LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv:2609.14523v1 Announce Type: new Abstract: Change visual question answering (Change VQA) requires understanding semantic changes across bi-temporal remote sensing images. Although vision languag...
arXiv:2609.14097v1 Announce Type: cross Abstract: Subtomogram classification in cryo-electron tomography (cryo-ET) is a challenging problem due to the scarcity of labeled examples. While cryo-ET simu...
arXiv:2609.14802v1 Announce Type: cross Abstract: Importance weights are essential in domain adaptation under label shift, yet their utility is often undermined by the finite sample uncertainty assoc...
The paper evaluates seven open‑source large language models for retrieval‑augmented generation in the ESG reporting domain, using 498 real‑world ESG reports from EU‑listed companies and 100 synthetic QA pairs. Performance is measured with RAGAS metrics, showing strong retrieval scores but variable generation quality, especially in faithfulness and factual correctness. The results highlight significant differences across model architectures and underscore the need for domain‑specific fine‑tuning to improve factual accuracy.
Abstract‑LoRA introduces a lightweight LoRA training approach that targets specific U‑Net blocks in diffusion models to improve single‑image style transfer. By refining block selection, adding more blocks, and using clustering‑based style abstraction, it better disentangles and balances style and content compared to prior methods like B‑LoRA. Experiments show that the method produces more harmonious artistic images while quantitatively preserving both style and content.
arXiv:2609.14764v1 Announce Type: cross Abstract: Early detection of cognitive decline from speech offers a scalable and non-invasive alternative to conventional clinical assessment. Verbal fluency t...
arXiv:2409.17685v3 Announce Type: replace Abstract: Hypomimia has drawn growing interest as a digital marker for screening Parkinson's disease (PD). However, developing reliable facial-expression-bas...
arXiv:2606.23843v2 Announce Type: replace Abstract: Vision-language models (VLMs) achieve strong cross-modal alignment but remain brittle to negation, often relying on shallow word associations rathe...
arXiv:2609.15130v1 Announce Type: cross Abstract: woma is a real-time foundation model for gastrointestinal endoscopy: a network trained without labels on about a million endoscopy frames, from which...
arXiv:2609.13151v1 Announce Type: new Abstract: Leading multilingual speech recognition models like Whisper transcribe diverse, low-resource languages without language-specific training but are compu...
arXiv:2609.14080v1 Announce Type: new Abstract: Promptable models such as microSAM segment electron microscopy (EM) images from point prompts, but automation requires generating prompts without user...
arXiv:2609.14226v1 Announce Type: new Abstract: Automated radiology report generation has advanced rapidly in diagnostic accuracy, yet generated reports frequently diverge from the stylistic conventi...
arXiv:2609.13969v1 Announce Type: new Abstract: Ptychographic phase reconstruction is commonly formulated as an iterative inverse problem, requiring repeated object-probe updates and resulting in sub...
arXiv:2609.14302v1 Announce Type: new Abstract: Can financial vision-language models (VLMs) turn chart evidence into reliable action recommendations? Existing hallucination evaluations are mostly cla...
arXiv:2609.14817v1 Announce Type: new Abstract: In Yor\`ub\'a, pitch alone separates \d{o}k\d{o} (husband, Mid), \d{o}k\d{\`o} (vehicle, Low), and \d{o}k\d{\'o} (hoe, High) -- the diacritics ARE the...
arXiv:2609.15743v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems, trained on paired speech-text data, have been improved by leveraging language models (LMs) trained on text-...
arXiv:2609.15886v1 Announce Type: new Abstract: Large language models (LLMs) often learn both desirable and undesirable properties during post-training. We study whether midtraining, an earlier train...
arXiv:2609.13486v1 Announce Type: cross Abstract: Recent work has shown that fine-tuning decoder-only large language models (LLMs) for retrieval yields strong first-stage retrievers, with effectivene...
arXiv:2404.00727v3 Announce Type: replace Abstract: All state-of-the-art coreference resolution (CR) models involve finetuning a pretrained language model. Whether the superior performance of one CR...
The paper introduces a self‑supervised, physics‑aware deep learning method for hyperspectral image restoration and super‑resolution in biomedical imaging. It achieves 16× pixel super‑resolution and 12× faster imaging without external training data, preserving biological integrity across synthetic and experimental samples. The approach also reveals disease‑associated metabolic changes and offers physical insights into the model’s workings, with all code released as open source.