{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:DQDI5YG7HPPFD7XUVZNG35FHLW","short_pith_number":"pith:DQDI5YG7","canonical_record":{"source":{"id":"2505.15038","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-21T02:45:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9d92e654db5178568cdfaaba090d63ecc7a400cc7ec0160ef0630bb1156aea5a","abstract_canon_sha256":"166fccc59241f05f70483e5431e0a7a10c63a37c39ffa7cd88b3eb12766618f9"},"schema_version":"1.0"},"canonical_sha256":"1c068ee0df3bde51fef4ae5a6df4a75d97b7d8e19e377cbb5c351c37aa8ad19b","source":{"kind":"arxiv","id":"2505.15038","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.15038","created_at":"2026-07-05T11:45:14Z"},{"alias_kind":"arxiv_version","alias_value":"2505.15038v2","created_at":"2026-07-05T11:45:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15038","created_at":"2026-07-05T11:45:14Z"},{"alias_kind":"pith_short_12","alias_value":"DQDI5YG7HPPF","created_at":"2026-07-05T11:45:14Z"},{"alias_kind":"pith_short_16","alias_value":"DQDI5YG7HPPFD7XU","created_at":"2026-07-05T11:45:14Z"},{"alias_kind":"pith_short_8","alias_value":"DQDI5YG7","created_at":"2026-07-05T11:45:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:DQDI5YG7HPPFD7XUVZNG35FHLW","target":"record","payload":{"canonical_record":{"source":{"id":"2505.15038","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-21T02:45:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9d92e654db5178568cdfaaba090d63ecc7a400cc7ec0160ef0630bb1156aea5a","abstract_canon_sha256":"166fccc59241f05f70483e5431e0a7a10c63a37c39ffa7cd88b3eb12766618f9"},"schema_version":"1.0"},"canonical_sha256":"1c068ee0df3bde51fef4ae5a6df4a75d97b7d8e19e377cbb5c351c37aa8ad19b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:14.930392Z","signature_b64":"NTfvPR/z18SZyTJx6cXnSh4Ikk2NVywon+OAwJgqhkGp+mCu9x0Z2ivJCwF7oH9rx3LP2gdnAjnCFDQ6HbiVAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c068ee0df3bde51fef4ae5a6df4a75d97b7d8e19e377cbb5c351c37aa8ad19b","last_reissued_at":"2026-07-05T11:45:14.929854Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:14.929854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.15038","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:45:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZkjgfsTRa0OSys0QbbYd1hwcuAxJhtECvmkPHR9BKGFIBokI+Q7obm3yUq2P6oQiQAcas/0pnV/gF/mTADbwDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:40:22.012007Z"},"content_sha256":"efc72ee8010ac87ef62565a61da000b27fda6284b7b4261cc1f1bf3eb3e42c51","schema_version":"1.0","event_id":"sha256:efc72ee8010ac87ef62565a61da000b27fda6284b7b4261cc1f1bf3eb3e42c51"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:DQDI5YG7HPPFD7XUVZNG35FHLW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bo Shen, Fan Yang, Haiyan Zhao, Mengnan Du, Ninghao Liu, Xuansheng Wu","submitted_at":"2025-05-21T02:45:11Z","abstract_excerpt":"Linear concept vectors effectively steer LLMs, but existing methods suffer from noisy features in diverse datasets that undermine steering robustness. We propose Sparse Autoencoder-Denoised Concept Vectors (SDCV), which selectively keep the most discriminative SAE latents while reconstructing hidden representations. Our key insight is that concept-relevant signals can be explicitly separated from dataset noise by scaling up activations of top-k latents that best differentiate positive and negative samples. Applied to linear probing and difference-in-mean, SDCV consistently improves steering su"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.15038","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.15038/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T11:45:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qENWm+BfBDHvPDZzyOZGB4ZsVYW2GRIvhxvzpUxifNav+9BY//ov2pwivhI+klaPSHHI2TG0Y+0LT0ycf2OQAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:40:22.012537Z"},"content_sha256":"5d7107ac817b776739b51d4d86728244aa3b3b0db20e6d0ab9761e15b52f94e1","schema_version":"1.0","event_id":"sha256:5d7107ac817b776739b51d4d86728244aa3b3b0db20e6d0ab9761e15b52f94e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DQDI5YG7HPPFD7XUVZNG35FHLW/bundle.json","state_url":"https://pith.science/pith/DQDI5YG7HPPFD7XUVZNG35FHLW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DQDI5YG7HPPFD7XUVZNG35FHLW/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":"2026-08-09T01:40:22Z","links":{"resolver":"https://pith.science/pith/DQDI5YG7HPPFD7XUVZNG35FHLW","bundle":"https://pith.science/pith/DQDI5YG7HPPFD7XUVZNG35FHLW/bundle.json","state":"https://pith.science/pith/DQDI5YG7HPPFD7XUVZNG35FHLW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DQDI5YG7HPPFD7XUVZNG35FHLW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DQDI5YG7HPPFD7XUVZNG35FHLW","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":"166fccc59241f05f70483e5431e0a7a10c63a37c39ffa7cd88b3eb12766618f9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-21T02:45:11Z","title_canon_sha256":"9d92e654db5178568cdfaaba090d63ecc7a400cc7ec0160ef0630bb1156aea5a"},"schema_version":"1.0","source":{"id":"2505.15038","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.15038","created_at":"2026-07-05T11:45:14Z"},{"alias_kind":"arxiv_version","alias_value":"2505.15038v2","created_at":"2026-07-05T11:45:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15038","created_at":"2026-07-05T11:45:14Z"},{"alias_kind":"pith_short_12","alias_value":"DQDI5YG7HPPF","created_at":"2026-07-05T11:45:14Z"},{"alias_kind":"pith_short_16","alias_value":"DQDI5YG7HPPFD7XU","created_at":"2026-07-05T11:45:14Z"},{"alias_kind":"pith_short_8","alias_value":"DQDI5YG7","created_at":"2026-07-05T11:45:14Z"}],"graph_snapshots":[{"event_id":"sha256:5d7107ac817b776739b51d4d86728244aa3b3b0db20e6d0ab9761e15b52f94e1","target":"graph","created_at":"2026-07-05T11:45:14Z","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/2505.15038/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Linear concept vectors effectively steer LLMs, but existing methods suffer from noisy features in diverse datasets that undermine steering robustness. We propose Sparse Autoencoder-Denoised Concept Vectors (SDCV), which selectively keep the most discriminative SAE latents while reconstructing hidden representations. Our key insight is that concept-relevant signals can be explicitly separated from dataset noise by scaling up activations of top-k latents that best differentiate positive and negative samples. Applied to linear probing and difference-in-mean, SDCV consistently improves steering su","authors_text":"Bo Shen, Fan Yang, Haiyan Zhao, Mengnan Du, Ninghao Liu, Xuansheng Wu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-21T02:45:11Z","title":"Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.15038","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:efc72ee8010ac87ef62565a61da000b27fda6284b7b4261cc1f1bf3eb3e42c51","target":"record","created_at":"2026-07-05T11:45:14Z","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":"166fccc59241f05f70483e5431e0a7a10c63a37c39ffa7cd88b3eb12766618f9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-21T02:45:11Z","title_canon_sha256":"9d92e654db5178568cdfaaba090d63ecc7a400cc7ec0160ef0630bb1156aea5a"},"schema_version":"1.0","source":{"id":"2505.15038","kind":"arxiv","version":2}},"canonical_sha256":"1c068ee0df3bde51fef4ae5a6df4a75d97b7d8e19e377cbb5c351c37aa8ad19b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c068ee0df3bde51fef4ae5a6df4a75d97b7d8e19e377cbb5c351c37aa8ad19b","first_computed_at":"2026-07-05T11:45:14.929854Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:45:14.929854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NTfvPR/z18SZyTJx6cXnSh4Ikk2NVywon+OAwJgqhkGp+mCu9x0Z2ivJCwF7oH9rx3LP2gdnAjnCFDQ6HbiVAg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:45:14.930392Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.15038","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:efc72ee8010ac87ef62565a61da000b27fda6284b7b4261cc1f1bf3eb3e42c51","sha256:5d7107ac817b776739b51d4d86728244aa3b3b0db20e6d0ab9761e15b52f94e1"],"state_sha256":"549ca7275ade553370abdf5786adf6827796056e291f4d2b0e753616a085ef17"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rUIJ8+fqyZsRVwpKerlkrx41YCVMsr8KP2DaQ8sxnHBj0b/sjILJ/R7gOckjLkb9EvnhPX1inDET2H+c1F3tBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T01:40:22.017019Z","bundle_sha256":"6f255fee3eba7290a5a32e2a531b9a32eb2ee1128d48275dfe97469452c2fbad"}}