{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:AO5CUTAYHSQLUHCARBOGOLKD7H","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":"fc0d582837ddcca35e092a2bf2bf00f5368933206fc1649d5e54f59328e3f5af","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-02T07:02:44Z","title_canon_sha256":"40c887cac96519b0a25adc8fde72e28083329929b7409ed8d527823083b32c59"},"schema_version":"1.0","source":{"id":"2607.01789","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.01789","created_at":"2026-07-03T01:17:29Z"},{"alias_kind":"arxiv_version","alias_value":"2607.01789v1","created_at":"2026-07-03T01:17:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.01789","created_at":"2026-07-03T01:17:29Z"},{"alias_kind":"pith_short_12","alias_value":"AO5CUTAYHSQL","created_at":"2026-07-03T01:17:29Z"},{"alias_kind":"pith_short_16","alias_value":"AO5CUTAYHSQLUHCA","created_at":"2026-07-03T01:17:29Z"},{"alias_kind":"pith_short_8","alias_value":"AO5CUTAY","created_at":"2026-07-03T01:17:29Z"}],"graph_snapshots":[{"event_id":"sha256:13f07df205d9a194e0ef3165e84a5a4779ab01a28475faf47a9cec04c4b3cddf","target":"graph","created_at":"2026-07-03T01:17:29Z","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/2607.01789/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mixture-of-Experts (MoE) models scale efficiently but remain costly to adapt due to redundant experts and uniform parameter allocation. Existing parameter-efficient fine-tuning (PEFT) methods such as LoRA ignore MoE routing dynamics, leading to suboptimal resource use. We propose EPnG, an adaptive prune-and-grow framework that reallocates LoRA capacity based on expert importance derived from router gate probabilities. EPnG prunes under-utilized experts and expands high-importance experts via rank growth with orthogonal initialization, while maintaining a fixed parameter budget. Across OLMoE an","authors_text":"Ahin Lee, Sehyun Yun, Taesik Gong","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-02T07:02:44Z","title":"EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.01789","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":"sha256:19b697c9c5f2a72d9fdb57ba2452fc2ae1aa55c378d1a05d619e8d27644cd123","target":"record","created_at":"2026-07-03T01:17:29Z","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":"fc0d582837ddcca35e092a2bf2bf00f5368933206fc1649d5e54f59328e3f5af","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-02T07:02:44Z","title_canon_sha256":"40c887cac96519b0a25adc8fde72e28083329929b7409ed8d527823083b32c59"},"schema_version":"1.0","source":{"id":"2607.01789","kind":"arxiv","version":1}},"canonical_sha256":"03ba2a4c183ca0ba1c40885c672d43f9fb260e72e5fe83cf4a90c9a03b5fdafb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"03ba2a4c183ca0ba1c40885c672d43f9fb260e72e5fe83cf4a90c9a03b5fdafb","first_computed_at":"2026-07-03T01:17:29.917868Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-03T01:17:29.917868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Mo3Ctqr0DFVyp229uBJHz1e6VT9ymlnCNy4imNbKyBlVVaq1hYT83sOccfoJnAwNtQM5ZyAH4MpEP8Xp9WlnBg==","signature_status":"signed_v1","signed_at":"2026-07-03T01:17:29.918276Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.01789","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:19b697c9c5f2a72d9fdb57ba2452fc2ae1aa55c378d1a05d619e8d27644cd123","sha256:13f07df205d9a194e0ef3165e84a5a4779ab01a28475faf47a9cec04c4b3cddf"],"state_sha256":"3260b807fe91004a0232f81ffa9509ca73bcc69642f8f8e76552591cf5629016"}