AutoAdapt is a modular framework that automatically discovers latent domains and trains per-domain Low‑Rank Adaptation (LoRA) adapters independently in parallel, without modifying other adapters. It uses parameter‑free routing to integrate new domains, achieving performance comparable to a LoRA adapter trained on all domains while avoiding full‑model retraining. The approach prevents domain interference by construction, enabling taxonomy‑free domain specialization across 14 benchmarks and GPT‑4o evaluations.
By Josh McGiff, Salma Mekaoui, Robert Shanahan, Nikola S. Nikolov
arXiv:2608. 05161v1 Announce Type: cross Abstract: Instruction-tuned LLMs are deployed into environments where domains evolve, yet extending a fine-tuned model's capabilities without full retraining remains an unsolved practical challenge.
By Josh McGiff, Salma Mekaoui, Robert Shanahan, Nikola S. Nikolov
The paper introduces Domain‑Specialized Upcycling (DS‑Upcycling), a method that expands a dense Transformer with domain‑specific experts and a router to improve multi‑domain adaptation. Unlike prior LoRA‑based adapters that either weight all domains equally or activate all experts, DS‑Upcycling trains a domain‑aware router to select the appropriate expert while freezing other parameters. Experiments demonstrate superior average performance across domains and high routing accuracy (90‑98%) with only 256 examples per domain.
By Mingzi Cao, Xi Wang, Nikolaos Aletras
arXiv:2407. 21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data.
By Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah
arXiv:2608. 16384v1 Announce Type: cross Abstract: Universal visual representations require adaptation mechanisms that adapt across heterogeneous domains without fragmenting knowledge into domain-specific modules.
By Suraj Yadav
HyperRepair is a framework that restores the general capabilities of fine‑tuned language models while keeping their domain specialization, using only inference time. It learns a hypernetwork from repairs on source models, which generates a Repairing Side Net (RSN) that selectively corrects the model based on activation fingerprints. Evaluated on two model families, 12 LoRA adaptation datasets, and 15 general benchmarks, HyperRepair retains 99.5% of fine‑tuned domain performance and 93.4% of the base model’s general performance.
By Junrong Tang, Jianghui Wang, Hao Kong, Yang Lin, Yibo Yang
arXiv:2607. 17467v1 Announce Type: cross Abstract: Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples.
By Siobhan Reid, Zhixiang Chi, Li Gu, Omid Reza Heidari, Ziqiang Wang, Yang Wang
arXiv:2609.37362v1 Announce Type: new
Abstract: Large language model (LLM) routing aims to assign each query to the most suitable model from a heterogeneous candidate pool, improving the quality--eff...
By Guannan Lai, Han-Jia Ye
arXiv:2608. 10804v1 Announce Type: cross Abstract: Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts.
By Qiang Wang, Songlin Dong, Shaokun Wang, Jizhou Han, Xiang Song, Chenhao Ding, Yuhang He, Yihong Gong
arXiv:2606.22272v2 Announce Type: replace
Abstract: Applying pre-trained language models to new domains through full fine-tuning is computationally expensive and prone to catastrophic forgetting. To...
By Mohammed Rawhani, Dervi\c{s} Karabo\u{g}a, \"Ozkan Ufuk Nalbanto\u{g}lu, Alper Ba\c{s}t\"urk, Bahriye Akay
The paper investigates whether the sparsity of Mixture-of-Experts (MoE) models leads to intrinsic semantic organization across modalities and domains. It shows that experts naturally specialize semantically even without explicit modular training. The authors propose ExpertLens, a data‑free method that decodes router weights to identify domain‑specialized experts, enabling selective fine‑tuning that matches or exceeds full fine‑tuning while updating only 21.7–47.0% of parameters and achieving a 4.0× speedup, outperforming LoRA in both performance and efficiency.
By Damiano Marsili, Raphi Kang, Aditya Mehta, Pietro Perona, Georgia Gkioxari
arXiv:2511. 18468v2 Announce Type: replace Abstract: Continual Test-Time Adaptation (CTTA) is crucial for deploying models in real-world applications with unseen, evolving target domains.
By Md Akil Raihan Iftee, Mir Sazzat Hossain, Rakibul Hasan Rajib, Tariq Iqbal, Md Mofijul Islam, M Ashraful Amin, Amin Ahsan Ali, AKM Mahbubur Rahman