{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JLDQHHA6SEZGHHY5X32E7G5FQB","short_pith_number":"pith:JLDQHHA6","schema_version":"1.0","canonical_sha256":"4ac7039c1e9132639f1dbef44f9ba5807a71ed03f26a98212a7a9a051b3ae1c8","source":{"kind":"arxiv","id":"2607.04222","version":1},"attestation_state":"computed","paper":{"title":"Unsupervised Features Mining via Activation Geometry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Amit Levi, Elad David, Max Fomin","submitted_at":"2026-07-05T10:28:04Z","abstract_excerpt":"Interpretability methods aim to reveal the features represented inside large language models (LLMs). Many existing methods begin with labeled examples of a human-defined concept that may reflect human biases, and then identify how that concept is represented within the model, for example in its activation space or through other decomposition methods. We introduce \\emph{Mining via Activation Geometry} (MAG), a simple unsupervised framework for extracting reasoning features from model activations by prepending the same natural-language instruction $Q$ to every input $p$, where $Q$ defines the re"},"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":"2607.04222","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-05T10:28:04Z","cross_cats_sorted":[],"title_canon_sha256":"80e37c7688b02addeb1efa4ae5756c0a6891953cad7a20daeac847bdcb4fa485","abstract_canon_sha256":"be95c72c545aae89222e47f869942815219f4e0e106eaab58d72ebd38852f33d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:19:03.770525Z","signature_b64":"bQqWMApQ5X3P2/JK/ty/dNpoedXz2ooV6F1Mg+yQm0kR4VDmO19cv00Q3N9JY5ZBF35XvLPjnXddabEse+jHBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ac7039c1e9132639f1dbef44f9ba5807a71ed03f26a98212a7a9a051b3ae1c8","last_reissued_at":"2026-07-07T02:19:03.769526Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:19:03.769526Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Features Mining via Activation Geometry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Amit Levi, Elad David, Max Fomin","submitted_at":"2026-07-05T10:28:04Z","abstract_excerpt":"Interpretability methods aim to reveal the features represented inside large language models (LLMs). Many existing methods begin with labeled examples of a human-defined concept that may reflect human biases, and then identify how that concept is represented within the model, for example in its activation space or through other decomposition methods. We introduce \\emph{Mining via Activation Geometry} (MAG), a simple unsupervised framework for extracting reasoning features from model activations by prepending the same natural-language instruction $Q$ to every input $p$, where $Q$ defines the re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.04222","kind":"arxiv","version":1},"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/2607.04222/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":"2607.04222","created_at":"2026-07-07T02:19:03.769721+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.04222v1","created_at":"2026-07-07T02:19:03.769721+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.04222","created_at":"2026-07-07T02:19:03.769721+00:00"},{"alias_kind":"pith_short_12","alias_value":"JLDQHHA6SEZG","created_at":"2026-07-07T02:19:03.769721+00:00"},{"alias_kind":"pith_short_16","alias_value":"JLDQHHA6SEZGHHY5","created_at":"2026-07-07T02:19:03.769721+00:00"},{"alias_kind":"pith_short_8","alias_value":"JLDQHHA6","created_at":"2026-07-07T02:19:03.769721+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/JLDQHHA6SEZGHHY5X32E7G5FQB","json":"https://pith.science/pith/JLDQHHA6SEZGHHY5X32E7G5FQB.json","graph_json":"https://pith.science/api/pith-number/JLDQHHA6SEZGHHY5X32E7G5FQB/graph.json","events_json":"https://pith.science/api/pith-number/JLDQHHA6SEZGHHY5X32E7G5FQB/events.json","paper":"https://pith.science/paper/JLDQHHA6"},"agent_actions":{"view_html":"https://pith.science/pith/JLDQHHA6SEZGHHY5X32E7G5FQB","download_json":"https://pith.science/pith/JLDQHHA6SEZGHHY5X32E7G5FQB.json","view_paper":"https://pith.science/paper/JLDQHHA6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.04222&json=true","fetch_graph":"https://pith.science/api/pith-number/JLDQHHA6SEZGHHY5X32E7G5FQB/graph.json","fetch_events":"https://pith.science/api/pith-number/JLDQHHA6SEZGHHY5X32E7G5FQB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JLDQHHA6SEZGHHY5X32E7G5FQB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JLDQHHA6SEZGHHY5X32E7G5FQB/action/storage_attestation","attest_author":"https://pith.science/pith/JLDQHHA6SEZGHHY5X32E7G5FQB/action/author_attestation","sign_citation":"https://pith.science/pith/JLDQHHA6SEZGHHY5X32E7G5FQB/action/citation_signature","submit_replication":"https://pith.science/pith/JLDQHHA6SEZGHHY5X32E7G5FQB/action/replication_record"}},"created_at":"2026-07-07T02:19:03.769721+00:00","updated_at":"2026-07-07T02:19:03.769721+00:00"}