{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:7EM42I6B5QCLGYWYARQS4WU6NG","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":"2ea1da67d61b59a515701c9ed3a7ec4a0c530440fa4ee8a9866e4d5c0c2c899b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-15T09:22:55Z","title_canon_sha256":"a78254482a20dcb2f82e4792dc4ac9f23938ba8cd382683a8d392a0de6c25a07"},"schema_version":"1.0","source":{"id":"2605.15763","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15763","created_at":"2026-05-20T00:01:16Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15763v1","created_at":"2026-05-20T00:01:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15763","created_at":"2026-05-20T00:01:16Z"},{"alias_kind":"pith_short_12","alias_value":"7EM42I6B5QCL","created_at":"2026-05-20T00:01:16Z"},{"alias_kind":"pith_short_16","alias_value":"7EM42I6B5QCLGYWY","created_at":"2026-05-20T00:01:16Z"},{"alias_kind":"pith_short_8","alias_value":"7EM42I6B","created_at":"2026-05-20T00:01:16Z"}],"graph_snapshots":[{"event_id":"sha256:a642ef63640dc37a23a71c937fc06105642bd87cfe52555d46d992924cc3f5b2","target":"graph","created_at":"2026-05-20T00:01:16Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:48.761681Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T17:21:55.951889Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.15763/integrity.json","findings":[],"snapshot_sha256":"fa90fb757659b998a35789a3079186b2d870e70b455432c23e89a5493a31bf65","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current state-of-the-art Quality Estimation (QE) in machine translation relies on massive, proprietary LLMs, raising data privacy concerns. We demonstrate that smaller, open-source LLMs (<30B parameters) are a viable, cost-effective and privacy-preserving alternative. Using a single-pass prompting strategy, our models simultaneously generate quality scores, MQM error annotations, suggested error corrections, and full post-editions. Our analysis shows these models achieve highly competitive system-level correlations with human judgments that outperform traditional neural metrics, fine-tuned mod","authors_text":"Artur Nowakowski, Kamil Guttmann, Krzysztof Jassem, Zofia Fra\\'s","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-15T09:22:55Z","title":"CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.15763","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":"sha256:145c9317c1034a256a1a1627cdf8c8c8b7f2776d7b8d5b33867a96da9be45062","target":"record","created_at":"2026-05-20T00:01:16Z","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":"2ea1da67d61b59a515701c9ed3a7ec4a0c530440fa4ee8a9866e4d5c0c2c899b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-15T09:22:55Z","title_canon_sha256":"a78254482a20dcb2f82e4792dc4ac9f23938ba8cd382683a8d392a0de6c25a07"},"schema_version":"1.0","source":{"id":"2605.15763","kind":"arxiv","version":1}},"canonical_sha256":"f919cd23c1ec04b362d804612e5a9e69aa42b3141ef0fda8f19d3d4981721cc7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f919cd23c1ec04b362d804612e5a9e69aa42b3141ef0fda8f19d3d4981721cc7","first_computed_at":"2026-05-20T00:01:16.916421Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:16.916421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G9yhoQA7fBSLOYBqc7X2Ti5MvcSNM287IAHZzB3y/XqNKf+gzhfLAMTJY1lZwpwa/kG+lRl9wmW0tRM/v3jaCA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:16.917259Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15763","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:145c9317c1034a256a1a1627cdf8c8c8b7f2776d7b8d5b33867a96da9be45062","sha256:a642ef63640dc37a23a71c937fc06105642bd87cfe52555d46d992924cc3f5b2"],"state_sha256":"1991f0169b15124f743188cf5eb49e004e97142f77e7022a5cc1c87c10bc393a"}