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 Machine Learning

1d ago

Structuring Semantic Embeddings for Principle Evaluation: A Prototype-Guided Contrastive Learning Approach

arXiv:2608. 15224v1 Announce Type: new Abstract: Reliable post-hoc evaluation asks whether already generated text satisfies a target criterion after generation.

By Che Shen, Junwei Su, Lingpeng Kong, Chuan Wu

llms rag fine-tuning benchmarks safety

arXiv Machine Learning

1d ago

Fiber Fingerprints of Hidden Learning-State Dynamics

arXiv:2608. 15976v1 Announce Type: new Abstract: A learning system can occupy execution states that are indistinguishable under every declared present-behavior readout yet respond differently to future training.

By Qinyou Wang

llms fine-tuning

arXiv AI

1d ago

DirMixE: Harnessing Test Agnostic Long-tail Recognition with Hierarchical Label Variations

arXiv:2405. 07780v3 Announce Type: replace-cross Abstract: This paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbitrarily imbalanced.

By Zhiyong Yang, Qianqian Xu, Sicong Li, Zitai Wang, Xiaochun Cao, Qingming Huang

fine-tuning

arXiv AI

1d ago

OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing

arXiv:2509. 24900v2 Announce Type: replace-cross Abstract: The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensiveness of their training data.

By Zhihong Chen, Xuehai Bai, Yang Shi, Chaoyou Fu, Huanyu Zhang, Haotian Wang, Xiaoyan Sun, Zhang Zhang, Liang Wang, Yuanxing Zhang, Pengfei Wan, Yi-Fan Zhang

llms diffusion robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

1d ago

Spectral Rank Certification for Foundation Model Adapters

arXiv:2608. 15351v1 Announce Type: new Abstract: Nominal LoRA rank is a design parameter; calibrated spectral evidence is a separate inferential quantity.

By Mohammed Ahnouch, Lotfi Elaachak

fine-tuning

arXiv Machine Learning

1d ago

Multi-Feature Riemannian Hypergraph for Online Test-Time Adaptation of Motor Imagery Brain-Computer Interface

arXiv:2608. 16134v1 Announce Type: new Abstract: In clinical motor imagery brain-computer interface (MI-BCI) decoding, cross-day transferability and online operation remain two critical challenges.

By Siqi Li (Peking University, Chinese Institute for Brain Research, Beijing), Zhi Li (NeuCyber Neurotech), Tong Liu (NeuCyber Neurotech), Shuai Zhang (NeuCyber Neurotech), Yanfei Jia (Beijing Medical University), Zhiqiang Yi (Beijing Medical University), Jue Xie (NeuCyber Neurotech), Ni Ji (Chinese Academy of Medical Sciences & Peking Union Medical College, Chinese Institute for Brain Research, Beijing)

fine-tuning benchmarks safety

arXiv Machine Learning

1d ago

Misconception Diagnosis From Student-Tutor Dialogue: Generate, Retrieve, Rerank

arXiv:2602. 02414v2 Announce Type: replace-cross Abstract: Timely and accurate identification of student misconceptions is key to improving learning outcomes and pre-empting the compounding of student errors.

By Joshua Mitton, Prarthana Bhattacharyya, Digory Smith, Thomas Christie, Ralph Abboud, Simon Woodhead

llms rag fine-tuning benchmarks

arXiv AI

1d ago

MetaReason: Precise Interleaved Multimodal Reasoning via Editing Meta Information for Solving Geometry Problems

arXiv:2608. 15006v1 Announce Type: cross Abstract: Although visual reasoning is crucial for solving complex geometry tasks, existing vision-language models rely heavily on text-only reasoning.

By Penghao Yin, Haomin Wang, Qihong Tang, Xiaoye Qu, Hongjie Zhang, Xiao-Ping Zhang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

1d ago

Learning Generalizable Reconstruction of High-Dimensional Neural Dynamics

arXiv:2608. 16569v1 Announce Type: new Abstract: Accurate reconstruction of long-duration neural recordings is challenging because local field potentials (LFPs) are high-resolution, multichannel, transient, and variable across subjects.

By Anima Kujur, Zahra Monfared

diffusion fine-tuning

arXiv AI

1d ago

PhaseLoRA: Control-Regime-Conditioned Low-Rank Adaptation for Continuous-Action Vision-Language-Action Policies

arXiv:2608. 15285v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) is a natural way to adapt pretrained vision-language-action (VLA) policies, but most adapter designs apply temporally static updates throughout a control rollout, overlooking the phase-dependent nature of continuous-action manipulation.

By Yufei Guo, Yinan Wu, Haoran Duan, Guiguang Ding, Jungong Han

robotics fine-tuning multimodal

arXiv AI

1d ago

SAPE: Sandwich Adapters for Parameter Efficiency in Large Language Model Fine-Tuning

arXiv:2608. 15360v1 Announce Type: cross Abstract: While Parameter-Efficient Fine-Tuning (PEFT) has substantially reduced the hardware cost of adapting Large Language Models (LLMs) by decreasing the number of trainable parameters, recent studies have sought to further improve PEFT through parameter sharing.

By Mohammad Aref Jafari-Raddani, Morteza Mohajjel Kafshdooz

llms rag fine-tuning benchmarks

arXiv AI

1d ago

FluxBin: Flexible LUT-based Ultra-low-bit LLM Inference by Algorithm-Kernel Synergy

arXiv:2608. 15602v1 Announce Type: cross Abstract: While binary quantization theoretically promises extreme compression and acceleration for Large Language Models (LLMs), existing research often overlooks the necessity of specialized hardware kernels, thus failing to unleash the full acceleration potential due to persistent reliance on expensive floating-point arithmetic or runtime dequantization overheads.

By Qingyao Yang, Runming Yang, He Xiao, Wendong Xu, Junyu Chen, Haobo Liu, Chenchen Ding, Ruihan Hu, Yik-Chung Wu, Ngai Wong

llms fine-tuning efficiency

arXiv AI

1d ago

ML-AutoResearch: Training Machine Learning Research Agents with Automatically Generated Environments

arXiv:2603. 17216v2 Announce Type: replace Abstract: With the advent of AI agents, automated scientific discovery is becoming an increasingly plausible goal.

By Ziyang Cai, Amir Saeidi, Harkirat Behl

agents fine-tuning benchmarks

arXiv AI

1d ago

VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models

arXiv:2603. 24575v2 Announce Type: replace-cross Abstract: Scalable Vector Graphics (SVG) are essential for technical illustration and digital design, offering resolution independence and semantic editability.

By Qijia He, Xunmei Liu, Hammaad Memon, Ziang Li, Zixian Ma, Jaemin Cho, Zhongzheng Ren, Daniel S Weld, Ranjay Krishna

llms rag reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

1d ago

UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations

arXiv:2608. 15930v1 Announce Type: new Abstract: Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliable execution.

By Zihan Ding, Longxu Dou, Qi Gao, Xiangwu Guo, Shengchao Hu, Zilong Huang, Zihang Jiang, Lei Ke, Mengcheng Lan, Weixian Lei, Hanxuan Li, Honglin Li, Xiyun Li, Zaitang Li, Leowei Liang, Xin Luo, Haozhe Ma, Jiayi Mao, Zhoujie Pan, Can Qin, Tianyuan Qu, Weiqi Wang, Wenkai Wang, Yonglin Wang, Yuxin Wang, Chenxu Wu, Yingchen Yu, Chenyu Zhang, Yuhao Zheng

agents reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

1d ago

Take it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection

arXiv:2608. 14675v1 Announce Type: cross Abstract: While Self-Supervised Learning (SSL) effectively extracts general representations from noisy, unconstrained physiological signals such as photoplethysmography (PPG), its suitability for highly subjective tasks remains unproven.

By Dominika Kunc, Przemys{\l}aw Kazienko, Stanis{\l}aw Saganowski

reinforcement-learning fine-tuning

arXiv Machine Learning

1d ago

Discrete Diffusion Language Models Are Training-Free Multi-Label Classifiers

arXiv:2608. 14649v1 Announce Type: new Abstract: We present dLLM-SetScore, a training-free method that uses discrete masked-diffusion language models for multi-label text classification.

By Pawan Kumar

llms diffusion nlp fine-tuning

arXiv AI

1d ago

MedMCP-Calc: Benchmarking LLMs for Realistic Medical Calculator Scenarios via MCP Integration

arXiv:2601. 23049v2 Announce Type: replace Abstract: Medical calculators are fundamental to quantitative, evidence-based clinical practice.

By Yakun Zhu, Yutong Huang, Shengqian Qin, Zhongzhen Huang, Shaoting Zhang, Xiaofan Zhang

llms agents fine-tuning benchmarks data-engineering

arXiv AI

1d ago

Characterising cardiac tissue properties with graph neural networks

arXiv:2608. 15843v1 Announce Type: cross Abstract: Characterising electrophysiological properties of cardiac tissue efficiently and accurately from spatially sparse intracardiac measurements is clinically important for localising ablation targets and improving arrhythmia treatment.

By Ching-En Chiu, Yoo Ri Kim, Magdi Saba, Danilo Mandic, Marta Varela

fine-tuning benchmarks

arXiv AI

1d ago

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

arXiv:2506. 07449v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG) frameworks.

By Vahid Azizi, Fatemeh Koochaki

llms rag fine-tuning