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Fine-tuning and adaptation

LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.

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arXiv AI
Oct 2

Counterfactual Auditing of Bias in Open-Source Large Language Models for Clinical Triage

The study evaluates counterfactual bias in ten open‑source large language models (LLMs) for pediatric Emergency Severity Index (ESI) prediction. By creating paired clinical vignettes that differ only in demographic or socioeconomic variables, the authors measure shifts in acuity assignment, finding that counterfactual sensitivity varies widely across model families and sizes. A fine‑tuned Qwen2.5‑7B model exhibited the lowest sensitivity, while larger or medical‑domain models sometimes showed greater shifts, highlighting the need for fairness assessment before clinical deployment.

By Manar Aljohani, Brandon Ho, Kenneth McKinley, Dennis Ren, Xuan Wang
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arXiv Computer Vision
Oct 2

Omni-Embed-Mini: Binding Modalities Without Forgetting via Dense Distillation

Omni-Embed-Mini is a 0.9B‑parameter model that embeds text, speech, audio, images, video, and visually‑rich documents into a single shared cosine space without updating any text‑side parameters. It uses a dense cascaded caption as a teacher signal, allowing the teacher and student to share identical backbone weights and requiring only lightweight projectors and phased LoRA adapters for alignment. The model achieves strong text retrieval performance (49.57 nDCG@10 on MTEB‑v2 BEIR‑8) while extending to five additional modalities and is significantly smaller than other open omni‑modal embedders.

By Mohammed Irfan Kurpath, Jaseel Muhammad Kaithakkodan, Sahal Shaji Mullappilly, Ivan Laptev, Hisham Cholakkal
llmsragfine-tuningefficiencymultimodalsafety
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arXiv AI
Oct 2

Scores That Hold, Benchmarks That Leak: Measuring Dataset Contamination in Public Brain-Tumor MRI Classification

The paper examines dataset contamination in public brain‑tumor MRI classification benchmarks, introducing a three‑layer framework—duplicate, patient, and source‑label leakage—to evaluate integrity. Audits of the three most used corpora reveal severe leakage: up to 28.8% of test images duplicate training images, 22.3% are byte‑identical, and 95.5% share patients with training data. Even after removing all leaked images, model accuracy remains unchanged, indicating that benchmark integrity cannot be inferred from stable leaderboard scores alone.

By Bhanu Prakash Vangala, Sowmya Guda, Latha Peddi, Navya Vangala
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arXiv AI
Oct 2

Sequential Functional Structured Tucker Compression for Large Language Model Attentions

The paper introduces FTC, a sequential functional structured Tucker compression method for large language model attentions. FTC adapts its approximation to the current compressed model, jointly exploits the Q/K/V head structure, and treats the output projection separately to handle representation shifts. It achieves the lowest WikiText‑2 perplexity across seven decoder‑only LLMs (6B–32B) at all tested keep ratios, with significant gains under aggressive compression and benefits that transfer to downstream tasks.

By Jiangfeng Chen, Xinyu Wang, Tianshuo Yan, Hanwei Wu, Xiao-Wen Chang, Yang Zhang, Lei Ding
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arXiv AI
Oct 2

CineMR: Tool-Integrated Vision-Language Reasoning for Quantitative Cardiac MRI Assessment

CineMR is a vision‑language model that integrates cardiac image‑analysis tools to perform quantitative assessment of cine cardiac MRI. It uses supervised fine‑tuning and Group Relative Policy Optimization to learn reliable tool invocation, achieving significantly higher accuracy on a multi‑cohort benchmark than existing medical VLMs. The model demonstrates that tool‑augmented reasoning improves ventricular measurement accuracy by up to 23.7% and is essential for robust quantitative CMR interpretation.

By Kunyang Li, Hai Nguyen, Joshua Lowe, Chenguang Zhao, Peace C. Madueme, Mehdi Hedjazi Moghari, Mubarak Shah, Pegah Khosravi, Yuzhang Zhang
llmsagentscomputer-visionnlpfine-tuningmultimodalbenchmarks
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arXiv AI
Oct 2

Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching

The paper proposes an unsupervised domain adaptation technique for satellite radiometer images using conditional flow matching models. By leveraging deterministic ODE components of flow matching, the method aligns domains bijectively, preserving essential information across different satellite instruments. Experiments on the GPM-Core constellation demonstrate improved rain precipitation estimation from radiometer imagery.

By Victor Enescu, Assaad Zeghina, Matthieu Meignin, Nicolas Viltard, C\'ecile Mallet
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arXiv Machine Learning
Oct 2

QATFactory: A Versatile, Deployment-Aligned Framework for Quantization-aware Training and Distillation of LLMs

arXiv:2609.39223v2 Announce Type: new Abstract: Large language model (LLM) inference is increasingly moving toward lower precision to realize the throughput of hardware accelerators, but aggressive p...

By Weili Xu, Jisen Li, Yuqing Jian, Chenxi Li, Zhizhou Sha, Yifan Yu, Qingyang Wu, Chenfeng Xu, Zhongzhu Zhou, Tianyi Zhang, Ben Athiwaratkun
llmsreinforcement-learningfine-tuningefficiencybenchmarks
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arXiv Machine Learning
Oct 2

Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental Learning

arXiv:2609.39839v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. Parameter-efficient fine-tuni...

By Hongwei Zhao (School of Computer Science,Engineering, Beihang University), Rui Liu (School of Computer Science,Engineering, Beihang University), Yansong Liu (School of Computer Science,Engineering, Beihang University)
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