{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZEHD5TIYNSL5LZPR4NULTSNMYX","short_pith_number":"pith:ZEHD5TIY","schema_version":"1.0","canonical_sha256":"c90e3ecd186c97d5e5f1e368b9c9acc5c6fee3627f08260657ab6541a6429608","source":{"kind":"arxiv","id":"2508.17320","version":3},"attestation_state":"computed","paper":{"title":"AdaptiveK: Complexity-Driven Sparse Autoencoders for Interpretable Language Model Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hanrong Zhang, Mengnan Du, Yifei Yao","submitted_at":"2025-08-24T12:00:41Z","abstract_excerpt":"Understanding the internal representations of large language models (LLMs) remains a central challenge for interpretability research. Sparse autoencoders (SAEs) offer a promising solution by decomposing activations into interpretable features, but existing approaches rely on fixed sparsity constraints that fail to account for input complexity. We propose AdaptiveK SAE (Adaptive Top K Sparse Autoencoders), a novel framework that dynamically adjusts sparsity levels based on the semantic complexity of each input. Leveraging linear probes, we demonstrate that context complexity is linearly encoded"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2508.17320","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-24T12:00:41Z","cross_cats_sorted":[],"title_canon_sha256":"5bddfd990530e87d1cc431fb1a84e0263c11fe9c815903df307ecbd11cd6ec7c","abstract_canon_sha256":"dc81d1e526b59d4da601accf66c5e35c66230d0d14a49d430f6176cb79e22b6b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:08.204795Z","signature_b64":"c/iGw83WZ+PhcOIPceeqXzdRWTFyqUnkBqbijdWdPWFLUSTA3msSS+ei3PuoeJmLpzV89BzOt6QotN2w2XQ0CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c90e3ecd186c97d5e5f1e368b9c9acc5c6fee3627f08260657ab6541a6429608","last_reissued_at":"2026-06-02T02:04:08.204187Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:08.204187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdaptiveK: Complexity-Driven Sparse Autoencoders for Interpretable Language Model Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hanrong Zhang, Mengnan Du, Yifei Yao","submitted_at":"2025-08-24T12:00:41Z","abstract_excerpt":"Understanding the internal representations of large language models (LLMs) remains a central challenge for interpretability research. Sparse autoencoders (SAEs) offer a promising solution by decomposing activations into interpretable features, but existing approaches rely on fixed sparsity constraints that fail to account for input complexity. We propose AdaptiveK SAE (Adaptive Top K Sparse Autoencoders), a novel framework that dynamically adjusts sparsity levels based on the semantic complexity of each input. Leveraging linear probes, we demonstrate that context complexity is linearly encoded"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.17320","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.17320/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2508.17320","created_at":"2026-06-02T02:04:08.204263+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.17320v3","created_at":"2026-06-02T02:04:08.204263+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.17320","created_at":"2026-06-02T02:04:08.204263+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZEHD5TIYNSL5","created_at":"2026-06-02T02:04:08.204263+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZEHD5TIYNSL5LZPR","created_at":"2026-06-02T02:04:08.204263+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZEHD5TIY","created_at":"2026-06-02T02:04:08.204263+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZEHD5TIYNSL5LZPR4NULTSNMYX","json":"https://pith.science/pith/ZEHD5TIYNSL5LZPR4NULTSNMYX.json","graph_json":"https://pith.science/api/pith-number/ZEHD5TIYNSL5LZPR4NULTSNMYX/graph.json","events_json":"https://pith.science/api/pith-number/ZEHD5TIYNSL5LZPR4NULTSNMYX/events.json","paper":"https://pith.science/paper/ZEHD5TIY"},"agent_actions":{"view_html":"https://pith.science/pith/ZEHD5TIYNSL5LZPR4NULTSNMYX","download_json":"https://pith.science/pith/ZEHD5TIYNSL5LZPR4NULTSNMYX.json","view_paper":"https://pith.science/paper/ZEHD5TIY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.17320&json=true","fetch_graph":"https://pith.science/api/pith-number/ZEHD5TIYNSL5LZPR4NULTSNMYX/graph.json","fetch_events":"https://pith.science/api/pith-number/ZEHD5TIYNSL5LZPR4NULTSNMYX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZEHD5TIYNSL5LZPR4NULTSNMYX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZEHD5TIYNSL5LZPR4NULTSNMYX/action/storage_attestation","attest_author":"https://pith.science/pith/ZEHD5TIYNSL5LZPR4NULTSNMYX/action/author_attestation","sign_citation":"https://pith.science/pith/ZEHD5TIYNSL5LZPR4NULTSNMYX/action/citation_signature","submit_replication":"https://pith.science/pith/ZEHD5TIYNSL5LZPR4NULTSNMYX/action/replication_record"}},"created_at":"2026-06-02T02:04:08.204263+00:00","updated_at":"2026-06-02T02:04:08.204263+00:00"}