":"FN5EBS45CWQHIOEB","created_at":"2026-05-25T02:01:49.854811+00:00"},{"alias_kind":"pith_short_8","alias_value":"FN5EBS45","created_at":"2026-05-25T02:01:49.854811+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FN5EBS45CWQHIOEBA2JYMVZEIM","json":"https://pith.science/pith/FN5EBS45CWQHIOEBA2JYMVZEIM.json","graph_json":"https://pith.science/api/pith-number/FN5EBS45CWQHIOEBA2JYMVZEIM/graph.json","events_json":"https://pith.science/api/pith-number/FN5EBS45CWQHIOEBA2JYMVZEIM/events.json","paper":"https://pith.science/paper/FN5EBS45"},"agent_actions":{"view_html":"https://pith.science/pith/FN5EBS45CWQHIOEBA2JYMVZEIM","download_json":"https://pith.science/pith/FN5EBS45CWQHIOEBA2JYMVZEIM.json","view_paper":"https://pith.science/paper/FN5EBS45","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.23344&json=true","fetch_graph":"https://pith.science/api/pith-number/FN5EBS45CWQHIOEBA2JYMVZEIM/graph.json","fetch_events":"https://pith.science/api/pith-number/FN5EBS45CWQHIOEBA2JYMVZEIM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FN5EBS45CWQHIOEBA2JYMVZEIM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FN5EBS45CWQHIOEBA2JYMVZEIM/action/storage_attestation","attest_author":"https://pith.science/pith/FN5EBS45CWQHIOEBA2JYMVZEIM/action/author_attestation","sign_citation":"https://pith.science/pith/FN5EBS45CWQHIOEBA2JYMVZEIM/action/citation_signature","submit_replication":"https://pith.science/pith/FN5EBS45CWQHIOEBA2JYMVZEIM/action/replication_record"}},"created_at":"2026-05-25T02:01:49.854811+00:00","updated_at":"2026-05-25T02:01:49.854811+00:00"}