{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:23VNEKNIXMWRCND4CTRNMVTPYH","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":"949a906e32082035f73aa9e761e948c26cf0051f6c746371dd08e262d202a0d7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-10T17:59:11Z","title_canon_sha256":"5724e03f7c64b3a140d0e52b8cd5c99b8ba27e6f0344e706b821aec3ab3f8d37"},"schema_version":"1.0","source":{"id":"2410.08201","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.08201","created_at":"2026-07-05T11:14:24Z"},{"alias_kind":"arxiv_version","alias_value":"2410.08201v2","created_at":"2026-07-05T11:14:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.08201","created_at":"2026-07-05T11:14:24Z"},{"alias_kind":"pith_short_12","alias_value":"23VNEKNIXMWR","created_at":"2026-07-05T11:14:24Z"},{"alias_kind":"pith_short_16","alias_value":"23VNEKNIXMWRCND4","created_at":"2026-07-05T11:14:24Z"},{"alias_kind":"pith_short_8","alias_value":"23VNEKNI","created_at":"2026-07-05T11:14:24Z"}],"graph_snapshots":[{"event_id":"sha256:a312c4cc195c674bdd79ee5b6caaad5c9ede03c01e3ffad8eea2b23e50bd6b45","target":"graph","created_at":"2026-07-05T11:14:24Z","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/2410.08201/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Sparse autoencoders (SAEs) are a recent technique for decomposing neural network activations into human-interpretable features. However, in order for SAEs to identify all features represented in frontier models, it will be necessary to scale them up to very high width, posing a computational challenge. In this work, we introduce Switch Sparse Autoencoders, a novel SAE architecture aimed at reducing the compute cost of training SAEs. Inspired by sparse mixture of experts models, Switch SAEs route activation vectors between smaller \"expert\" SAEs, enabling SAEs to efficiently scale to many more f","authors_text":"Anish Mudide, Christian Schroeder de Witt, Eric J. Michaud, Joshua Engels, Max Tegmark","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-10T17:59:11Z","title":"Efficient Dictionary Learning with Switch Sparse Autoencoders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.08201","kind":"arxiv","version":2},"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":"sha256:070237ed6bd8fb88109cf92dd7d4070aa0b5d32f367111bebbb26ae01d14de23","target":"record","created_at":"2026-07-05T11:14:24Z","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":"949a906e32082035f73aa9e761e948c26cf0051f6c746371dd08e262d202a0d7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-10T17:59:11Z","title_canon_sha256":"5724e03f7c64b3a140d0e52b8cd5c99b8ba27e6f0344e706b821aec3ab3f8d37"},"schema_version":"1.0","source":{"id":"2410.08201","kind":"arxiv","version":2}},"canonical_sha256":"d6ead229a8bb2d11347c14e2d6566fc1c12d1e0bd9c6d5a6a19b8e09f2007d13","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d6ead229a8bb2d11347c14e2d6566fc1c12d1e0bd9c6d5a6a19b8e09f2007d13","first_computed_at":"2026-07-05T11:14:24.989941Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:14:24.989941Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EbY7vE/mSAM2ddqwNzgSoImlgvl/JFH5crtZLb3kQjzD3D9OcgBEIo+1+QYGfhXr1cPP3709AODMq5kqtfbaBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:14:24.990447Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.08201","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:070237ed6bd8fb88109cf92dd7d4070aa0b5d32f367111bebbb26ae01d14de23","sha256:a312c4cc195c674bdd79ee5b6caaad5c9ede03c01e3ffad8eea2b23e50bd6b45"],"state_sha256":"f66b7a1467043a9ca4e90d983fd8804fffb12a9aa71c8d7994bf39feb55575fc"}