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 Computer Vision

Sep 1

OptiGeo: Efficient Monocular Geometry for Embodied Perception in Optically Challenging Scenes

arXiv:2608.29881v1 Announce Type: new Abstract: Monocular depth estimation has achieved strong open-domain generalization, yet reliable robotic deployment remains difficult in transparent, reflective...

By Muxin Liu, Tianbo Liu, Jing Xia, Xiaoyang Lyu, Xiaoshan Wu, Bo Wang, Peng Dai, Zhongrui Wang, Shaoshuai Shi, Xiaojuan Qi

computer-vision robotics fine-tuning benchmarks safety

arXiv AI

Sep 1

SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents

arXiv:2605.05726v2 Announce Type: replace Abstract: As LLM agents are increasingly deployed with large libraries of reusable skills, selecting the right skill for a user request has become a critical...

By Hongcheol Cho, Ryangkyung Kang, Youngeun Kim

llms rag agents fine-tuning benchmarks

arXiv Computation and Language

Sep 1

Cloud and On-Premises Deployment of Uzbek Legal RAG via Targeted Retriever Fine-Tuning

The paper reports on building a retrieval‑augmented legal assistant for Uzbek that operates in both a managed cloud service and an on‑premises deployment. It introduces two new domain benchmarks—one for retrieval and one for end‑to‑end QA—and shows that fine‑tuning an open‑weight text embedder (UTE‑1) can close the performance gap with proprietary models under tight cost and latency constraints. The authors also provide negative results for a QLoRA experiment and release the benchmarks, evaluation code, and the fine‑tuned embedder for future low‑resource legal NLP work.

By Tatul Danielyan, Mariam Avetisyan, Hrant Davtyan

llms rag nlp fine-tuning benchmarks

arXiv AI

Sep 1

Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training

The paper investigates how different forms of compressed chain‑of‑thought (CoT) reasoning—Explicit, Composed, and Implicit—affect large language model (LLM) performance after supervised fine‑tuning (SFT). Using a synthetic compositional reasoning task, the authors show that coarser CoT requires more SFT data, that Composed and Implicit CoT benefit more from data scaling (with Composed also benefiting from repetition), and that reinforcement learning with verifiable rewards (RLVR) can decompose compressed steps learned during SFT. Additionally, unidirectional CoT ordering improves generalization on longer sequential tasks.

By Kohsei Matsutani, Gouki Minegishi, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Sep 1

Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation

arXiv:2608.31009v1 Announce Type: new Abstract: Structure-based drug design (SBDD) requires ligands that satisfy both 3D target affinity and 1D chemical validity. Existing controllable generation met...

By Tianyu Gao, Zhikai Su, Jiashu Li, Wenjun Gao, Zichuan Ying, Zhe Zhao, Fei Zhang, Ye Wei

agents diffusion fine-tuning multimodal

arXiv Computer Vision

Sep 1

DICS: Exploring Data Intrinsic Consistency for Visual Instruction Selection

arXiv:2608.30209v1 Announce Type: new Abstract: Visual instruction tuning is crucial for advancing the vision-language alignment and instruction-following capabilities of Vision-Language Models (VLMs...

By Yuyang Hong, Jinhui Guo, Jiaqi Gu, Lubin Fan, Ruixiang Wang, Kun Ding, Yue Wu, Shiming Xiang, Jieping Ye

llms fine-tuning multimodal benchmarks safety

arXiv Computer Vision

Sep 1

AnnotateAnything: Automatic Annotation of 3D Assets for Robot Manipulation

arXiv:2606.17446v2 Announce Type: replace-cross Abstract: Simulation enables scalable robot data collection, but raw 3D assets provide only geometry, lacking the semantic, interactive, and physical k...

By Haoran Lu, Mutian Shen, Shuyang Yu, Yu Xiao, Songling Liu, Jianshu Zhang, Shang Wu, Yue Chen, Guo Ye, Jiayi Wang, Zhaoran Wang, Han Liu

robotics fine-tuning multimodal benchmarks

arXiv AI

Sep 1

Interpreting and Steering for Safe and Correct Code Generation

arXiv:2608.30025v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate source code containing vulnerabilities, yet little work studies the internal mechanisms that distingui...

By Hao Yan, Ziyu Yao

llms fine-tuning safety

arXiv Computer Vision

Sep 1

LISynSeg: Data-Centric Label-to-Image Synthesis for Cross-Modality Whole-Heart Segmentation

arXiv:2608.31073v1 Announce Type: new Abstract: Whole-heart segmentation (WHS) in computed tomography (CT) and magnetic resonance imaging (MRI) is affected by acquisition shifts and heterogeneous car...

By Jiacheng Wang, Ivana Isgum, Ipek Oguz

computer-vision fine-tuning benchmarks

arXiv Computation and Language

Sep 1

Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understanding

arXiv:2608.24621v2 Announce Type: replace Abstract: Can language models be trusted in safety- critical operations? In such settings, strong per- formance on semantic metrics does not guaran- tee oper...

By Yujing Chang, Thinh Pham, Van-Phat Thai, Chunyao Ma, Yash Guleria, Pham Nhut Huy, Sameer Alam

llms nlp fine-tuning benchmarks safety

arXiv Computation and Language

Sep 1

ArabicDialectSafety: A Dialect-Aware Benchmark for Arabic Content Safety Classification

arXiv:2608.01291v2 Announce Type: replace Abstract: We present ArabicDialectSafety, a human-curated Arabic safety dataset of 25,071 prompts covering six Arabic varieties: Modern Standard Arabic, Syri...

By Wajdi Zaghouani, Md. Rafiul Biswas, Kholoud Khalil Aldous, Mabrouka Bessghaier

llms fine-tuning benchmarks safety

arXiv Computation and Language

Sep 1

WebWorld: The Browser as a World Model for Self-Improving Web Code

arXiv:2608.30530v1 Announce Type: new Abstract: VLM-driven self-improvement of web code has a structural flaw: the model that proposes the repair is the model that judges it, and visual plausibility...

By Jiajun Wu, Jian Yang, Yaxin Du, Wei Zhang, Haowen Wang, Junhang Cheng, Yuxuan Zhang, Tuney Zheng, Xianglong Liu, Ming Zhou

llms fine-tuning multimodal

arXiv Computer Vision

Sep 1

Reconstruction-Aware Cryo-EM Particle Picking

arXiv:2608.28838v1 Announce Type: cross Abstract: Cryo-electron microscopy (cryo-EM) determines the structures of proteins and macromolecular assemblies at near-atomic resolution, and the final 3D re...

By Riku Itsuji, Yuanhao Wang, Xingjian Li, Seonghui Min, Hideo Saito, Min Xu

fine-tuning benchmarks

arXiv AI

Sep 1

Dynamic Important Example Mining for Reinforcement Finetuning

arXiv:2608.29252v1 Announce Type: new Abstract: Reinforcement fine-tuning (RFT) is increasingly used to strengthen the reasoning abilities of large models, yet its effectiveness is bound by how train...

By Haoru Tan, Sitong Wu, Yanfeng Chen, Shizhen Zhao, Yang-Tian Sun, Tianjia Liu, Chirui Chang, Shaofeng Zhang, Samm Sun, Xiuzhe Wu, Ruobing Xie, Xiaojuan Qi

fine-tuning benchmarks safety

arXiv Computation and Language

Sep 1

Large Language Models Systematically Favor Popular Options: Evidence and Mitigation Across MCQs

arXiv:2608.29257v1 Announce Type: new Abstract: Multiple-choice questions (MCQs) are a standard format for evaluating large language models (LLMs), yet the popularity of answer options can confound e...

By Abdelrahman Abdallah, Mohammed Ali, Bhawna Piryani, Mahmoud Abdalla, Adam Jatowt

llms fine-tuning benchmarks safety

arXiv Machine Learning

Sep 1

Efficient Real-Time Adaptation of ROMs for Unsteady Flows Using Data Assimilation

arXiv:2602.23188v2 Announce Type: replace Abstract: We propose an efficient retraining strategy for a parameterized Reduced Order Model (ROM) that attains accuracy comparable to full retraining while...

By Isma\"el Zighed, Andrea N\'ovoa, Luca Magri, Taraneh Sayadi

llms fine-tuning

arXiv Machine Learning

Sep 1

Least but not Last: Fine-tuning Intermediate Principal Components for Better Performance-Forgetting Trade-Offs

arXiv:2602.03493v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) methods have emerged as crucial techniques for adapting large pre-trained models to downstream tasks under computational...

By Alessio Quercia, Arya Bangun, Ira Assent, Hanno Scharr

computer-vision nlp fine-tuning

arXiv Machine Learning

Sep 1

Universal Redundancies in Time Series Foundation Models

arXiv:2602.01605v2 Announce Type: replace Abstract: Time Series Foundation Models (TSFMs) leverage extensive pretraining to accurately predict unseen time series during inference, without the need fo...

By Anthony Bao, Venkata Hasith Vattikuti, Jeffrey Lai, William Gilpin

llms fine-tuning benchmarks safety

arXiv Computation and Language

Sep 1

When History Is Multimodal: Rethinking Context Management for Long-Horizon Agents

arXiv:2608.29897v1 Announce Type: new Abstract: Long-horizon agents need a context manager to compress growing interaction histories into a bounded working context, via passive strategies or active s...

By Jiaqi Su, Cong Pang, Jiawei Hong, Tiankuo Yao, Zixuan Chen, Xin Lou, Lewei Lu

agents nlp reinforcement-learning fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Sep 1

Item-Mean Surrogates: Why Richer Persona Data Fail to Improve LLMs as Human Surrogates

arXiv:2608.29455v1 Announce Type: cross Abstract: LLMs are increasingly used as human surrogates, often on the premise that richer persona data could make them substitutes or exploratory tools for sp...

By Daehwan Ahn, Chengfeng Mao, Dokyun Lee

llms fine-tuning benchmarks

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