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 17

Uncertainty Quantification for Flow-Based Vision-Language-Action Models

arXiv:2606. 18043v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets.

By Ralf R\"omer, Maximilian Seeliger, Saida Liu, Ben Sturgis, Marco Bagatella, Daniel Marta, Andreas Krause, Angela P. Schoellig

diffusion robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 17

Rift: A Conflict Signature for Deception in Language Models

arXiv:2606. 17229v1 Announce Type: cross Abstract: A model that lies while knowing the truth is the central case ELK cannot handle with behavioral evaluation alone.

By Petr Nyoma

llms agents fine-tuning safety

arXiv AI

Jun 17

Reinforcing Dual-Path Reasoning in Spatial Vision Language Models

arXiv:2606. 17539v1 Announce Type: cross Abstract: Spatial VLMs have made substantial progress in geometric perception, yet complex spatial reasoning requiring multi-step inference over depth, distance, and scene relations remains challenging.

By Yatai Ji, An-Chieh Cheng, Yang Fu, Yukang Chen, Han Zhang, Zhaojing Yang, Wei Huang, Ka Chun Cheung, Song Han, Vidya Nariyambut Murali, Pavlo Molchanov, Jan Kautz, Simon See, Hongxu Yin, Ping Luo, Sifei Liu

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 17

Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation

arXiv:2606. 17340v1 Announce Type: cross Abstract: Accurate vision-based navigation in monocular endoscopy is difficult due to limited depth cues, weak tissue texture, non-rigid deformation, and substantial appearance variation across domains, all of which complicate pose estimation, depth prediction, and image-to-anatomy alignment.

By Hongchao Shu, Roger D. Soberanis-Mukul, Hao Ding, Morgan Ringel, Mali Shen, Saif Iftekar Sayed, Hedyeh Rafii-Tari, Mathias Unberath

llms computer-vision fine-tuning safety data-engineering

arXiv Machine Learning

Jun 17

From Drift to Coherence: Stabilizing Beliefs in LLMs

arXiv:2606. 17832v1 Announce Type: new Abstract: Large language models (LLMs) are often hypothesized to perform implicit Bayesian inference, yet a key coherence condition, the martingale property of predictive beliefs, has been shown to fail in controlled synthetic in-context learning settings.

By SongEun Kim, Seungyoo Lee, Edwin Fong, Hyungi Lee, Juho Lee

llms nlp fine-tuning benchmarks

arXiv AI

Jun 17

L-Proto: Language-Aware Episodic Prototypical Training for Multilingual Speaker Verification

arXiv:2606. 17416v1 Announce Type: cross Abstract: Multilingual speaker verification remains challenging because language-dependent acoustic variability causes speaker identity to become entangled with linguistic characteristics, degrading generalization across languages.

By Hyung-Seok Oh, Deok-Hyeon Cho, Seung-Bin Kim, Seong-Whan Lee

rag fine-tuning benchmarks

arXiv Machine Learning

Jun 17

Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models

arXiv:2403. 18957v3 Announce Type: replace-cross Abstract: Online user generated content games (UGCGs) are increasingly popular among children and adolescents for social interaction and more creative online entertainment.

By Keyan Guo, Ayush Utkarsh, Wenbo Ding, Isabelle Ondracek, Ziming Zhao, Guo Freeman, Nishant Vishwamitra, Hongxin Hu

llms fine-tuning multimodal safety

arXiv AI

Jun 17

Catastrophic Forgetting is Low-Rank: A Function-Space Theory for Continual Adaptation

arXiv:2606. 18024v1 Announce Type: cross Abstract: Catastrophic forgetting in continual adaptation is usually studied through parameter drift, replay, or distillation, but these views do not identify which output-space directions are vulnerable.

By Ido Nitzan Hidekel, Dan Raviv

fine-tuning efficiency

arXiv AI

Jun 17

RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models

arXiv:2506. 17639v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLA) have demonstrated remarkable capabilities and strong potential in complex robotic manipulation.

By Yuxuan Chen, Yixin Han, Yize Huang, Xiao Li

reinforcement-learning robotics fine-tuning efficiency multimodal

arXiv AI

Jun 17

EAGG: Embodiment-Aligned Grasp Generation via Geometry-Aware Graph Conditioning

arXiv:2606. 18092v1 Announce Type: cross Abstract: Cross-end-effector grasp generation seeks a unified model that generalizes across objects and across embodiments ranging from parallel grippers to dexterous end effectors.

By Wanhao Niu, Qiyan Ke, Yuan Sun, Hao Sun, Jie Xu, Muyuan Ma, Ruiqi Hu, Fuchun Sun

fine-tuning benchmarks

arXiv AI

Jun 17

When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning

arXiv:2606. 18033v1 Announce Type: cross Abstract: Cross-lingual transfer in multilingual NLP has been widely explored in supervised fine-tuning contexts, where factors like data availability and linguistic similarity largely determine transfer quality.

By Fred Philippy, Siwen Guo, Jacques Klein, Tegawend\'e F. Bissyand\'e

llms nlp fine-tuning

arXiv Machine Learning

Jun 17

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

arXiv:2602. 11715v2 Announce Type: replace Abstract: Diffusion large language models (dLLMs) have emerged as a compelling alternative to autoregressive (AR) LLMs, owing to their capacity for parallel token generation.

By Haolei Bai, Lingcheng Kong, Xueyi Chen, Jianmian Wang, Zhiqiang Tao, Huan Wang

llms diffusion reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 17

Perceptual compensation for tonal context in self-supervised speech models

arXiv:2606. 17835v1 Announce Type: cross Abstract: This study examines the extent to which the wav2vec2.

By James Kirby, Ioana Krehan, Michele Gubian

rag fine-tuning multimodal

arXiv AI

Jun 17

RooseBERT: A New Deal For Political Language Modelling

arXiv:2508. 03250v4 Announce Type: replace-cross Abstract: The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the final goal of lightening up political deliberation to citizens.

By Deborah Dore, Elena Cabrio, Serena Villata

llms nlp fine-tuning

arXiv AI

Jun 17

A Risk Decomposition Framework for Pre-Hoc Fine-Tuning Prediction

arXiv:2606. 17649v1 Announce Type: cross Abstract: The high cost of fine-tuning LLMs poses a significant economic barrier; pre-hoc performance prediction offers a critical solution to substantially reduce this expense.

By Yuxiang Luo, Chen Wang, Nan Tang

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 17

Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation

arXiv:2606. 18190v1 Announce Type: cross Abstract: Multi-stage cyberattacks span system, network, and browser logs.

By Abir Ashab Niloy, Ahmed Ryan, Imamul Hossain Rafi, Md Erfan, Md Rayhanur Rahman

llms fine-tuning safety

arXiv AI

Jun 17

FllumaOne: A Code-Native Multimodal CAD Dataset with Executable Programs and Kernel-Validated Feature Histories

arXiv:2606. 17696v1 Announce Type: new Abstract: Parametric computer-aided design records both final geometry and the ordered construction history that determines how a part can be edited.

By Jizong Zhan

fine-tuning multimodal

arXiv Machine Learning

Jun 17

A Closer Look at Failure Modes in Temporal Understanding of Large Audio-Language Models

arXiv:2606. 17417v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) achieve strong performance on a variety of audio understanding tasks but continue to struggle with temporal reasoning, a fundamental capability central to human auditory perception.

By Apoorva Kulkarni, Kaousheik Jayakumar, Sreyan Ghosh, Sarah Wiegreffe, Dinesh Manocha, Ramani Duraiswami

llms fine-tuning benchmarks

arXiv AI

Jun 17

NarrativeWorldBench: A Frontier-Saturated Benchmark and a Latent World Model for Long-Horizon Co-Creative Audio Drama

arXiv:2606. 17391v1 Announce Type: cross Abstract: Long-form serialized audio drama, with arcs that run for 200 to 800 episodes, is a major creative medium and a setting where frontier large language models (LLMs) fail.

By Logan Mann, Abdur Rahman, Mohammad Saifullah, Taaha Kazi, Vasu Sharma

llms fine-tuning benchmarks

arXiv AI

Jun 17

See First, Answer Later: Visual Evidence Pre-Alignment via Sufficiency-Driven RL

arXiv:2606. 17678v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) integrate strong text reasoning with visual inputs, yet their responses can be inconsistent with the underlying images, indicating ineffective utilization of visual evidence during inference.

By Yilian Liu, Sicong Leng, Guoshun Nan, Junyi Zhu, Jiayu Huang, Minghao Sun, Xuancheng Zhu, Yisong Chen, Zexian Wei, Xiaofeng Tao

llms reinforcement-learning fine-tuning multimodal benchmarks safety

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