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 2

Enhancing LLM Metacognition via Cognitive Pairwise Training

arXiv:2606. 00869v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to give confident answers when evidence or reasoning is unreliable.

By Weitao Li, Hao Zhou, Xuanyu Lei, Fandong Meng, Yuanhang Liu, Jingyi Ren, Ante Wang, Xiaolong Wang, Yuanchi Zhang, Fuwen Luo, Guangwen Yang, Lin Gan, Weizhi Ma, Yang Liu

llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 2

KDH-CAD: Knowledge-data hybrid CAD learning under data scarcity

arXiv:2606. 01702v1 Announce Type: cross Abstract: Deep learning in computer-aided design (CAD) remains fundamentally constrained by the data scarcity challenge: authentic CAD data is difficult to collect at scale, while synthetic data may not faithfully reflect real design practice.

By Ziqin Gao, Zhijie Yang, Qiang Zou

diffusion fine-tuning benchmarks

arXiv Machine Learning

Jun 2

Neural Low-Discrepancy Sequences

arXiv:2510. 03745v2 Announce Type: replace Abstract: Low-discrepancy points are designed to efficiently fill the space in a uniform manner.

By Michael Etienne Van Huffel, Nathan Kirk, Makram Chahine, Daniela Rus, T. Konstantin Rusch

computer-vision robotics fine-tuning

arXiv AI

Jun 2

ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Emergent Adaptation

arXiv:2602. 07883v3 Announce Type: replace Abstract: LLM-powered agentic systems excel at complex long-horizon tasks, but remain constrained by static configurations fixed before execution.

By Jingqi Zhou, Sheng Wang, Dezhao Deng, Junwen Lu, Junwei Su, Qintong Li, Jiahui Gao, Hao Wu, Jiyue Jiang, Lingpeng Kong, Dunhong Jin, Chuan Wu

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 2

MOSS-Audio Technical Report

arXiv:2606. 01802v1 Announce Type: cross Abstract: MOSS-Audio is a unified audio-language model for speech, environmental sound, and music understanding, supporting audio captioning, time-aware question answering, timestamped transcription, and audio-grounded reasoning.

By Chen Yang, Chufan Yu, Hanfu Chen, Jie Zhu, Jingqi Chen, Ke Chen, Wenxuan Wang, Yang Wang, Yaozhou Jiang, Yi Jiang, Zhengyuan Lin, Ziqi Chen, Zhaoye Fei, Chenghao Liu, Jun Zhan, Kang Yu, Kexin Huang, Mingshu Chen, Qinyuan Cheng, Ruixiao Li, Shimin Li, Songlin Wang, Yang Gao, Yiyang Zhang, Xipeng Qiu

llms agents nlp fine-tuning multimodal

arXiv AI

Jun 2

Cross-modal linkage risk in clinical vision-language models

arXiv:2606. 02276v1 Announce Type: cross Abstract: Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shared embedding space that can preserve instance-level image-report correspondence.

By Soroosh Tayebi Arasteh, Mahshad Lotfinia, Sven Nebelung, Daniel Truhn

llms rag fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 2

ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression

arXiv:2606. 00494v1 Announce Type: new Abstract: Post-Training Quantization (PTQ) and Low-Rank Adaptation (LoRA) constitute the standard pipeline for efficient Large Language Model (LLM) deployment.

By Wneya Yu, Chao Zhang, Li Wang, Samson Lasaulce, Merouane Debbah

llms fine-tuning efficiency

arXiv AI

Jun 2

Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters

arXiv:2606. 02107v1 Announce Type: cross Abstract: This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control.

By Youssef Mahran, Zeyad Gamal, Aamir Ahmad, Ayman El-Badawy

agents reinforcement-learning fine-tuning

arXiv AI

Jun 2

Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion

arXiv:2606. 00616v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos.

By Shivam Singh, Saptarshi Majumdar, Pratik Prabhanjan, Zicheng Liu, Emad Barsoum

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 2

A Protocol-Language Model for Network Intrusion (Without Deep Packet Inspection)

arXiv:2606. 00155v1 Announce Type: cross Abstract: Modern network intrusion detection systems (NIDS) are caught in a structural contradiction: the protocols carrying the highest threat intelligence are precisely those encrypted under TLS 1.

By Vivek Kumar Sharma

llms fine-tuning safety

arXiv AI

Jun 2

REAL: Resolving Knowledge Conflicts in Knowledge-Intensive Visual Question Answering via Reasoning-Pivot Alignment

arXiv:2602. 14065v2 Announce Type: replace Abstract: Knowledge-intensive Visual Question Answering (KI-VQA) frequently suffers from severe knowledge conflicts caused by the inherent limitations of open-domain retrieval.

By Kai Ye, Xianwei Mao, Sheng Zhou, Zirui Shao, Ye Mo, Liangliang Liu, Haikuan Huang, Bin Li, Jiajun Bu

nlp fine-tuning safety

arXiv AI

Jun 2

GuidaPA: Privacy-Preserving Chatbot for Public Administration via Federated Learning

arXiv:2606. 01386v1 Announce Type: new Abstract: We present GuidaPA, a privacy-preserving chatbot for the Italian Public Administration (PA) trained via Federated Learning (FL) on documentation from two national PA platforms, SIGESON and SIDFORS.

By Daniel M. Jimenez-Gutierrez, Albenzio Cirillo, Raffaele Nicolussi, Alessio Beltrame, Andrea Vitaletti

llms fine-tuning safety

arXiv Machine Learning

Jun 2

Reconsidering Positional Supervision in Masked Diffusion Language Model Training

arXiv:2601. 22947v2 Announce Type: replace-cross Abstract: Masked diffusion language models (MDLMs) generate text by unmasking tokens in parallel and have recently emerged as alternatives to autoregressive language models.

By Mengyu Ye, Keito Kudo, Ryosuke Takahashi, Jun Suzuki

llms diffusion fine-tuning benchmarks safety

arXiv AI

Jun 2

ProductWebGen: Benchmarking Multimodal Product Webpage Generation

arXiv:2606. 01022v1 Announce Type: cross Abstract: Crafting a product display webpage from a source product image, along with layout and visual content instructions, holds significant practical value for domains such as marketing, advertising, and E-commerce.

By Zhihong Liu, Siqi Kou, Zheng Li, Ye Ma, Quan Chen, Peng Jiang, Kai Yu, Zhijie Deng

llms diffusion fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 2

GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases

arXiv:2605. 30237v2 Announce Type: replace-cross Abstract: Semi-structured knowledge bases (SKBs) embed textual documents in a typed graph of entities and relations, and underpin applications such as product search, academic paper search, and precision-medicine inquiries.

By Yicheng Tao, Yiqun Wang, Xiangchen Song, Xin Luo, Kai Liu, Jie Liu

rag fine-tuning benchmarks

arXiv AI

Jun 2

CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards

arXiv:2606. 00020v1 Announce Type: cross Abstract: Large Language Model (LLM) based Chinese Grammatical Error Correction (CGEC) systems face two critical challenges: general-purpose models lack specialized linguistic priors for subtle grammatical distinctions, and Supervised Fine-Tuning (SFT) with Maximum Likelihood Estimation fails to optimize for precision-focused metrics, leading to systematic over-correction.

By Wei Tian, Yuhao Zhou, Man Lan

llms nlp reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 2

EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction

arXiv:2606. 02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.

By Vigneshwar Hariharan (National University of Singapore), Chithra Reghuvaran (University College Dublin), Arlene John (University of Twente), Nhat Pham (Cardiff University), Omer Rana (Cardiff University), Deepu John (University College Dublin), Ganesh Neelakanta Iyer (National University of Singapore)

llms fine-tuning benchmarks

arXiv AI

Jun 2

EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks

arXiv:2606. 01767v1 Announce Type: new Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that severely limit cross-task scalability.

By Yangxuan Zhou, Sha Zhao, Jiquan Wang, Shijian Li, Gang Pan

fine-tuning efficiency benchmarks

arXiv AI

Jun 2

MASER: Modality-Adaptive Specialist Routing for Embodied 3D Spatial Intelligence

arXiv:2606. 02463v1 Announce Type: cross Abstract: In 3D environments, Embodied Agents answer spatially relevant questions through reasoning from a mixture of modalities including natural language, RGB images, point clouds, depth maps and camera poses.

By Hilton Raj, Vishnuram AV

llms rag agents robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 2

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.

By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li

llms agents computer-vision fine-tuning multimodal benchmarks

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