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Nevertheless, they often struggle when applied to low-resource tasks. In case in-domain labeled data are scarce or mismatched with the true test distribution, direct fine-tuning can be brittle. In-Context Learning (ICL) provides a training-free, inference-time solution by adapting auditory LLMs through conditioning on a few in-domain demonstrations. In this work, we first show that $\\textit{Vanilla ICL}$, improves zero-shot performance across diverse speech and audio tasks for selected models which suggest that this ICL adaptation capability can be generalized to multimodal setting. Building on this, we propose $\\textbf{Meta Speech In-Context Learning (MetaSICL)}$, a post-training recipe utilizes only high resource speech data from various tasks intending to strengthen model's in-context learni","title":"MetaSICL: Adapting Audiroty LLM via Meta Speech In-Context Learning","url":"https://arxiv.org/abs/2601.18904","vendor":"arxiv_cs_ai"},"summary":"arXiv:2601.18904v2 Announce Type: replace-cross \nAbstract: Auditory Large Language Models (LLMs) have demonstrated strong performance across a wide range of speech and audio understanding tasks. Nevertheless, they often struggle when applied to low-resource tasks. In case in-domain labeled data are scarce or mismatched with the true test distribution, direct fine-tuning can be brittle. In-Context Learning (ICL) provides a training-free, inference-time solution by adapting auditory LLMs through conditioning on a few in-domain demonstrations. In this work, we first show that $\\textit{Vanilla ICL}$, improves zero-shot performance across diverse speech and audio tasks for selected models which suggest that this ICL adaptation capability can be generalized to multimodal setting. Building on this, we propose $\\textbf{Meta Speech In-Context Learning (MetaSICL)}$, a post-training recipe utilizes only high resource speech data from various tasks intending to strengthen model's in-context learni","title":"MetaSICL: Adapting Audiroty LLM via Meta Speech In-Context Learning","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-27T04:43:18Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2601.18904"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:6edb69e59da2465437ff6180cd151e268638bec3d64ca0508171fa13c0737e9a3cad210b00163c3c88ec2a86ec3c3344040f40be208039c99afb3d9d21c8ee07","signer":"crovia.substrate","subject":{"observed_at":"2026-05-27T04:43:18Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2601.18904"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"05cc9c08cb21fb8685f1b718a0fb33188a91783039d53006d07d364cf4d075df","leaf_index":153919,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"c70d5156a68581d5acf779c3884fc13d9fffa28263a1ff860c0b99a999edfc38","side":"left"},{"sibling":"4b6e83dda7fdd6d24a4f0e0707b9bf818049230fa512463e2fb89b9f581c6e95","side":"left"},{"sibling":"c5d0230e7bd55ce746f2298da6fb01c2000c44995e5533759e105744270664ef","side":"left"},{"sibling":"be30c1cb70c4d1bc15838702f422db86ed4ed594763192c0f89a21680155d6c1","side":"left"},{"sibling":"ff3efd1e92f468a691f6e8332b3bb38a5ad76589cc58815d5d86761a8bbe63c4","side":"left"},{"sibling":"6644087266f827f79c1b05a55e9f69362c3695a15e4a1b4d950c8f57634f5521","side":"left"},{"sibling":"62e24c574a8b11b6817a5dfdbf87ff01cb3206c901cc1834307cc59fc60392d5","side":"right"},{"sibling":"16c794666e8ffc4b9e71a0bacc0e4f9bbc68a8544c340b385c6d1abff1d748e5","side":"right"},{"sibling":"c14a4eb4b07d22a6a83f548cf6c304559dc63f0e53071e550ecc566dbba50b14","side":"left"},{"sibling":"3165125427a29042fc9d02858a59a68858dbdcb2e19d1afa5f6dd6a95cfbce6a","side":"right"},{"sibling":"04b9a68b8ec6fa37251564383c685c23ce69e5e031df4eae69f79a3a334b68bf","side":"right"},{"sibling":"816f233274bb10f5a122aac086a0c8c697b78fec67a4af55190bb596b7506fab","side":"left"},{"sibling":"d415e6939aee710631f5062799379b547d2c3e3d9a68f263bbb5a693285ab2ca","side":"left"},{"sibling":"374c02d15fb12bd356c179c94766043a982052c6132af8bfc15361b431ffa9f7","side":"right"},{"sibling":"35ca36cee447f0ef7064a25d55f59357c66901e31427729c4c1d8b14aa8adb6c","side":"left"},{"sibling":"990705096edf483cc877217308f731dc42d6f6d99f82880167bbdbbfef32560a","side":"right"},{"sibling":"dd265753d95fa2e2fb4f5768e37fab6f691ccff09ad60d0910ff7dc23bac9226","side":"right"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":154065,"merkle_root":"4993cfdc172e7880b60667f16789dc2e831ff000f81bb1ecba248e73f1510eca","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260527T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-27T05:37:36Z","sig_algorithm":"ed25519","signature":"76ecf118011540405e96506e6219752df04a2850632f2903dc6f10e08b98bc5a42c8d9e1cb5b7c5bf714479806a403df5f34399afa40c23fbb71493a1f77bd0c","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_78adbe9b6354df104a8f2e9cbbd2d95a7f5ab9b1aabc3212ccc3b666c3c2868a"}}