{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:3BTXRKHD5NCIQVAT4RR4R2Z64N","short_pith_number":"pith:3BTXRKHD","canonical_record":{"source":{"id":"2510.13999","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-15T18:29:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bd76ec03f536c8c6071ed3021e92a8a51534f7eb9957115796966891f84757f9","abstract_canon_sha256":"c648eea18869308aceb0f59a3f7b30cbeb8f8b831db962e0e4a84e8596b7cafc"},"schema_version":"1.0"},"canonical_sha256":"d86778a8e3eb44885413e463c8eb3ee370f742e3c581f18ec9c1607538aba33e","source":{"kind":"arxiv","id":"2510.13999","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.13999","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"arxiv_version","alias_value":"2510.13999v3","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.13999","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"pith_short_12","alias_value":"3BTXRKHD5NCI","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"3BTXRKHD5NCIQVAT","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"3BTXRKHD","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:3BTXRKHD5NCIQVAT4RR4R2Z64N","target":"record","payload":{"canonical_record":{"source":{"id":"2510.13999","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-15T18:29:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bd76ec03f536c8c6071ed3021e92a8a51534f7eb9957115796966891f84757f9","abstract_canon_sha256":"c648eea18869308aceb0f59a3f7b30cbeb8f8b831db962e0e4a84e8596b7cafc"},"schema_version":"1.0"},"canonical_sha256":"d86778a8e3eb44885413e463c8eb3ee370f742e3c581f18ec9c1607538aba33e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:09:33.856482Z","signature_b64":"mcQAde8sp4FjD/fczhYqR41TsxPnq7JKkoL0BioTjAE+Lzr9FxXb6ja73jTKNSZgjJnQ1uy0BmFbGWGvGuQOAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d86778a8e3eb44885413e463c8eb3ee370f742e3c581f18ec9c1607538aba33e","last_reissued_at":"2026-05-18T03:09:33.855730Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:09:33.855730Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.13999","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:09:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yp0TrLr0tVsxxqjbYBTBb3hGdlJvc51gy+Do+UbFwl7pS8Ejx3tg5JOF7hMUvOhg0kPmypQH5wgk6Uhtw4viDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:47:44.469806Z"},"content_sha256":"e52eaaa3bc1a9c5d97862ba7be8499337dc83f9225d1a81a40c857aab2e179ee","schema_version":"1.0","event_id":"sha256:e52eaaa3bc1a9c5d97862ba7be8499337dc83f9225d1a81a40c857aab2e179ee"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:3BTXRKHD5NCIQVAT4RR4R2Z64N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"REAP the Experts: Why Pruning Prevails for One-Shot MoE compression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ivan Lazarevich, Mike Lasby, Nish Sinnadurai, Sean Lie, Vithursan Thangarasa, Yani Ioannou","submitted_at":"2025-10-15T18:29:28Z","abstract_excerpt":"Sparsely-activated Mixture-of-Experts (SMoE) models offer efficient pre-training and low latency but their large parameter counts create significant memory overhead, motivating research into expert compression. Contrary to recent findings favouring expert merging on discriminative benchmarks, we find that expert pruning is a superior strategy for generative tasks. We demonstrate that existing merging techniques introduce an irreducible error due to the loss of fine-grained routing control over experts. Leveraging this insight, we propose Router-weighted Expert Activation Pruning (REAP), a nove"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.13999","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:09:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RoocVkFslIddpKHKjGTzc1ynItq93M5ZW/3U45ygsAek2aMHCHgaZi1gK24+vCS7NN5wSkbOiuuXcJRmBt9qAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:47:44.470557Z"},"content_sha256":"f705302699944c956fe875844ecefa326abd7a584302924ffa377002315266f7","schema_version":"1.0","event_id":"sha256:f705302699944c956fe875844ecefa326abd7a584302924ffa377002315266f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3BTXRKHD5NCIQVAT4RR4R2Z64N/bundle.json","state_url":"https://pith.science/pith/3BTXRKHD5NCIQVAT4RR4R2Z64N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3BTXRKHD5NCIQVAT4RR4R2Z64N/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-06T06:47:44Z","links":{"resolver":"https://pith.science/pith/3BTXRKHD5NCIQVAT4RR4R2Z64N","bundle":"https://pith.science/pith/3BTXRKHD5NCIQVAT4RR4R2Z64N/bundle.json","state":"https://pith.science/pith/3BTXRKHD5NCIQVAT4RR4R2Z64N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3BTXRKHD5NCIQVAT4RR4R2Z64N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:3BTXRKHD5NCIQVAT4RR4R2Z64N","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":"c648eea18869308aceb0f59a3f7b30cbeb8f8b831db962e0e4a84e8596b7cafc","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-15T18:29:28Z","title_canon_sha256":"bd76ec03f536c8c6071ed3021e92a8a51534f7eb9957115796966891f84757f9"},"schema_version":"1.0","source":{"id":"2510.13999","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.13999","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"arxiv_version","alias_value":"2510.13999v3","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.13999","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"pith_short_12","alias_value":"3BTXRKHD5NCI","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"3BTXRKHD5NCIQVAT","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"3BTXRKHD","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:f705302699944c956fe875844ecefa326abd7a584302924ffa377002315266f7","target":"graph","created_at":"2026-05-18T03:09:33Z","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"},"paper":{"abstract_excerpt":"Sparsely-activated Mixture-of-Experts (SMoE) models offer efficient pre-training and low latency but their large parameter counts create significant memory overhead, motivating research into expert compression. Contrary to recent findings favouring expert merging on discriminative benchmarks, we find that expert pruning is a superior strategy for generative tasks. We demonstrate that existing merging techniques introduce an irreducible error due to the loss of fine-grained routing control over experts. Leveraging this insight, we propose Router-weighted Expert Activation Pruning (REAP), a nove","authors_text":"Ivan Lazarevich, Mike Lasby, Nish Sinnadurai, Sean Lie, Vithursan Thangarasa, Yani Ioannou","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-15T18:29:28Z","title":"REAP the Experts: Why Pruning Prevails for One-Shot MoE compression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.13999","kind":"arxiv","version":3},"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:e52eaaa3bc1a9c5d97862ba7be8499337dc83f9225d1a81a40c857aab2e179ee","target":"record","created_at":"2026-05-18T03:09:33Z","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":"c648eea18869308aceb0f59a3f7b30cbeb8f8b831db962e0e4a84e8596b7cafc","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-15T18:29:28Z","title_canon_sha256":"bd76ec03f536c8c6071ed3021e92a8a51534f7eb9957115796966891f84757f9"},"schema_version":"1.0","source":{"id":"2510.13999","kind":"arxiv","version":3}},"canonical_sha256":"d86778a8e3eb44885413e463c8eb3ee370f742e3c581f18ec9c1607538aba33e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d86778a8e3eb44885413e463c8eb3ee370f742e3c581f18ec9c1607538aba33e","first_computed_at":"2026-05-18T03:09:33.855730Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:33.855730Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mcQAde8sp4FjD/fczhYqR41TsxPnq7JKkoL0BioTjAE+Lzr9FxXb6ja73jTKNSZgjJnQ1uy0BmFbGWGvGuQOAw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:33.856482Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.13999","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e52eaaa3bc1a9c5d97862ba7be8499337dc83f9225d1a81a40c857aab2e179ee","sha256:f705302699944c956fe875844ecefa326abd7a584302924ffa377002315266f7"],"state_sha256":"b5f770cfa5cddfa966eb9fb66f831800a419eef52b5e1fb905c2ff757921759e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"va974lESuqvDBgaIiNwoO8Ie79YxeamvIm9bx03TwYTLMTKGfP1m2ATTqJK4MG3jINw+2kEVyiGgGwJqKkKZCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T06:47:44.474150Z","bundle_sha256":"8289340b481f09bb6b3adfa3c217b6c89f034dddce132a78a14834115ca6c1d2"}}