{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UXQ3QGRPBFQRXMEWMQ2ZKPMR4U","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":"3403c7a90cbef4b741a1de1f9b668359bbad238e1c0f95069ad6530ec4bff5bb","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-16T20:33:06Z","title_canon_sha256":"3a55c9429016a7c3fd798800fb4ac7f6e0ac24afc490404f1f867686c6efd1de"},"schema_version":"1.0","source":{"id":"2410.13025","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.13025","created_at":"2026-07-05T09:42:31Z"},{"alias_kind":"arxiv_version","alias_value":"2410.13025v2","created_at":"2026-07-05T09:42:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13025","created_at":"2026-07-05T09:42:31Z"},{"alias_kind":"pith_short_12","alias_value":"UXQ3QGRPBFQR","created_at":"2026-07-05T09:42:31Z"},{"alias_kind":"pith_short_16","alias_value":"UXQ3QGRPBFQRXMEW","created_at":"2026-07-05T09:42:31Z"},{"alias_kind":"pith_short_8","alias_value":"UXQ3QGRP","created_at":"2026-07-05T09:42:31Z"}],"graph_snapshots":[{"event_id":"sha256:922e94b010424dbd4657f7de8cf6741bde9a738278ee2cdb13cef428b3be4547","target":"graph","created_at":"2026-07-05T09:42:31Z","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/2410.13025/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-Rank Adaptation (LoRA) is a popular technique for parameter-efficient fine-tuning of Large Language Models (LLMs). We study how different LoRA modules can be merged to achieve skill composition -- testing the performance of the merged model on a target task that involves combining multiple skills, each skill coming from a single LoRA. This setup is favorable when it is difficult to obtain training data for the target task and when it can be decomposed into multiple skills. First, we identify practically occurring use-cases that can be studied under the realm of skill composition, e.g. solv","authors_text":"Akshara Prabhakar, Eran Malach, Karthik Narasimhan, Samy Jelassi, Sham Kakade, Yuanzhi Li","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-16T20:33:06Z","title":"LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13025","kind":"arxiv","version":2},"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:e02eeb5a3c7192d8af73d81a4fb697f6f3cbd79c08ef0bb45a84d53d4f31f96e","target":"record","created_at":"2026-07-05T09:42:31Z","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":"3403c7a90cbef4b741a1de1f9b668359bbad238e1c0f95069ad6530ec4bff5bb","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-16T20:33:06Z","title_canon_sha256":"3a55c9429016a7c3fd798800fb4ac7f6e0ac24afc490404f1f867686c6efd1de"},"schema_version":"1.0","source":{"id":"2410.13025","kind":"arxiv","version":2}},"canonical_sha256":"a5e1b81a2f09611bb0966435953d91e50fcf757952b8c721cdfd77ede47d3f62","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5e1b81a2f09611bb0966435953d91e50fcf757952b8c721cdfd77ede47d3f62","first_computed_at":"2026-07-05T09:42:31.288654Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:42:31.288654Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"d7Z6Z/h6rDR7lpJMqs9xD92S8OweFYUuqu/O62ywVACukTCJZMLGdcFIGYJT0fY7yXCfNO3LZH/4j1QfXqQ3DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:42:31.289212Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.13025","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e02eeb5a3c7192d8af73d81a4fb697f6f3cbd79c08ef0bb45a84d53d4f31f96e","sha256:922e94b010424dbd4657f7de8cf6741bde9a738278ee2cdb13cef428b3be4547"],"state_sha256":"a78fe9e6dc261a97118eb3f0fda65d94f24c853597a8a4174e9b30aa877cc549"}