{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EX6QJBDVK5HG2UMWMLWRKHTISB","short_pith_number":"pith:EX6QJBDV","schema_version":"1.0","canonical_sha256":"25fd048475574e6d519662ed151e689077021de3ccff0d067ecdd189b50c554c","source":{"kind":"arxiv","id":"2508.15068","version":1},"attestation_state":"computed","paper":{"title":"S3LoRA: Safe Spectral Sharpness-Guided Pruning in Adaptation of Agent Planner","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Gopal Rumchurn, Shuang Ao","submitted_at":"2025-08-20T21:08:29Z","abstract_excerpt":"Adapting Large Language Models (LLMs) using parameter-efficient fine-tuning (PEFT) techniques such as LoRA has enabled powerful capabilities in LLM-based agents. However, these adaptations can unintentionally compromise safety alignment, leading to unsafe or unstable behaviors, particularly in agent planning tasks. Existing safety-aware adaptation methods often require access to both base and instruction-tuned model checkpoints, which are frequently unavailable in practice, limiting their applicability. We propose S3LoRA (Safe Spectral Sharpness-Guided Pruning LoRA), a lightweight, data-free, "},"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":"2508.15068","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-08-20T21:08:29Z","cross_cats_sorted":[],"title_canon_sha256":"c3f88fbda03dd89e95cdcc73f4d42055e7597a87b9eb4280c28c255d11a3e6f0","abstract_canon_sha256":"59d6040141f0abeae422e1ed2b80898f29a89b5ba769b64cd1e01af0def30e98"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:57:06.000409Z","signature_b64":"knSJaaPIxwxLImTmLTEMIP+hmpc0/ZIs/YlXrMIE+o2+/kT+oGvfX8egzBaPNRODa6n/+aAGGZP67JZGTMmbDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25fd048475574e6d519662ed151e689077021de3ccff0d067ecdd189b50c554c","last_reissued_at":"2026-07-05T11:57:05.999781Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:57:05.999781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"S3LoRA: Safe Spectral Sharpness-Guided Pruning in Adaptation of Agent Planner","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Gopal Rumchurn, Shuang Ao","submitted_at":"2025-08-20T21:08:29Z","abstract_excerpt":"Adapting Large Language Models (LLMs) using parameter-efficient fine-tuning (PEFT) techniques such as LoRA has enabled powerful capabilities in LLM-based agents. However, these adaptations can unintentionally compromise safety alignment, leading to unsafe or unstable behaviors, particularly in agent planning tasks. Existing safety-aware adaptation methods often require access to both base and instruction-tuned model checkpoints, which are frequently unavailable in practice, limiting their applicability. We propose S3LoRA (Safe Spectral Sharpness-Guided Pruning LoRA), a lightweight, data-free, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.15068","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/2508.15068/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":"2508.15068","created_at":"2026-07-05T11:57:05.999836+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.15068v1","created_at":"2026-07-05T11:57:05.999836+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.15068","created_at":"2026-07-05T11:57:05.999836+00:00"},{"alias_kind":"pith_short_12","alias_value":"EX6QJBDVK5HG","created_at":"2026-07-05T11:57:05.999836+00:00"},{"alias_kind":"pith_short_16","alias_value":"EX6QJBDVK5HG2UMW","created_at":"2026-07-05T11:57:05.999836+00:00"},{"alias_kind":"pith_short_8","alias_value":"EX6QJBDV","created_at":"2026-07-05T11:57:05.999836+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/EX6QJBDVK5HG2UMWMLWRKHTISB","json":"https://pith.science/pith/EX6QJBDVK5HG2UMWMLWRKHTISB.json","graph_json":"https://pith.science/api/pith-number/EX6QJBDVK5HG2UMWMLWRKHTISB/graph.json","events_json":"https://pith.science/api/pith-number/EX6QJBDVK5HG2UMWMLWRKHTISB/events.json","paper":"https://pith.science/paper/EX6QJBDV"},"agent_actions":{"view_html":"https://pith.science/pith/EX6QJBDVK5HG2UMWMLWRKHTISB","download_json":"https://pith.science/pith/EX6QJBDVK5HG2UMWMLWRKHTISB.json","view_paper":"https://pith.science/paper/EX6QJBDV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.15068&json=true","fetch_graph":"https://pith.science/api/pith-number/EX6QJBDVK5HG2UMWMLWRKHTISB/graph.json","fetch_events":"https://pith.science/api/pith-number/EX6QJBDVK5HG2UMWMLWRKHTISB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EX6QJBDVK5HG2UMWMLWRKHTISB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EX6QJBDVK5HG2UMWMLWRKHTISB/action/storage_attestation","attest_author":"https://pith.science/pith/EX6QJBDVK5HG2UMWMLWRKHTISB/action/author_attestation","sign_citation":"https://pith.science/pith/EX6QJBDVK5HG2UMWMLWRKHTISB/action/citation_signature","submit_replication":"https://pith.science/pith/EX6QJBDVK5HG2UMWMLWRKHTISB/action/replication_record"}},"created_at":"2026-07-05T11:57:05.999836+00:00","updated_at":"2026-07-05T11:57:05.999836+00:00"}