{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:UGGK55APE6TG4VBUQOJJTY5WTH","short_pith_number":"pith:UGGK55AP","schema_version":"1.0","canonical_sha256":"a18caef40f27a66e5434839299e3b699ef4684bea3bfa225ebf3ce20d21a149a","source":{"kind":"arxiv","id":"2607.23047","version":1},"attestation_state":"computed","paper":{"title":"MixQuant: Adaptive Mixed-Precision Quantization for Large Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Arham Jain, Ashitabh Misra, Madhav Agrawal, Tarek Abdelzaher","submitted_at":"2026-07-25T05:10:01Z","abstract_excerpt":"Mixed-precision quantization improves the accuracy of post-training quantization by allocating higher bitwidths to sensitive layers, but existing methods solve the allocation for a single fixed memory budget. In practice the budget varies across deployments and is unknown at calibration time. Adaptive quantization addresses this with one offline calibration that serves any budget, yet current methods score layer sensitivity in a manner that does not consider its dependency on quantization levels of other layers. We show that a layer's sensitivity depends strongly on the bitwidths of its upstre"},"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":"2607.23047","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-25T05:10:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d162f89b84d3a36adacc1f9e1445645107c6886689cd16f0733086b888eea71c","abstract_canon_sha256":"1ffed2348b0c7cb2a0cd2cb7422f48609b8eab4bf518d74e2b5d34356f0b86e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T00:22:09.540007Z","signature_b64":"2ap8tApjOTbnn60+ba7eCt+ZfACSJ6FlsDl+DNCJc/ZkgqHhzEXYNny+v9BTw2+LWyOSBxr7PQCizC3uwB4JDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a18caef40f27a66e5434839299e3b699ef4684bea3bfa225ebf3ce20d21a149a","last_reissued_at":"2026-07-28T00:22:09.539122Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T00:22:09.539122Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MixQuant: Adaptive Mixed-Precision Quantization for Large Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Arham Jain, Ashitabh Misra, Madhav Agrawal, Tarek Abdelzaher","submitted_at":"2026-07-25T05:10:01Z","abstract_excerpt":"Mixed-precision quantization improves the accuracy of post-training quantization by allocating higher bitwidths to sensitive layers, but existing methods solve the allocation for a single fixed memory budget. In practice the budget varies across deployments and is unknown at calibration time. Adaptive quantization addresses this with one offline calibration that serves any budget, yet current methods score layer sensitivity in a manner that does not consider its dependency on quantization levels of other layers. We show that a layer's sensitivity depends strongly on the bitwidths of its upstre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.23047","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/2607.23047/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":"2607.23047","created_at":"2026-07-28T00:22:09.539594+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.23047v1","created_at":"2026-07-28T00:22:09.539594+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.23047","created_at":"2026-07-28T00:22:09.539594+00:00"},{"alias_kind":"pith_short_12","alias_value":"UGGK55APE6TG","created_at":"2026-07-28T00:22:09.539594+00:00"},{"alias_kind":"pith_short_16","alias_value":"UGGK55APE6TG4VBU","created_at":"2026-07-28T00:22:09.539594+00:00"},{"alias_kind":"pith_short_8","alias_value":"UGGK55AP","created_at":"2026-07-28T00:22:09.539594+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/UGGK55APE6TG4VBUQOJJTY5WTH","json":"https://pith.science/pith/UGGK55APE6TG4VBUQOJJTY5WTH.json","graph_json":"https://pith.science/api/pith-number/UGGK55APE6TG4VBUQOJJTY5WTH/graph.json","events_json":"https://pith.science/api/pith-number/UGGK55APE6TG4VBUQOJJTY5WTH/events.json","paper":"https://pith.science/paper/UGGK55AP"},"agent_actions":{"view_html":"https://pith.science/pith/UGGK55APE6TG4VBUQOJJTY5WTH","download_json":"https://pith.science/pith/UGGK55APE6TG4VBUQOJJTY5WTH.json","view_paper":"https://pith.science/paper/UGGK55AP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.23047&json=true","fetch_graph":"https://pith.science/api/pith-number/UGGK55APE6TG4VBUQOJJTY5WTH/graph.json","fetch_events":"https://pith.science/api/pith-number/UGGK55APE6TG4VBUQOJJTY5WTH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UGGK55APE6TG4VBUQOJJTY5WTH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UGGK55APE6TG4VBUQOJJTY5WTH/action/storage_attestation","attest_author":"https://pith.science/pith/UGGK55APE6TG4VBUQOJJTY5WTH/action/author_attestation","sign_citation":"https://pith.science/pith/UGGK55APE6TG4VBUQOJJTY5WTH/action/citation_signature","submit_replication":"https://pith.science/pith/UGGK55APE6TG4VBUQOJJTY5WTH/action/replication_record"}},"created_at":"2026-07-28T00:22:09.539594+00:00","updated_at":"2026-07-28T00:22:09.539594+00:00"}