{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:AFBU3EZFBDF4FLVS3NLWZXFYNQ","short_pith_number":"pith:AFBU3EZF","canonical_record":{"source":{"id":"2605.02443","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-04T10:43:27Z","cross_cats_sorted":[],"title_canon_sha256":"85d837d6891f48efbec948a0daf4484ec0c1b6bb65df91440679fdb993fb9319","abstract_canon_sha256":"a8d4aa271dcdba19a8e074e11c31c6c2314cc65b4663f6828f11fb536ca80ae9"},"schema_version":"1.0"},"canonical_sha256":"01434d932508cbc2aeb2db576cdcb86c38372400133e99ec29c243388ce2d812","source":{"kind":"arxiv","id":"2605.02443","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.02443","created_at":"2026-05-25T02:02:16Z"},{"alias_kind":"arxiv_version","alias_value":"2605.02443v2","created_at":"2026-05-25T02:02:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.02443","created_at":"2026-05-25T02:02:16Z"},{"alias_kind":"pith_short_12","alias_value":"AFBU3EZFBDF4","created_at":"2026-05-25T02:02:16Z"},{"alias_kind":"pith_short_16","alias_value":"AFBU3EZFBDF4FLVS","created_at":"2026-05-25T02:02:16Z"},{"alias_kind":"pith_short_8","alias_value":"AFBU3EZF","created_at":"2026-05-25T02:02:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:AFBU3EZFBDF4FLVS3NLWZXFYNQ","target":"record","payload":{"canonical_record":{"source":{"id":"2605.02443","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-04T10:43:27Z","cross_cats_sorted":[],"title_canon_sha256":"85d837d6891f48efbec948a0daf4484ec0c1b6bb65df91440679fdb993fb9319","abstract_canon_sha256":"a8d4aa271dcdba19a8e074e11c31c6c2314cc65b4663f6828f11fb536ca80ae9"},"schema_version":"1.0"},"canonical_sha256":"01434d932508cbc2aeb2db576cdcb86c38372400133e99ec29c243388ce2d812","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:02:16.155138Z","signature_b64":"8QjU/Fg7RocVmKUB7k/La7mcYUrfButPjVIaSMFmjylwFvjiBoMoVe8Iw+2788zhhBjqqslD045wMyDAdMsKBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01434d932508cbc2aeb2db576cdcb86c38372400133e99ec29c243388ce2d812","last_reissued_at":"2026-05-25T02:02:16.154597Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:02:16.154597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.02443","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-25T02:02:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wz6Jpu3zieA6EYuHiOMPGCan4TwRK04RAlajfE59uu8mvrZnVSS1wn3MHfYPKj00Y6Mer6I6FvRroKQg0yxPBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T02:01:25.223419Z"},"content_sha256":"a8d2fcacf4a9fcac7a384843a0d5c688db962fcc25ad54ac86f281e943affb22","schema_version":"1.0","event_id":"sha256:a8d2fcacf4a9fcac7a384843a0d5c688db962fcc25ad54ac86f281e943affb22"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:AFBU3EZFBDF4FLVS3NLWZXFYNQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HalluScan: A Systematic Benchmark for Detecting and Mitigating Hallucinations in Instruction-Following LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Systematic benchmarking reveals NLI Verification as the most effective method for detecting hallucinations in LLMs at an AUROC of 0.88.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ahmed Cherif","submitted_at":"2026-05-04T10:43:27Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks, yet they remain susceptible to hallucinations -- generating content that is factually incorrect, unfaithful to provided context, or misaligned with user instructions. We present HalluScan, a comprehensive benchmark framework that systematically evaluates hallucination detection and mitigation across 72 configurations spanning 6 detection methods, 4 open-weight model families, and 3 diverse domains. We introduce three key contributions: (1) HalluScore, a novel composite metri"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"NLI Verification achieves the highest overall AUROC of 0.88, while RAV achieves the second-highest AUROC of 0.66. HalluScore achieves a Pearson correlation of r = 0.41 with human expert judgments.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the 72 configurations, chosen models, and three domains sufficiently represent the full range of hallucination behaviors in instruction-following LLMs and that human judgments provide a stable ground truth for validating the new metric.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"HalluScan benchmark tests hallucination detectors on LLMs, identifies NLI Verification as top performer with 0.88 AUROC, and introduces HalluScore (r=0.41 with humans) plus a routing method for 2x cost savings.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Systematic benchmarking reveals NLI Verification as the most effective method for detecting hallucinations in LLMs at an AUROC of 0.88.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f3a18cc1848e370773a4dc20497832d0bf7bf8819d4796e7cdab01b6c775c0d7"},"source":{"id":"2605.02443","kind":"arxiv","version":2},"verdict":{"id":"b7d1684b-537a-4e18-bf2b-4b834e1c50ee","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T18:25:03.167234Z","strongest_claim":"NLI Verification achieves the highest overall AUROC of 0.88, while RAV achieves the second-highest AUROC of 0.66. HalluScore achieves a Pearson correlation of r = 0.41 with human expert judgments.","one_line_summary":"HalluScan benchmark tests hallucination detectors on LLMs, identifies NLI Verification as top performer with 0.88 AUROC, and introduces HalluScore (r=0.41 with humans) plus a routing method for 2x cost savings.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the 72 configurations, chosen models, and three domains sufficiently represent the full range of hallucination behaviors in instruction-following LLMs and that human judgments provide a stable ground truth for validating the new metric.","pith_extraction_headline":"Systematic benchmarking reveals NLI Verification as the most effective method for detecting hallucinations in LLMs at an AUROC of 0.88."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.02443/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T15:40:33.758746Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-20T03:31:22.113923Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T16:22:47.877602Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"b8c5b882ae4b8996fd4da6824b4d1c15004b86d4411de55f572f5a396c4a5592"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":3,"snapshot_sha256":"439e5e5d84d3042ab501687000b3249d1ced253ce7681d07e180d3c7a4a204bb"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"b7d1684b-537a-4e18-bf2b-4b834e1c50ee"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-25T02:02:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fFfZym5xIyswZAMY3i6aTrFaKSFHAVeCCCDNlwUn+QbAjJzOrZxfnyZFQAFJ0xjq94+HAIOMJk+dfYenRFqOBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T02:01:25.223927Z"},"content_sha256":"77ded69ab2bc1b0af8a62cbd8117563e93e653ff010b5efdc1edef003fe2cafa","schema_version":"1.0","event_id":"sha256:77ded69ab2bc1b0af8a62cbd8117563e93e653ff010b5efdc1edef003fe2cafa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AFBU3EZFBDF4FLVS3NLWZXFYNQ/bundle.json","state_url":"https://pith.science/pith/AFBU3EZFBDF4FLVS3NLWZXFYNQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AFBU3EZFBDF4FLVS3NLWZXFYNQ/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":"2026-07-09T02:01:25Z","links":{"resolver":"https://pith.science/pith/AFBU3EZFBDF4FLVS3NLWZXFYNQ","bundle":"https://pith.science/pith/AFBU3EZFBDF4FLVS3NLWZXFYNQ/bundle.json","state":"https://pith.science/pith/AFBU3EZFBDF4FLVS3NLWZXFYNQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AFBU3EZFBDF4FLVS3NLWZXFYNQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:AFBU3EZFBDF4FLVS3NLWZXFYNQ","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":"a8d4aa271dcdba19a8e074e11c31c6c2314cc65b4663f6828f11fb536ca80ae9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-04T10:43:27Z","title_canon_sha256":"85d837d6891f48efbec948a0daf4484ec0c1b6bb65df91440679fdb993fb9319"},"schema_version":"1.0","source":{"id":"2605.02443","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.02443","created_at":"2026-05-25T02:02:16Z"},{"alias_kind":"arxiv_version","alias_value":"2605.02443v2","created_at":"2026-05-25T02:02:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.02443","created_at":"2026-05-25T02:02:16Z"},{"alias_kind":"pith_short_12","alias_value":"AFBU3EZFBDF4","created_at":"2026-05-25T02:02:16Z"},{"alias_kind":"pith_short_16","alias_value":"AFBU3EZFBDF4FLVS","created_at":"2026-05-25T02:02:16Z"},{"alias_kind":"pith_short_8","alias_value":"AFBU3EZF","created_at":"2026-05-25T02:02:16Z"}],"graph_snapshots":[{"event_id":"sha256:77ded69ab2bc1b0af8a62cbd8117563e93e653ff010b5efdc1edef003fe2cafa","target":"graph","created_at":"2026-05-25T02:02:16Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"NLI Verification achieves the highest overall AUROC of 0.88, while RAV achieves the second-highest AUROC of 0.66. HalluScore achieves a Pearson correlation of r = 0.41 with human expert judgments."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the 72 configurations, chosen models, and three domains sufficiently represent the full range of hallucination behaviors in instruction-following LLMs and that human judgments provide a stable ground truth for validating the new metric."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"HalluScan benchmark tests hallucination detectors on LLMs, identifies NLI Verification as top performer with 0.88 AUROC, and introduces HalluScore (r=0.41 with humans) plus a routing method for 2x cost savings."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Systematic benchmarking reveals NLI Verification as the most effective method for detecting hallucinations in LLMs at an AUROC of 0.88."}],"snapshot_sha256":"f3a18cc1848e370773a4dc20497832d0bf7bf8819d4796e7cdab01b6c775c0d7"},"formal_canon":{"evidence_count":3,"snapshot_sha256":"439e5e5d84d3042ab501687000b3249d1ced253ce7681d07e180d3c7a4a204bb"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-20T15:40:33.758746Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-20T03:31:22.113923Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T16:22:47.877602Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.02443/integrity.json","findings":[],"snapshot_sha256":"b8c5b882ae4b8996fd4da6824b4d1c15004b86d4411de55f572f5a396c4a5592","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks, yet they remain susceptible to hallucinations -- generating content that is factually incorrect, unfaithful to provided context, or misaligned with user instructions. We present HalluScan, a comprehensive benchmark framework that systematically evaluates hallucination detection and mitigation across 72 configurations spanning 6 detection methods, 4 open-weight model families, and 3 diverse domains. We introduce three key contributions: (1) HalluScore, a novel composite metri","authors_text":"Ahmed Cherif","cross_cats":[],"headline":"Systematic benchmarking reveals NLI Verification as the most effective method for detecting hallucinations in LLMs at an AUROC of 0.88.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-04T10:43:27Z","title":"HalluScan: A Systematic Benchmark for Detecting and Mitigating Hallucinations in Instruction-Following LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.02443","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-08T18:25:03.167234Z","id":"b7d1684b-537a-4e18-bf2b-4b834e1c50ee","model_set":{"reader":"grok-4.3"},"one_line_summary":"HalluScan benchmark tests hallucination detectors on LLMs, identifies NLI Verification as top performer with 0.88 AUROC, and introduces HalluScore (r=0.41 with humans) plus a routing method for 2x cost savings.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Systematic benchmarking reveals NLI Verification as the most effective method for detecting hallucinations in LLMs at an AUROC of 0.88.","strongest_claim":"NLI Verification achieves the highest overall AUROC of 0.88, while RAV achieves the second-highest AUROC of 0.66. HalluScore achieves a Pearson correlation of r = 0.41 with human expert judgments.","weakest_assumption":"That the 72 configurations, chosen models, and three domains sufficiently represent the full range of hallucination behaviors in instruction-following LLMs and that human judgments provide a stable ground truth for validating the new metric."}},"verdict_id":"b7d1684b-537a-4e18-bf2b-4b834e1c50ee"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a8d2fcacf4a9fcac7a384843a0d5c688db962fcc25ad54ac86f281e943affb22","target":"record","created_at":"2026-05-25T02:02:16Z","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":"a8d4aa271dcdba19a8e074e11c31c6c2314cc65b4663f6828f11fb536ca80ae9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-04T10:43:27Z","title_canon_sha256":"85d837d6891f48efbec948a0daf4484ec0c1b6bb65df91440679fdb993fb9319"},"schema_version":"1.0","source":{"id":"2605.02443","kind":"arxiv","version":2}},"canonical_sha256":"01434d932508cbc2aeb2db576cdcb86c38372400133e99ec29c243388ce2d812","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"01434d932508cbc2aeb2db576cdcb86c38372400133e99ec29c243388ce2d812","first_computed_at":"2026-05-25T02:02:16.154597Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-25T02:02:16.154597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8QjU/Fg7RocVmKUB7k/La7mcYUrfButPjVIaSMFmjylwFvjiBoMoVe8Iw+2788zhhBjqqslD045wMyDAdMsKBg==","signature_status":"signed_v1","signed_at":"2026-05-25T02:02:16.155138Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.02443","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a8d2fcacf4a9fcac7a384843a0d5c688db962fcc25ad54ac86f281e943affb22","sha256:77ded69ab2bc1b0af8a62cbd8117563e93e653ff010b5efdc1edef003fe2cafa"],"state_sha256":"377245a0ade9444e3d404ef84e3cf4f78fd403c8158062e593d0dcd33dadf33f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TVlTfwDpEaP28vUmqZnXKFxxabJTqpeCj98Tguf0BMzZItZp/4eOAuUqN+7fTWesd1wmJMKnJw87dpfuChajDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T02:01:25.226163Z","bundle_sha256":"d8b00f8ced595cff8ebd1be980e302cf7567aa1fdfcd6211232a66d8dfd7f6e2"}}