{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:JQQDZSXII2CLETKTYUI2O3IGMD","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":"fbe0bb0913667374e0dfcffdf85fc64651d05e4f17295ac1a8ca4a68e4227b7d","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-03T18:32:01Z","title_canon_sha256":"ce964d7e11cae485d2660b34061f9719578e854c398c88ec3a91204428bc2636"},"schema_version":"1.0","source":{"id":"2506.03292","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.03292","created_at":"2026-07-05T11:15:24Z"},{"alias_kind":"arxiv_version","alias_value":"2506.03292v1","created_at":"2026-07-05T11:15:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.03292","created_at":"2026-07-05T11:15:24Z"},{"alias_kind":"pith_short_12","alias_value":"JQQDZSXII2CL","created_at":"2026-07-05T11:15:24Z"},{"alias_kind":"pith_short_16","alias_value":"JQQDZSXII2CLETKT","created_at":"2026-07-05T11:15:24Z"},{"alias_kind":"pith_short_8","alias_value":"JQQDZSXI","created_at":"2026-07-05T11:15:24Z"}],"graph_snapshots":[{"event_id":"sha256:92c7cc6b79aa5ef6d0418f23d4a0eaa51e51029c8f833fbd7c73b0e5e870f9d5","target":"graph","created_at":"2026-07-05T11:15:24Z","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/2506.03292/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Steering language models (LMs) by modifying internal activations is a popular approach for controlling text generation. Unsupervised dictionary learning methods, e.g., sparse autoencoders, can be scaled to produce many steering vectors, but lack guarantees on the individual efficacy of each vector and control over the coverage of relevant steering tasks. In contrast, supervised methods for constructing steering vectors are targeted and effective, but require more data collection and training for each additional steering vector produced. In this work, we introduce HyperSteer, a family of hypern","authors_text":"Atticus Geiger, Christopher Potts, Jiuding Sun, Michael Sklar, Sidharth Baskaran, Zhengxuan Wu","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-03T18:32:01Z","title":"HyperSteer: Activation Steering at Scale with Hypernetworks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.03292","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:f616e55209f9d240eaa8312ecef0b21fc6d464bc939eeb8b596c945721a5e757","target":"record","created_at":"2026-07-05T11:15:24Z","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":"fbe0bb0913667374e0dfcffdf85fc64651d05e4f17295ac1a8ca4a68e4227b7d","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-03T18:32:01Z","title_canon_sha256":"ce964d7e11cae485d2660b34061f9719578e854c398c88ec3a91204428bc2636"},"schema_version":"1.0","source":{"id":"2506.03292","kind":"arxiv","version":1}},"canonical_sha256":"4c203ccae84684b24d53c511a76d0660e7563f0446a99d0307826af9f42540cc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4c203ccae84684b24d53c511a76d0660e7563f0446a99d0307826af9f42540cc","first_computed_at":"2026-07-05T11:15:24.298773Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:15:24.298773Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xooAaIx3321SdOuTsLl1+JDOZdPQrPiIBV33A/oDTFbTFgD82EP8VzLszrpP860aFgkNNNz846XfehL/rkddDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:15:24.299263Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.03292","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f616e55209f9d240eaa8312ecef0b21fc6d464bc939eeb8b596c945721a5e757","sha256:92c7cc6b79aa5ef6d0418f23d4a0eaa51e51029c8f833fbd7c73b0e5e870f9d5"],"state_sha256":"8eccbd10b48c303b08abb5619b1e6576a6f2874bf95e520d9dc3715b7b6d718d"}