{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:UZDMREH5H7AR27VBAGUMP2YIAU","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":"2c2d906909b79bb4ad10773d07884f9db9653bdad43d57d4595385530a15c98b","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-16T04:06:15Z","title_canon_sha256":"2fa887e7090e8879bb93948d8793364c94f59da8dc42f8460f129cf37a0dbf6d"},"schema_version":"1.0","source":{"id":"2504.12359","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.12359","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"arxiv_version","alias_value":"2504.12359v1","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12359","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"pith_short_12","alias_value":"UZDMREH5H7AR","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"pith_short_16","alias_value":"UZDMREH5H7AR27VB","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"pith_short_8","alias_value":"UZDMREH5","created_at":"2026-07-05T10:50:14Z"}],"graph_snapshots":[{"event_id":"sha256:bff81a55de3222a1e898e05e513d26189bc5ddf938ada8ffb0a16a6441e3a9af","target":"graph","created_at":"2026-07-05T10:50:14Z","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/2504.12359/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mixture-of-Experts based large language models (MoE LLMs) have shown significant promise in multitask adaptability by dynamically routing inputs to specialized experts. Despite their success, the collaborative mechanisms among experts are still not well understood, limiting both the interpretability and optimization of these models. In this paper, we focus on two critical issues: (1) identifying expert collaboration patterns, and (2) optimizing MoE LLMs through expert pruning. To address the first issue, we propose a hierarchical sparse dictionary learning (HSDL) method that uncovers the colla","authors_text":"Meixuan Chen, Naifan Zhang, Yang Li, Yan Tang, Yuanbo Tang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-16T04:06:15Z","title":"Unveiling Hidden Collaboration within Mixture-of-Experts in Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12359","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:a3a269766f25aaa3697c51ae9386df46a1e70a8a4373733e4a08f388947fe5ef","target":"record","created_at":"2026-07-05T10:50:14Z","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":"2c2d906909b79bb4ad10773d07884f9db9653bdad43d57d4595385530a15c98b","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-16T04:06:15Z","title_canon_sha256":"2fa887e7090e8879bb93948d8793364c94f59da8dc42f8460f129cf37a0dbf6d"},"schema_version":"1.0","source":{"id":"2504.12359","kind":"arxiv","version":1}},"canonical_sha256":"a646c890fd3fc11d7ea101a8c7eb080529657a52f9f0f83d51f65c7544f4bc84","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a646c890fd3fc11d7ea101a8c7eb080529657a52f9f0f83d51f65c7544f4bc84","first_computed_at":"2026-07-05T10:50:14.782080Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:50:14.782080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"O7RxldwVrpL/gt7bAF7fhcr7LFxk6bnrPNziHNtbEuq/Kjy6dAf70GkaefblurHEW91a/4xNbWOCr+GuWDfHDw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:50:14.782635Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.12359","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a3a269766f25aaa3697c51ae9386df46a1e70a8a4373733e4a08f388947fe5ef","sha256:bff81a55de3222a1e898e05e513d26189bc5ddf938ada8ffb0a16a6441e3a9af"],"state_sha256":"5576daf136693b734d7b2b7ff7d80f03f68676fb473627db7718667bf320c0b6"}