{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:DMKSVJL6OCYOI6OLUC5H7Q6NA4","short_pith_number":"pith:DMKSVJL6","schema_version":"1.0","canonical_sha256":"1b152aa57e70b0e479cba0ba7fc3cd072ec6c044383212312ad33438bc946e7d","source":{"kind":"arxiv","id":"2606.24245","version":1},"attestation_state":"computed","paper":{"title":"AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.SE","authors_text":"Pingchuan Ma, Shuai Wang, Xiaoqin Zhang, Yuguang Zhou, Zhantong Xue, Zhaoyu Wang, Zimo Ji, Zongjie Li","submitted_at":"2026-06-23T07:31:03Z","abstract_excerpt":"Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments. However, their autonomy poses significant safety risks: agents may execute destructive commands, leak sensitive data, or violate domain constraints. Existing safety approaches face a fundamental tradeoff: hand-crafted rules are interpretable but brittle, with overly conservative rules blocking safe operations (high false positives) while permissive rules miss unsafe behaviors (high false negatives). Neural classifiers lack the interpretability required for "},"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":"2606.24245","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2026-06-23T07:31:03Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"8608a96d224f464897a82d89d76d8e16b215d32993e86d9d11eb088f8c089425","abstract_canon_sha256":"ebce7883be74d90f408babff4a769baf9da5d2fcd2ef1ffff030ee58ea4daa75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:14:47.890655Z","signature_b64":"OS4/pcscyquXRz1GLnTeHqMF9T1yP79uPu00a08cDgp7t8p34aOqXs48nuce/l31/F44K1Lq3YMJI0xP9l58CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b152aa57e70b0e479cba0ba7fc3cd072ec6c044383212312ad33438bc946e7d","last_reissued_at":"2026-06-24T01:14:47.890182Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:14:47.890182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.SE","authors_text":"Pingchuan Ma, Shuai Wang, Xiaoqin Zhang, Yuguang Zhou, Zhantong Xue, Zhaoyu Wang, Zimo Ji, Zongjie Li","submitted_at":"2026-06-23T07:31:03Z","abstract_excerpt":"Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments. However, their autonomy poses significant safety risks: agents may execute destructive commands, leak sensitive data, or violate domain constraints. Existing safety approaches face a fundamental tradeoff: hand-crafted rules are interpretable but brittle, with overly conservative rules blocking safe operations (high false positives) while permissive rules miss unsafe behaviors (high false negatives). Neural classifiers lack the interpretability required for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24245","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/2606.24245/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":"2606.24245","created_at":"2026-06-24T01:14:47.890240+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.24245v1","created_at":"2026-06-24T01:14:47.890240+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24245","created_at":"2026-06-24T01:14:47.890240+00:00"},{"alias_kind":"pith_short_12","alias_value":"DMKSVJL6OCYO","created_at":"2026-06-24T01:14:47.890240+00:00"},{"alias_kind":"pith_short_16","alias_value":"DMKSVJL6OCYOI6OL","created_at":"2026-06-24T01:14:47.890240+00:00"},{"alias_kind":"pith_short_8","alias_value":"DMKSVJL6","created_at":"2026-06-24T01:14:47.890240+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/DMKSVJL6OCYOI6OLUC5H7Q6NA4","json":"https://pith.science/pith/DMKSVJL6OCYOI6OLUC5H7Q6NA4.json","graph_json":"https://pith.science/api/pith-number/DMKSVJL6OCYOI6OLUC5H7Q6NA4/graph.json","events_json":"https://pith.science/api/pith-number/DMKSVJL6OCYOI6OLUC5H7Q6NA4/events.json","paper":"https://pith.science/paper/DMKSVJL6"},"agent_actions":{"view_html":"https://pith.science/pith/DMKSVJL6OCYOI6OLUC5H7Q6NA4","download_json":"https://pith.science/pith/DMKSVJL6OCYOI6OLUC5H7Q6NA4.json","view_paper":"https://pith.science/paper/DMKSVJL6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.24245&json=true","fetch_graph":"https://pith.science/api/pith-number/DMKSVJL6OCYOI6OLUC5H7Q6NA4/graph.json","fetch_events":"https://pith.science/api/pith-number/DMKSVJL6OCYOI6OLUC5H7Q6NA4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DMKSVJL6OCYOI6OLUC5H7Q6NA4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DMKSVJL6OCYOI6OLUC5H7Q6NA4/action/storage_attestation","attest_author":"https://pith.science/pith/DMKSVJL6OCYOI6OLUC5H7Q6NA4/action/author_attestation","sign_citation":"https://pith.science/pith/DMKSVJL6OCYOI6OLUC5H7Q6NA4/action/citation_signature","submit_replication":"https://pith.science/pith/DMKSVJL6OCYOI6OLUC5H7Q6NA4/action/replication_record"}},"created_at":"2026-06-24T01:14:47.890240+00:00","updated_at":"2026-06-24T01:14:47.890240+00:00"}