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

Jun 29

LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior

arXiv:2606. 28182v1 Announce Type: cross Abstract: Embodied agents operating in decentralized and partially observable environments have attracted growing attention in recent years.

By Qinhong Zhou, Chuang Gan, Anoop Cherian

llms agents robotics fine-tuning benchmarks

arXiv AI

Jun 29

HAT-4D: Lifting Monocular Video for 4D Multi-Object Interactions via Human-Agent Collaboration

arXiv:2606. 28215v1 Announce Type: cross Abstract: Extracting dynamic 4D object interactions from massive, in-the-wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs.

By Jiaxin Li, Yuxiang Wu, Zhenkai Zhang, Xinrui Shi, Haoyuan Wang, Yichen Zhao, Su Linxiang, Chenyang Yu, Mingyu Zhang, Yifan Ding, Boran Wen, Li Zhang, Ruiyang Liu, Yong-Lu Li

agents robotics fine-tuning benchmarks safety

arXiv AI

Jun 29

POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation

arXiv:2606. 23533v2 Announce Type: replace Abstract: Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation because the output must follow strict formatting and structural rules.

By Hung Phan, Aniroop Naladala, Dubey Avanindra, Supryia Chinthavali, Lunga Dalton, Ali Jannesari

llms nlp fine-tuning

arXiv AI

Jun 29

SIDA: Synthetic Image Driven Zero-shot Domain Adaptation

arXiv:2507. 18632v2 Announce Type: replace-cross Abstract: Zero-shot domain adaptation is a method for adapting a model to a target domain without utilizing target domain image data.

By Ye-Chan Kim, SeungJu Cha, Si-Woo Kim, Taewhan Kim, Dong-Jin Kim

rag fine-tuning benchmarks safety

arXiv AI

Jun 29

Two-Stage Fine-Tuning for Protein Sequence Generation with Targeted Amino-Acid Composition

arXiv:2606. 27939v1 Announce Type: cross Abstract: Protein language models are standard priors for biological sequence generation, but steering them toward explicit distributional design targets remains largely unexplored.

By Violeta Basten-Romero, Rub\'en Mu\~noz-Tafalla, Anna Mar\'ia D\'iaz-Rovira, Bertran Miquel-Oliver, Isaac Filella-Merce, V\'ictor Guallar

llms reinforcement-learning fine-tuning safety

arXiv AI

Jun 29

MetaBreak: Jailbreaking Online LLM Services via Special Token Manipulation

arXiv:2510. 10271v2 Announce Type: replace-cross Abstract: Unlike regular tokens derived from existing text corpora, special tokens are artificially created to annotate structured conversations during the fine-tuning process of Large Language Models (LLMs).

By Wentian Zhu, Zhen Xiang, Wei Niu, Le Guan

llms rag nlp robotics fine-tuning benchmarks safety

arXiv Machine Learning

Jun 29

Continual Learning for Sequential Personalization of Small Language Models: A Stability Monitoring Analysis

arXiv:2606. 27634v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly being considered for deployment on edge devices such as laptops, enabling private, low-latency, and locally personalized applications.

By Thomas S. Paula, Lucas S. Kupssinsk\"u, Rodrigo C. Barros

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 29

Layerwise Progressive Freezing: A Training Scaffold for Depth-Scalable Binary Networks

arXiv:2606. 27759v1 Announce Type: new Abstract: Training binary neural networks (BNNs) from scratch is dominated by the straight-through estimator (STE), whose forward/backward mismatch produces severe accuracy degradation as networks deepen.

By Evan Gibson Smith, Bashima Islam

fine-tuning

arXiv Machine Learning

Jun 29

FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models

arXiv:2606. 27866v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models scale model ability with sparsely activated experts, making this architecture a standard recipe for modern large models.

By Fan Mo, Yuxuan Han, Geng Zhang, Wangbo Zhao, Yang You

llms fine-tuning efficiency

arXiv Machine Learning

Jun 29

Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte

arXiv:2606. 28220v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models.

By Gift Modekwe, Qiugang Lu

diffusion fine-tuning

arXiv Machine Learning

Jun 29

Qwen-Image-2.0-RL Technical Report

arXiv:2606. 27608v1 Announce Type: cross Abstract: We present Qwen-Image-2.

By Yixian Xu, Kaiyuan Gao, Yuxiang Chen, Yilei Chen, Zecheng Tang, Zihao Liu, Zikai Zhou, Deqing Li, Hao Meng, Kuan Cao, Jiahao Li, Jie Zhang, Liang Peng, Lihan Jiang, Ningyuan Tang, Shengming Yin, Tianhe Wu, Xiaoyue Chen, Yan Shu, Yanran Zhang, Yi Wang, Yu Wu, Yujia Wu, Zekai Zhang, Zhendong Wang, Xiao Xu, Kun Yan, Chenfei Wu

llms diffusion reinforcement-learning fine-tuning efficiency multimodal safety

arXiv Machine Learning

Jun 29

EchoSonar-R: A Multi-View Reasoning-Enabled Model for Disease Classification and Report Generation in Echocardiography

arXiv:2606. 28164v1 Announce Type: cross Abstract: Echocardiography is the most widely used non-invasive cardiac imaging modality, providing essential information for cardiovascular diagnosis.

By Darya Taratynova, Ahmed Aly, Numan Saeed, Mohammad Yaqub

llms fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 29

Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting

arXiv:2510. 18874v3 Announce Type: replace Abstract: Adapting language models (LMs) to new tasks via post-training carries the risk of degrading existing capabilities -- a phenomenon classically known as catastrophic forgetting.

By Howard Chen, Noam Razin, Karthik Narasimhan, Danqi Chen

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 29

Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting

arXiv:2510. 00809v3 Announce Type: replace Abstract: While Time Series Foundation Models (TSFMs) excel in zero-shot tasks, their behavior under continual fine tuning is poorly understood.

By Nouha Karaouli, Denis Coquenet, Elisa Fromont, Martial Mermillod, Marina Reyboz

fine-tuning benchmarks

arXiv AI

Jun 29

The Context-Ready Transformer

arXiv:2606. 27538v1 Announce Type: cross Abstract: We introduce the context-ready transformer, a new recurrent neural network architecture built from a D-layer transformer block that pre-contextualizes each token before it enters the block.

By Mahesh Godavarti

llms rag fine-tuning

arXiv AI

Jun 29

ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering

arXiv:2606. 27974v1 Announce Type: cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires models to combine image understanding with external knowledge.

By ZhengXian Wu, Hangrui Xu, Kai Shi, Zhuohong Chen, Yunyao Yu, Chuanrui Zhang, Zirui Liao, Jun Yang, Zhenyu Yang, Haonan Lu, Haoqian Wang

rag agents nlp reinforcement-learning fine-tuning multimodal

arXiv Machine Learning

Jun 29

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings

arXiv:2606. 27672v1 Announce Type: new Abstract: Inspired by advances in natural language processing and computer vision, "time-series foundation models" (TSFMs) have recently been introduced with the promise of strong generalization across diverse time-series tasks, including forecasting, classification, and anomaly detection, as well as across domains such as healthcare, climate science, and manufacturing.

By Taeyeong Choi, Mohammed Kamruzzaman

rag computer-vision nlp fine-tuning

arXiv AI

Jun 29

Can LLMs Judge Better Than They Generate? Evaluating Task Asymmetry, Mechanistic Interpretability and Transferability for In-Context QA

arXiv:2606. 28050v1 Announce Type: cross Abstract: LLM-as-a-Judge and self-evaluation pipelines implicitly assume that evaluation is easier than generation.

By Sambaran Bandyopadhyay

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jun 29

Unified Zero-Shot Time Series Forecasting: A Darts Foundation

arXiv:2606. 27438v1 Announce Type: new Abstract: Since its initial release in 2020, Darts has become a widely used open-source Python library for time series analysis.

By Zhihao Dai, Dennis Bader, Alain Gysi

fine-tuning

arXiv AI

Jun 29

Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification

arXiv:2511. 03217v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information.

By Shaghayegh Kolli, Richard Rosenbaum, Timo Cavelius, Lasse Strothe, Andrii Lata, Jana Diesner

llms agents fine-tuning benchmarks

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