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

Jul 16

Screening of Biosecurity Features in Metagenomic Data with Evo 2 Probes

arXiv:2607. 14070v1 Announce Type: cross Abstract: Genomic foundation models such as Evo 2 learn rich sequence representations, but their value for biosecurity screening is largely unexplored.

By Jeremy Guntoro, Alexander Dack, Dylan Danno, Michaela Jan\v{c}ovi\v{c}ov\'a, Kri\v{z}an Jurinovi\'c, Vanessa Smilansky

rag fine-tuning

arXiv AI

Jul 16

The Perplexity Trap: When Patent Law Makes Human Writing Look Like AI

arXiv:2607. 13044v1 Announce Type: cross Abstract: The European Patent Office (EPO) reported record filings in 2025, and the 2026 EPO Guidelines hold applicants strictly responsible for LLM-assisted content under Article 83 and Rule 42, creating pressure to triage suspected AI-generated patent text.

By Anubhab Banerjee

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 16

HRIBench: Benchmarking Interaction-Centric Human-Robot Collaboration

arXiv:2607. 13056v1 Announce Type: cross Abstract: Current vision-language-action (VLA) benchmarks primarily evaluate isolated manipulation skills while leaving human-robot interaction structure largely unmodeled.

By Chang Liu, Jiawei Zhang, Tao Zhang, Ye Wang, Hongyu Zhou, Qin Jin

agents robotics fine-tuning multimodal benchmarks safety

arXiv AI

Jul 16

Efficient Text-to-Audio Generation via Pruning

arXiv:2607. 13330v1 Announce Type: cross Abstract: Diffusion-based text-to-audio generative models such as AudioLDM achieve high perceptual quality and strong semantic consistency; however, their practical deployment is hindered by the substantial computational cost of the U-Net denoising backbone.

By Arshdeep Singh, Yi Yuan, Yun Chen, Wenwu Wang, Mark D. Plumbley

diffusion fine-tuning efficiency safety

arXiv AI

Jul 16

SingGuard-NSFA: Extensible Guardrails for Agentic AI via Generative Reasoning and Real-Time Classification

arXiv:2607. 13081v1 Announce Type: cross Abstract: We present nsfaguard, a guardrail framework for securing agentic AI systems against operational threats, such as prompt injection, sensitive information extraction, malicious code requests, dangerous tool misuse, and resource exhaustion.

By SingGuard Team

agents fine-tuning benchmarks safety

arXiv AI

Jul 16

OvisOCR2 Technical Report

arXiv:2607. 13639v1 Announce Type: cross Abstract: We introduce OvisOCR2, a 0.

By Shiyin Lu, Yinglun Li, Yu Xia, Yuhui Chen, An-Yang Ji, Jun-Peng Jiang, Qing-Guo Chen, Jianshan Zhao, En Lin, Haijun Li, Cheng Qin, Zhao Xu, Weihua Luo

reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 16

Delving into the Temporal Challenges of Unified Video Protection Against Image-to-Video and Fine-Tuning-based Customization

arXiv:2607. 13336v1 Announce Type: cross Abstract: Recent diffusion-based video generation models have enabled high-quality personalized video customization through both tuning-based pipelines, which fine-tune a video diffusion model, and reference-based pipelines such as image-to-video generation.

By Yuxin Huang, Ziming Hong, Mingming Gong, Wanyu Wang, Jing Zhang, Tongliang Liu

diffusion fine-tuning safety

arXiv AI

Jul 16

Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Study

arXiv:2607. 14041v1 Announce Type: cross Abstract: Historical Manchu OCR must accommodate various visually distinct writing styles, including regular script, running script, and the semi-cursive chancery hand used in palace memorials, despite limited labeled data.

By Zhan Chen, Jiqiao Ma, Chih-wen Kuo

computer-vision fine-tuning

arXiv Machine Learning

Jul 16

Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems

arXiv:2607. 13735v1 Announce Type: new Abstract: The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engineering.

By Dhruv Shivkant, Saket Mohanty, Utkarsh Wadhwa

fine-tuning efficiency

arXiv Machine Learning

Jul 16

Tactile Modality Fusion for Vision-Language-Action Models

arXiv:2603. 14604v2 Announce Type: replace-cross Abstract: We propose TacFiLM, a lightweight modality-fusion approach that integrates visual-tactile signals into vision-language-action (VLA) models.

By Charlotte Morissette, Amin Abyaneh, Wei-Di Chang, Anas Houssaini, David Meger, Hsiu-Chin Lin, Jonathan Tremblay, Gregory Dudek

robotics fine-tuning multimodal

arXiv Machine Learning

Jul 16

Pretraining in Actor-Critic Reinforcement Learning for Locomotion

arXiv:2510. 12363v4 Announce Type: replace-cross Abstract: The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years.

By Jiale Fan, Andrei Cramariuc, Tifanny Portela, Marco Hutter

reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jul 16

BenthiCat: An opti-acoustic dataset for advancing benthic classification and habitat mapping

arXiv:2510. 04876v3 Announce Type: replace-cross Abstract: Benthic habitat mapping is fundamental for understanding marine ecosystems, guiding conservation efforts, and supporting sustainable resource management.

By Hayat Rajani, Valerio Franchi, Borja Martinez-Clavel Valles, Raimon Ramos, Rafael Garcia, Nuno Gracias

agents computer-vision fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 16

EXPLORE: Exploration with Guided Search for Analog Topology Generation using Language Models

arXiv:2607. 13416v1 Announce Type: new Abstract: Automating analog circuit topology design is essential to reduce the extensive manual effort required to meet increasingly diverse and customized application demands.

By Guanglei Zhou, Chen-Chia Chang, Yikang Shen, Jonathan Ku, Isaac Jacobson, Jingyu Pan, Yiran Chen, Xin Zhang

llms fine-tuning benchmarks

arXiv AI

Jul 16

Consensus as Privileged Context for Label-Free Self-Distillation

arXiv:2607. 13643v1 Announce Type: cross Abstract: Sampling multiple solutions and returning the majority answer is among the most reliable ways to improve the reasoning accuracy of large language models without labels, and a growing family of methods converts this consensus signal into training supervision.

By John Gkountouras, Josip Juki\'c, Ivan Titov

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 16

Leveraging unlabelled data for generalizable neural population decoding

arXiv:2607. 14086v1 Announce Type: new Abstract: Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments.

By Ximeng Mao, Nanda H. Krishna, Avery Hee-Woon Ryoo, Matthew G. Perich, Guillaume Lajoie

fine-tuning benchmarks

arXiv AI

Jul 16

Policy of Thoughts: Scaling Test-Time Training for LLM Reasoning via Online Policy Evolution

arXiv:2601. 20379v2 Announce Type: replace Abstract: Large language models (LLMs) struggle with complex, long-horizon reasoning due to instability caused by their frozen policy assumption.

By Zhengbo Jiao, Hongyu Xian, Qinglong Wang, Yunpu Ma, Zhebo Wang, Zifan Zhang, Dezhang Kong, Meng Han

llms fine-tuning

arXiv AI

Jul 16

LLM-Guided Reinforcement Learning for Audio-Visual Speech Enhancement

arXiv:2603. 13952v3 Announce Type: replace-cross Abstract: In existing Audio-Visual Speech Enhancement (AVSE) methods, objectives such as Scale-Invariant Signal-to-Noise Ratio (SI-SNR) and Mean Squared Error (MSE) are widely used; however, their correlation with perceived speech quality is often suboptimal and provides limited interpretability for optimization.

By Chih-Ning Chen, Jen-Cheng Hou, Hsin-Min Wang, Shao-Yi Chien, Yu Tsao, Fan-Gang Zeng

llms nlp reinforcement-learning fine-tuning multimodal safety

arXiv Machine Learning

Jul 16

Where Should RL Post-Training Compute Go? Model Size, Search, Learning, and Feedback

arXiv:2607. 13389v1 Announce Type: new Abstract: Reinforcement Learning (RL) post-training is increasingly used to adapt foundation models for reasoning, planning, and feedback-driven robot-learning pipelines, but constrained post-training resources are often summarized by a single total FLOP budget.

By Patrick Wilhelm, Odej Kao

reinforcement-learning robotics fine-tuning

arXiv AI

Jul 16

Data-Efficient Adaptation of LLMs via Attention Head Reweighting

arXiv:2607. 13425v1 Announce Type: cross Abstract: Learning effectively from limited data is critical in domains like security where labeled examples are scarce.

By Tuomas Oikarinen, Zixiao Chen, Charlotte Siska, Tsui-Wei Weng, Chandan Singh, Jianfeng Gao

llms nlp fine-tuning

arXiv Machine Learning

Jul 16

Self-Improving is Often Sudden: Enlightenment-style Finetuning for Large-Scale Models

arXiv:2607. 13395v1 Announce Type: new Abstract: The pursuit of autonomously self-improving models has attracted growing interest in the era of large-scale foundation models.

By Jing-Xiao Liao, Tianwei Zhang, Yu-Hao Jiang, Feifei Zhang, Hang-Cheng Dong, Feng-Lei Fan

llms fine-tuning multimodal benchmarks

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