{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HLTTQN63KSHD2LSWCBHXVA7Q5T","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":"0122acad537aec8dba8b16d4f93d04a0877873a5f57d6827cb723c62b50aa26c","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-24T13:15:21Z","title_canon_sha256":"dc303a0a2b65e31925fababa0589d7f5a7a2daf5ec5a4a2a176e5c8aa4c44010"},"schema_version":"1.0","source":{"id":"2507.18392","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.18392","created_at":"2026-07-05T11:42:47Z"},{"alias_kind":"arxiv_version","alias_value":"2507.18392v1","created_at":"2026-07-05T11:42:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.18392","created_at":"2026-07-05T11:42:47Z"},{"alias_kind":"pith_short_12","alias_value":"HLTTQN63KSHD","created_at":"2026-07-05T11:42:47Z"},{"alias_kind":"pith_short_16","alias_value":"HLTTQN63KSHD2LSW","created_at":"2026-07-05T11:42:47Z"},{"alias_kind":"pith_short_8","alias_value":"HLTTQN63","created_at":"2026-07-05T11:42:47Z"}],"graph_snapshots":[{"event_id":"sha256:baaa41053a02e80dafeb5bb450ba3841d1652e12d9581a78306bd47ccee764f9","target":"graph","created_at":"2026-07-05T11:42:47Z","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/2507.18392/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The evaluation of Large Language Models (LLMs) increasingly relies on other LLMs acting as judges. However, current evaluation paradigms typically yield a single score or ranking, answering which model is better but not why. While essential for benchmarking, these top-level scores obscure the specific, actionable reasons behind a model's performance. To bridge this gap, we introduce CLEAR, an interactive, open-source package for LLM-based error analysis. CLEAR first generates per-instance textual feedback, then it creates a set of system-level error issues, and quantifies the prevalence of eac","authors_text":"Asaf Yehudai, Lilach Eden, Michal Shmueli-Scheuer, Roy Bar-Haim, Yotam Perlitz","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-24T13:15:21Z","title":"CLEAR: Error Analysis via LLM-as-a-Judge Made Easy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.18392","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":"sha256:3eeb43aebbe7ef6a178491f9cddc1a28fbce4832a71993f1dd82e3d6b63db0a5","target":"record","created_at":"2026-07-05T11:42:47Z","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":"0122acad537aec8dba8b16d4f93d04a0877873a5f57d6827cb723c62b50aa26c","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-24T13:15:21Z","title_canon_sha256":"dc303a0a2b65e31925fababa0589d7f5a7a2daf5ec5a4a2a176e5c8aa4c44010"},"schema_version":"1.0","source":{"id":"2507.18392","kind":"arxiv","version":1}},"canonical_sha256":"3ae73837db548e3d2e56104f7a83f0ece3f6ea6d92d6c781ba9c371e4f8ac890","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3ae73837db548e3d2e56104f7a83f0ece3f6ea6d92d6c781ba9c371e4f8ac890","first_computed_at":"2026-07-05T11:42:47.361710Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:42:47.361710Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Qdi8rWsOor/bX36GKtT04bTN7BRXE9ftMYGCbdaqQ+XwGBv75yBP96a1HQJFqCdvLMGu20rhWesSiw0iu38SCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:42:47.362265Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.18392","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3eeb43aebbe7ef6a178491f9cddc1a28fbce4832a71993f1dd82e3d6b63db0a5","sha256:baaa41053a02e80dafeb5bb450ba3841d1652e12d9581a78306bd47ccee764f9"],"state_sha256":"88f49e66534ed2db1b2b23a8ada1d08574ad9449e7299a9fdee310df92a640ab"}