{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:6SH76UYM4FJOYFYW6CVMKRWI75","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":"1d22fd5d52e2da4d68e16a11ff7cf2ecc5b2b26603d7f344b675eb06cd0c6814","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-03-01T21:21:27Z","title_canon_sha256":"79fa318ed045027e76a2a2ecd89d9f38de825358bfa9735f50c2e5021c9df70f"},"schema_version":"1.0","source":{"id":"2203.00748","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.00748","created_at":"2026-07-05T04:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"2203.00748v1","created_at":"2026-07-05T04:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.00748","created_at":"2026-07-05T04:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"6SH76UYM4FJO","created_at":"2026-07-05T04:01:23Z"},{"alias_kind":"pith_short_16","alias_value":"6SH76UYM4FJOYFYW","created_at":"2026-07-05T04:01:23Z"},{"alias_kind":"pith_short_8","alias_value":"6SH76UYM","created_at":"2026-07-05T04:01:23Z"}],"graph_snapshots":[{"event_id":"sha256:d91d69cb24ee0199fece5c67295ed4cb00d824c6a8c0597df2062b3328ecab95","target":"graph","created_at":"2026-07-05T04:01:23Z","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/2203.00748/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Building huge and highly capable language models has been a trend in the past years. Despite their great performance, they incur high computational cost. A common solution is to apply model compression or choose light-weight architectures, which often need a separate fixed-size model for each desirable computational budget, and may lose performance in case of heavy compression. This paper proposes an effective dynamic inference approach, called E-LANG, which distributes the inference between large accurate Super-models and light-weight Swift models. To this end, a decision making module routes","authors_text":"Amin Banitalebi-Dehkordi, Mohammad Akbari, Yong Zhang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-03-01T21:21:27Z","title":"E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.00748","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":"sha256:0054d919ae0dce0686e5271dfc7eb0afee3442565fddef6cc6c705c38b7f1afc","target":"record","created_at":"2026-07-05T04:01:23Z","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":"1d22fd5d52e2da4d68e16a11ff7cf2ecc5b2b26603d7f344b675eb06cd0c6814","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-03-01T21:21:27Z","title_canon_sha256":"79fa318ed045027e76a2a2ecd89d9f38de825358bfa9735f50c2e5021c9df70f"},"schema_version":"1.0","source":{"id":"2203.00748","kind":"arxiv","version":1}},"canonical_sha256":"f48fff530ce152ec1716f0aac546c8ff7bc73d55aed3c4b1f2469adb8e24c8bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f48fff530ce152ec1716f0aac546c8ff7bc73d55aed3c4b1f2469adb8e24c8bd","first_computed_at":"2026-07-05T04:01:23.267661Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:01:23.267661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zVoVG2DanP6jXf6OnVkh+trrWgbufpaOigOTkzcY3yiRZtIk2es6fe0q0Px7AxIm8f9F3+o3xV8i2GyJIqa4BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:01:23.268067Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.00748","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0054d919ae0dce0686e5271dfc7eb0afee3442565fddef6cc6c705c38b7f1afc","sha256:d91d69cb24ee0199fece5c67295ed4cb00d824c6a8c0597df2062b3328ecab95"],"state_sha256":"95b33898e6896b380988e0e149a7fc159a5014720b9e758c1b53acb8f639fd1e"}