{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:KZTRKDSNKQ244MZIYG6CRFGKFP","short_pith_number":"pith:KZTRKDSN","canonical_record":{"source":{"id":"2607.16448","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-17T18:47:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c623427fbeaa1a7a587f758f1e58a9ef43df45c1ab9413f55e28fa9aa85e2467","abstract_canon_sha256":"90105bfdd3ffe7d3ab7e621733f3ce4d25bc5c2567b65c62f3c399e63801b53f"},"schema_version":"1.0"},"canonical_sha256":"5667150e4d5435ce3328c1bc2894ca2bf7541d373f17c03f5751d30fb0a84952","source":{"kind":"arxiv","id":"2607.16448","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.16448","created_at":"2026-07-21T00:20:19Z"},{"alias_kind":"arxiv_version","alias_value":"2607.16448v1","created_at":"2026-07-21T00:20:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.16448","created_at":"2026-07-21T00:20:19Z"},{"alias_kind":"pith_short_12","alias_value":"KZTRKDSNKQ24","created_at":"2026-07-21T00:20:19Z"},{"alias_kind":"pith_short_16","alias_value":"KZTRKDSNKQ244MZI","created_at":"2026-07-21T00:20:19Z"},{"alias_kind":"pith_short_8","alias_value":"KZTRKDSN","created_at":"2026-07-21T00:20:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:KZTRKDSNKQ244MZIYG6CRFGKFP","target":"record","payload":{"canonical_record":{"source":{"id":"2607.16448","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-17T18:47:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c623427fbeaa1a7a587f758f1e58a9ef43df45c1ab9413f55e28fa9aa85e2467","abstract_canon_sha256":"90105bfdd3ffe7d3ab7e621733f3ce4d25bc5c2567b65c62f3c399e63801b53f"},"schema_version":"1.0"},"canonical_sha256":"5667150e4d5435ce3328c1bc2894ca2bf7541d373f17c03f5751d30fb0a84952","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T00:20:19.202849Z","signature_b64":"Nke9MPda7SZpDptOurHkZoH7IH/a3AHaZlWOjEjeXnWa66Ax6Mx5VyTfL6EjcwLCQHL4ihhEdMbsJtOkNKNUCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5667150e4d5435ce3328c1bc2894ca2bf7541d373f17c03f5751d30fb0a84952","last_reissued_at":"2026-07-21T00:20:19.201980Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T00:20:19.201980Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.16448","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-21T00:20:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pTAbY2ZIe9Ms33yKGMZ44OUWWGq4Vcnf9ZJguQslJVVEYlw4koaSM6WIWeHt0nMdszWylcZvrahfo09W2qtuCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T23:08:19.092193Z"},"content_sha256":"308193b939562549fc2fe7adcd0b6bb6f556cb656420d37d4bbfc9a6ac03ba5f","schema_version":"1.0","event_id":"sha256:308193b939562549fc2fe7adcd0b6bb6f556cb656420d37d4bbfc9a6ac03ba5f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:KZTRKDSNKQ244MZIYG6CRFGKFP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Retrieval is Enough: Training-Free Interpretability with a Tool-Using Agent","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Soheil Feizi, Sriram Balasubramanian","submitted_at":"2026-07-17T18:47:31Z","abstract_excerpt":"Interpretability methods for neural network activations span a wide cost spectrum, from cheap, training-free techniques (such as linear probes, PCA, SVD) to more expensive training-based ones (such as SAEs and activation oracles). Training-based methods are typically more powerful, in part because they leverage large activation datasets during training. This raises a natural question - do they actually surface insights that go beyond what is recoverable from the training dataset itself? To address this, we equip an LLM agent with a vector database of activations paired with their textual conte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.16448","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/2607.16448/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":"2026-07-21T00:20:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rwAb+7GmFWOR5+c+jOCaBBLycIDSd3bKqCEVKVzZa/mdLWy9pB6GMH67cdc3jxPWoIWZZR/ey3TVRnGT9S/UDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T23:08:19.092759Z"},"content_sha256":"200461a02c2437630cb5d1bd2645543b33381450ceefaae45bbb794d0a39e303","schema_version":"1.0","event_id":"sha256:200461a02c2437630cb5d1bd2645543b33381450ceefaae45bbb794d0a39e303"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KZTRKDSNKQ244MZIYG6CRFGKFP/bundle.json","state_url":"https://pith.science/pith/KZTRKDSNKQ244MZIYG6CRFGKFP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KZTRKDSNKQ244MZIYG6CRFGKFP/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":"2026-08-18T23:08:19Z","links":{"resolver":"https://pith.science/pith/KZTRKDSNKQ244MZIYG6CRFGKFP","bundle":"https://pith.science/pith/KZTRKDSNKQ244MZIYG6CRFGKFP/bundle.json","state":"https://pith.science/pith/KZTRKDSNKQ244MZIYG6CRFGKFP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KZTRKDSNKQ244MZIYG6CRFGKFP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:KZTRKDSNKQ244MZIYG6CRFGKFP","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":"90105bfdd3ffe7d3ab7e621733f3ce4d25bc5c2567b65c62f3c399e63801b53f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-17T18:47:31Z","title_canon_sha256":"c623427fbeaa1a7a587f758f1e58a9ef43df45c1ab9413f55e28fa9aa85e2467"},"schema_version":"1.0","source":{"id":"2607.16448","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.16448","created_at":"2026-07-21T00:20:19Z"},{"alias_kind":"arxiv_version","alias_value":"2607.16448v1","created_at":"2026-07-21T00:20:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.16448","created_at":"2026-07-21T00:20:19Z"},{"alias_kind":"pith_short_12","alias_value":"KZTRKDSNKQ24","created_at":"2026-07-21T00:20:19Z"},{"alias_kind":"pith_short_16","alias_value":"KZTRKDSNKQ244MZI","created_at":"2026-07-21T00:20:19Z"},{"alias_kind":"pith_short_8","alias_value":"KZTRKDSN","created_at":"2026-07-21T00:20:19Z"}],"graph_snapshots":[{"event_id":"sha256:200461a02c2437630cb5d1bd2645543b33381450ceefaae45bbb794d0a39e303","target":"graph","created_at":"2026-07-21T00:20:19Z","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/2607.16448/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Interpretability methods for neural network activations span a wide cost spectrum, from cheap, training-free techniques (such as linear probes, PCA, SVD) to more expensive training-based ones (such as SAEs and activation oracles). Training-based methods are typically more powerful, in part because they leverage large activation datasets during training. This raises a natural question - do they actually surface insights that go beyond what is recoverable from the training dataset itself? To address this, we equip an LLM agent with a vector database of activations paired with their textual conte","authors_text":"Soheil Feizi, Sriram Balasubramanian","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-17T18:47:31Z","title":"Retrieval is Enough: Training-Free Interpretability with a Tool-Using Agent"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.16448","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":"sha256:308193b939562549fc2fe7adcd0b6bb6f556cb656420d37d4bbfc9a6ac03ba5f","target":"record","created_at":"2026-07-21T00:20:19Z","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":"90105bfdd3ffe7d3ab7e621733f3ce4d25bc5c2567b65c62f3c399e63801b53f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-17T18:47:31Z","title_canon_sha256":"c623427fbeaa1a7a587f758f1e58a9ef43df45c1ab9413f55e28fa9aa85e2467"},"schema_version":"1.0","source":{"id":"2607.16448","kind":"arxiv","version":1}},"canonical_sha256":"5667150e4d5435ce3328c1bc2894ca2bf7541d373f17c03f5751d30fb0a84952","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5667150e4d5435ce3328c1bc2894ca2bf7541d373f17c03f5751d30fb0a84952","first_computed_at":"2026-07-21T00:20:19.201980Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-21T00:20:19.201980Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Nke9MPda7SZpDptOurHkZoH7IH/a3AHaZlWOjEjeXnWa66Ax6Mx5VyTfL6EjcwLCQHL4ihhEdMbsJtOkNKNUCw==","signature_status":"signed_v1","signed_at":"2026-07-21T00:20:19.202849Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.16448","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:308193b939562549fc2fe7adcd0b6bb6f556cb656420d37d4bbfc9a6ac03ba5f","sha256:200461a02c2437630cb5d1bd2645543b33381450ceefaae45bbb794d0a39e303"],"state_sha256":"c3d4eee0c89f6c3f1a07295230a4c56945435e148d1a5109c56b79319ec2b5bd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SLdArx8L7T4ZIN+2bevN5lgrFfynhNWSOVzPynYa9WiD9bC+vK0C2UWOCmYnAvca6rBT2QDVts+1spp8V/GCCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T23:08:19.098145Z","bundle_sha256":"3bde24ae565a3c0601569f14c895611fe7de7184887f96dd6179aa734a97dea8"}}