{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NJ46SXSGXXOG2LBRUIINVNWO6G","short_pith_number":"pith:NJ46SXSG","schema_version":"1.0","canonical_sha256":"6a79e95e46bddc6d2c31a210dab6cef1a9c98810f3730d08d46a79006c55e976","source":{"kind":"arxiv","id":"2404.09866","version":1},"attestation_state":"computed","paper":{"title":"Reimagining Self-Adaptation in the Age of Large Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Karthik Vaidhyanathan, Prakhar Jain, Raghav Donakanti, Shubham Kulkarni","submitted_at":"2024-04-15T15:30:12Z","abstract_excerpt":"Modern software systems are subjected to various types of uncertainties arising from context, environment, etc. To this end, self-adaptation techniques have been sought out as potential solutions. Although recent advances in self-adaptation through the use of ML techniques have demonstrated promising results, the capabilities are limited by constraints imposed by the ML techniques, such as the need for training samples, the ability to generalize, etc. Recent advancements in Generative AI (GenAI) open up new possibilities as it is trained on massive amounts of data, potentially enabling the int"},"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":"2404.09866","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.SE","submitted_at":"2024-04-15T15:30:12Z","cross_cats_sorted":[],"title_canon_sha256":"0fa36d9a91d33ed4050bcb051432db83a2999366334fcbd042f61ed35d740b13","abstract_canon_sha256":"53aed90a3f1264709846570a45f3eb0d937f0d916a3cdcbe7a123b8fd1848f49"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:08:12.626585Z","signature_b64":"xhyi0eiEtBWh1jQMgNtGWPKICW1Rd1vFGbPqrKRAARCmTSIHr3R+1h6IYQ8yp7+6xaKa2by07ET+MagcI2B5Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a79e95e46bddc6d2c31a210dab6cef1a9c98810f3730d08d46a79006c55e976","last_reissued_at":"2026-07-05T08:08:12.626105Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:08:12.626105Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reimagining Self-Adaptation in the Age of Large Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Karthik Vaidhyanathan, Prakhar Jain, Raghav Donakanti, Shubham Kulkarni","submitted_at":"2024-04-15T15:30:12Z","abstract_excerpt":"Modern software systems are subjected to various types of uncertainties arising from context, environment, etc. To this end, self-adaptation techniques have been sought out as potential solutions. Although recent advances in self-adaptation through the use of ML techniques have demonstrated promising results, the capabilities are limited by constraints imposed by the ML techniques, such as the need for training samples, the ability to generalize, etc. Recent advancements in Generative AI (GenAI) open up new possibilities as it is trained on massive amounts of data, potentially enabling the int"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.09866","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/2404.09866/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":"2404.09866","created_at":"2026-07-05T08:08:12.626164+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.09866v1","created_at":"2026-07-05T08:08:12.626164+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.09866","created_at":"2026-07-05T08:08:12.626164+00:00"},{"alias_kind":"pith_short_12","alias_value":"NJ46SXSGXXOG","created_at":"2026-07-05T08:08:12.626164+00:00"},{"alias_kind":"pith_short_16","alias_value":"NJ46SXSGXXOG2LBR","created_at":"2026-07-05T08:08:12.626164+00:00"},{"alias_kind":"pith_short_8","alias_value":"NJ46SXSG","created_at":"2026-07-05T08:08:12.626164+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.00064","citing_title":"Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NJ46SXSGXXOG2LBRUIINVNWO6G","json":"https://pith.science/pith/NJ46SXSGXXOG2LBRUIINVNWO6G.json","graph_json":"https://pith.science/api/pith-number/NJ46SXSGXXOG2LBRUIINVNWO6G/graph.json","events_json":"https://pith.science/api/pith-number/NJ46SXSGXXOG2LBRUIINVNWO6G/events.json","paper":"https://pith.science/paper/NJ46SXSG"},"agent_actions":{"view_html":"https://pith.science/pith/NJ46SXSGXXOG2LBRUIINVNWO6G","download_json":"https://pith.science/pith/NJ46SXSGXXOG2LBRUIINVNWO6G.json","view_paper":"https://pith.science/paper/NJ46SXSG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.09866&json=true","fetch_graph":"https://pith.science/api/pith-number/NJ46SXSGXXOG2LBRUIINVNWO6G/graph.json","fetch_events":"https://pith.science/api/pith-number/NJ46SXSGXXOG2LBRUIINVNWO6G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NJ46SXSGXXOG2LBRUIINVNWO6G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NJ46SXSGXXOG2LBRUIINVNWO6G/action/storage_attestation","attest_author":"https://pith.science/pith/NJ46SXSGXXOG2LBRUIINVNWO6G/action/author_attestation","sign_citation":"https://pith.science/pith/NJ46SXSGXXOG2LBRUIINVNWO6G/action/citation_signature","submit_replication":"https://pith.science/pith/NJ46SXSGXXOG2LBRUIINVNWO6G/action/replication_record"}},"created_at":"2026-07-05T08:08:12.626164+00:00","updated_at":"2026-07-05T08:08:12.626164+00:00"}