{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ETQXKHTFE54BEP5PZRQPWBUYHF","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":"a01247b3a7fd9483936ac9386580d4ed51efd103b766feb220d51e666e47e716","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-07T03:14:39Z","title_canon_sha256":"1657b8a85903e810d5e2739ebbd907877cae6bd098e7e4d13ac971f4b1ae580e"},"schema_version":"1.0","source":{"id":"2501.03486","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.03486","created_at":"2026-07-05T09:57:55Z"},{"alias_kind":"arxiv_version","alias_value":"2501.03486v1","created_at":"2026-07-05T09:57:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.03486","created_at":"2026-07-05T09:57:55Z"},{"alias_kind":"pith_short_12","alias_value":"ETQXKHTFE54B","created_at":"2026-07-05T09:57:55Z"},{"alias_kind":"pith_short_16","alias_value":"ETQXKHTFE54BEP5P","created_at":"2026-07-05T09:57:55Z"},{"alias_kind":"pith_short_8","alias_value":"ETQXKHTF","created_at":"2026-07-05T09:57:55Z"}],"graph_snapshots":[{"event_id":"sha256:1dd8ecf478359f471354b1965a25d2b6c6c1cea2920bc9e1cbe1bfe074a87672","target":"graph","created_at":"2026-07-05T09:57:55Z","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/2501.03486/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The alignment of large language models (LLMs) with human values is critical as these models become increasingly integrated into various societal and decision-making processes. Traditional methods, such as reinforcement learning from human feedback (RLHF), achieve alignment by fine-tuning model parameters, but these approaches are often computationally expensive and impractical when models are frozen or inaccessible for parameter modification. In contrast, prompt optimization is a viable alternative to RLHF for LLM alignment. While the existing literature has shown empirical promise of prompt o","authors_text":"Amrit Singh Bedi, Avinash Reddy, George K. Atia, Prashant Trivedi, Souradip Chakraborty, Vaneet Aggarwal","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-07T03:14:39Z","title":"Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.03486","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":"sha256:8e65785002d036b56a9e577065626e2d7cb4a60c4c891113b08fbe3018ee1cf5","target":"record","created_at":"2026-07-05T09:57:55Z","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":"a01247b3a7fd9483936ac9386580d4ed51efd103b766feb220d51e666e47e716","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-07T03:14:39Z","title_canon_sha256":"1657b8a85903e810d5e2739ebbd907877cae6bd098e7e4d13ac971f4b1ae580e"},"schema_version":"1.0","source":{"id":"2501.03486","kind":"arxiv","version":1}},"canonical_sha256":"24e1751e652778123fafcc60fb0698396b022bc2cc8f708fb8f72601383cd92c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24e1751e652778123fafcc60fb0698396b022bc2cc8f708fb8f72601383cd92c","first_computed_at":"2026-07-05T09:57:55.526465Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:57:55.526465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mGVxQAiUWcAhilpkFQbk4VjZ7cclMmRm+BLgkAZoLbXdZP5P//Ia1CQV2zdwKubyvT1hnyI5b9nQbVGklfSvDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:57:55.526952Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.03486","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8e65785002d036b56a9e577065626e2d7cb4a60c4c891113b08fbe3018ee1cf5","sha256:1dd8ecf478359f471354b1965a25d2b6c6c1cea2920bc9e1cbe1bfe074a87672"],"state_sha256":"e9bf2210418e0d75534da5da4edd97614b17c9bc76a6a86394e21859f0526e61"}