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

Jun 9

AttentionCap: Transformer Based Capacitance Matrix Learning Toward Full-Chip Extraction

arXiv:2606. 08161v1 Announce Type: new Abstract: As capacitance extraction accuracy of rule-based pattern matching becomes difficult to sustain at advanced nodes, a growing trend emerges to develop deep-learning-based 2D capacitance models.

By Jiechen Huang, Hector R. Rodriguez, Dingcheng Yang, Zuochang Ye, Yibo Lin, Wenjian Yu

llms rag fine-tuning efficiency

arXiv Machine Learning

Jun 9

Have I Solved This Before? Retrieving Similar Segmentation Problems for Evolutionary Learning

arXiv:2606. 08155v1 Announce Type: new Abstract: Reliable integration and solid configuration of monitoring systems constitute a fundamental prerequisites for achieving high efficiency and productivity in contemporary manufacturing environments.

By Andreas Margraf, Henning Cui, J\"org H\"ahner

computer-vision fine-tuning

arXiv AI

Jun 9

Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning

arXiv:2507. 12612v4 Announce Type: replace-cross Abstract: Supervised fine-tuning performance for large language models depends strongly on how training budget is distributed across a heterogeneous set of tasks.

By Prateek Chanda, Saral Sureka, Parth Pratim Chatterjee, Krishnateja Killamsetty, Nikhil Shivakumar Nayak, Ganesh Ramakrishnan

llms fine-tuning

arXiv Machine Learning

Jun 9

Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?

arXiv:2606. 08578v1 Announce Type: new Abstract: Recently, large time series models (LTSMs) have gained increasing attention due to their similarities to large language models, including flexible context length, scalability, and task generality, outperforming advanced task-specific models.

By Xu Zhang, Peang Wang, Wei Wang

llms fine-tuning benchmarks

arXiv AI

Jun 9

LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection

arXiv:2606. 08153v1 Announce Type: cross Abstract: Detecting anomalies in large-scale system logs is critical for the reliability and security of modern computing infrastructure.

By David Eje, Tanmay Sharma, Khush Patel, Manuel Mazzara, Leonard Johard

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 9

Activation Steering Induces Emergent Misalignment: A More Comprehensive Evaluation

arXiv:2606. 08682v1 Announce Type: cross Abstract: Activation steering has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs).

By Qi Cao, Jian Lou, Meiting Liu, Wenjie Feng, Dan Li, See-Kiong Ng, Anh Tuan Luu

llms fine-tuning safety

arXiv AI

Jun 9

Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models

arXiv:2606. 09142v1 Announce Type: cross Abstract: Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexplored.

By Danya Li, Xiang Su, Yan Feng, Rico Krueger

llms nlp fine-tuning benchmarks safety

arXiv AI

Jun 9

Decoupling the "What" and "Where" With Polar Coordinate Positional Embeddings

arXiv:2509. 10534v3 Announce Type: replace-cross Abstract: The attention mechanism in a Transformer architecture matches key to query based on both content -- the what -- and position in a sequence -- the where.

By Anand Gopalakrishnan, Robert Csord\'as, J\"urgen Schmidhuber, Michael C. Mozer

llms rag fine-tuning

arXiv Machine Learning

Jun 9

Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation

arXiv:2606. 07660v1 Announce Type: cross Abstract: Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations.

By Qiaoyu Chen, Bing Zhang

diffusion fine-tuning safety

arXiv AI

Jun 9

Emergence of Context Characteristics Sensitivity in Large Language Models

arXiv:2606. 09525v1 Announce Type: cross Abstract: During instruction fine-tuning (IFT), large language models (LLMs) learn to follow instructions by using the provided context to answer a query.

By Nadya Yuki Wangsajaya, Haeun Yu, Isabelle Augenstein

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 9

Transfer learning for causal forest

arXiv:2606. 07693v1 Announce Type: cross Abstract: Transfer learning addresses the challenge of transfering knowledge from one domain to another.

By B\'er\'enice-Alexia Jocteur (ICJ, PSPM), V\'eronique Maume-Deschamps (ICJ, PSPM), Pierre Ribereau (PSPM, ICJ)

fine-tuning

arXiv AI

Jun 9

Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM Agents

arXiv:2606. 08151v1 Announce Type: new Abstract: Tool-using LLM agents often fail not because relevant text is absent, but because decisive evidence is not selected, compressed, or surfaced at action time.

By Xinyu Guan, Qianyang Zhao, Yuming Deng

llms rag agents fine-tuning benchmarks

arXiv AI

Jun 9

Post-training is (Massive) Supervised Learning

arXiv:2606. 07527v1 Announce Type: cross Abstract: The prevailing paradigm for training LLMs has evolved to rely on a massive post-training phase consisting of SFT and RL.

By Michael Hassid, Yossi Adi, Roy Schwartz

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 9

Language-based Trial and Error Falls Behind in the Era of Experience

arXiv:2601. 21754v3 Announce Type: replace Abstract: While Large Language Models (LLMs) excel in language-based agentic tasks, their applicability to unseen, nonlinguistic environments (e.

By Haoyu Wang, Guozheng Ma, Shugang Cui, Yilun Kong, Haotian Luo, Li Shen, Mengya Gao, Yichao Wu, Xiaogang Wang, Dacheng Tao

llms agents reinforcement-learning fine-tuning

arXiv AI

Jun 9

FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning

arXiv:2606. 08653v1 Announce Type: cross Abstract: Action-supervised fine-tuning of vision-language-action (VLA) policies fits demonstrations effectively but constrains only the directions that change predicted actions, leaving visual structure consistent across action-equivalent states free to collapse.

By Haihao Lin, Xiangsheng Huang, Xiao Yang, Weibang Zhou, Yiqi Zhang, Bo Yang, Simin Zeng, Jiawei Yang, Zhengyang Wang, Jiahui Du

fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 9

In-Context Learning of Stochastic Differential Equations with Foundation Inference Models

arXiv:2502. 19049v3 Announce Type: replace Abstract: Stochastic differential equations (SDEs) describe dynamical systems where deterministic flows, governed by a drift function, are superimposed with random fluctuations, dictated by a diffusion function.

By Patrick Seifner, Kostadin Cvejoski, David Berghaus, Cesar Ojeda, Ramses J. Sanchez

llms diffusion fine-tuning

arXiv Machine Learning

Jun 9

Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction

arXiv:2601. 09285v2 Announce Type: replace Abstract: Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge.

By Mianzhi Pan, JianFei Li, Peishuo Liu, Botian Wang, Yawen Ouyang, Yiming Rong, Hao Zhou, Jianbing Zhang

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 9

A Finetuned SpeechLLM for Joint Multi-Granular L2 Assessment and Natural-Language Rationales

arXiv:2606. 09470v1 Announce Type: cross Abstract: Automated L2 speech assessment can assign proficiency labels, but often lacks interpretability.

By Aditya Kamlesh Parikh, Cristian Tejedor-Garcia, Catia Cucchiarini, Helmer Strik

fine-tuning safety

arXiv Machine Learning

Jun 9

The Routing Plateau: Understanding and Breaking the Accuracy Limits of LLM Routers

arXiv:2606. 07587v1 Announce Type: new Abstract: LLM routing has become a popular approach to improve the cost-quality trade-off of LLM services by dynamically selecting a model for each query.

By Yifan Lu, Qiyue Zhang, Shenrun Zhang, Zhibo Yu, Zhuang Wang, Hanjie Chen, Jiarong Xing

llms fine-tuning benchmarks

arXiv AI

Jun 9

FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction

arXiv:2606. 07645v1 Announce Type: cross Abstract: The scarcity of hard negative samples in current vision-language datasets significantly hinders fine-grained perception.

By Chang Kong, Yuebing Li, Peng Mo, Haigang Zhang, Qiuming Luo

agents fine-tuning multimodal benchmarks

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