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 23

Probabilistic Modeling of Jailbreak on Multimodal LLMs: From Quantification to Application

arXiv:2503.06989v5 Announce Type: replace-cross Abstract: Recently, Multimodal Large Language Models (MLLMs) have demonstrated their superior ability in understanding multimodal content. However, the...

By Wenzhuo Xu, Zhipeng Wei, Xiongtao Sun, Zonghao Ying, Deyue Zhang, Dongdong Yang, Xiangzheng Zhang, Quanchen Zou

llms fine-tuning multimodal safety

arXiv Computer Vision

Sep 23

IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection

arXiv:2606.22892v2 Announce Type: replace-cross Abstract: The clinical diagnosis of skin diseases is susceptible to interference from inter-class similarity of skin lesions, and over-reliance on clin...

By Haibiao Li, Di Lin, Xue Jiang, Weiwei Wu, Yanxi Li, Yugang Chi

llms computer-vision fine-tuning safety

arXiv Computation and Language

Sep 23

MultiViewDx: Evidence-Linked Multi-View Clinical Diagnosis

MultiViewDx is a physician‑validated multimodal instruction dataset that links medical imaging studies with patient context and normalizes heterogeneous reports into an evidence‑linked workflow (evidence → findings → differential discussion → diagnosis). The dataset covers a wide range of imaging modalities and uses a unified image‑text retriever to ensure that instruction synthesis is grounded in source‑supported evidence. Fine‑tuned models on MultiViewDx achieve the highest average accuracy on four MedVQA benchmarks and receive the strongest overall rating on JAMA Clinical Challenge cases, with ablations confirming the importance of case‑level multi‑view organization and evidence‑linked reasoning.

By Junda Wang, Zonghai Yao, Yujan Ting, Eric Z. Chen, Hieu Tran, Hong Yu, Weijing Huang, Terrence Chen

llms nlp fine-tuning multimodal benchmarks

arXiv AI

Sep 23

ACLArena: Agent Continue Learning in Multi-stage Post-training

arXiv:2609.23989v1 Announce Type: new Abstract: Building general-purpose agents for industrial deployment requires integrating multiple capabilities, each typically acquired at a distinct stage of tr...

By Haixin Wang, Xiaoxuan Wang, Junkai Zhang, Han Zhang, Renliang Sun, Alexander K Taylor, Yidan Shi, Haoran Deng, Chenguang Wang, Jason Cong, Yizhou Sun, Wei Wang

agents reinforcement-learning fine-tuning efficiency

arXiv AI

Sep 23

DocMIDE: Learning Multi-Hop Implicit Derivation in Visually Rich Documents

arXiv:2609.24092v1 Announce Type: new Abstract: Real-world document processing systems rely on rigid, predefined schemas, yet critical target fields often lack direct visual counterparts on the page....

By Jeremy Cerwin Wang, Wai Kit Wong, Jeff Kai Tai Tang

llms fine-tuning multimodal benchmarks

arXiv AI

Sep 23

Brain-Token Learning: Microstate-Based Tokenization and Multi-Scale Interaction for Long-Horizon EEG Sequence Modeling

arXiv:2609.24324v1 Announce Type: new Abstract: Electroencephalography (EEG) provides a non-invasive window into dynamic brain activity, yet modeling long-horizon EEG sequences remains challenging du...

By Weishan Ye, Yue Pan, Li Zhang, Gan Huang, Zhen Liang

llms nlp fine-tuning

arXiv AI

Sep 23

Construting Reverse Thinking: Developing Large Language Models' Reverse Thingking Ability

arXiv:2609.24760v1 Announce Type: new Abstract: When facing complex problems, humans tend to try various ideas for different issues. Human thinking patterns exhibit remarkable flexibility in adapting...

By Xin Liu, Yunhai Li, Chunfu Jia, Ziliang Chen, Jisen Song

llms fine-tuning

arXiv AI

Sep 23

Semantic Candidate-Job Matching: A Comparative Evaluation of Dense Embedding Models in Hybrid Retrieval

arXiv:2609.23307v1 Announce Type: cross Abstract: This paper presents a comparative evaluation of dense embedding models for semantic candidate-job matching in high-volume staffing workflows. Incomin...

By Sai Yashwant, Siddhartha Jain, Anurag Dubey, Samaroha Chatterjee, Gantala Thulsiram

llms rag fine-tuning benchmarks

arXiv AI

Sep 23

WaveletECO: A Closed-Loop Physical ECO Platform and a Specialized Local Language Model

arXiv:2609.23444v1 Announce Type: cross Abstract: Engineering change order (ECO) is an important step in repairing timing and electrical violations during the late stages of chip design. Existing Age...

By Guoxiang Xu, Guozhen Ji, Zijian Luo, Zhengrui Chen, Qi Sun, Cheng Zhuo

llms agents fine-tuning efficiency

arXiv AI

Sep 23

Which Constraints Are Missing? Ask the Verifier: Graded Rewards for Constraint-Following Music Generation

arXiv:2609.23665v1 Announce Type: cross Abstract: Constraint-following music generation asks a score to satisfy several user-specified properties at once, each checkable programmatically (key, meter,...

By Haoyue Liu, Ye Chen, Zhichao Wang, Xiaoyu Ma, Haoran Shou, Xiaoying Tang

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Sep 23

From Bits to Beliefs: Recoverable Semantic Fingerprints for Black-Box Verification of Large Language Models

arXiv:2609.24084v1 Announce Type: cross Abstract: Open-weight large language models (LLMs) can be copied, modified, and redeployed behind black-box APIs, making post-release ownership verification di...

By Jiaxin Hong, Yuxin Peng, Hongyao Yu, Hao Fang, Shuoyang Sun, Bin Chen

llms fine-tuning efficiency

arXiv AI

Sep 23

CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation

arXiv:2601.06352v3 Announce Type: replace Abstract: Adapting large language models to individual users remains challenging due to the tension between fine-grained personalization and scalable deploym...

By Yutong Song, Jiang Wu, Weijia Zhang, Chengze Shen, Shaofan Yuan, Weitao Lu, Jian Wang, Yu Wang, Nikil Dutt, Amir M. Rahmani

llms fine-tuning benchmarks

arXiv Machine Learning

Sep 23

Trains but Doesn't Learn: A Post-Training Delivery Benchmark for LLM Agents as Forward-Deployed Engineers

arXiv:2609.25237v1 Announce Type: new Abstract: Post-training is becoming a service (PTaaS): a customer hands an operator data and a goal, and a forward-deployed engineer (FDE) returns a fine-tuned,...

By Weihang Ding, Junfei Zhan

llms agents fine-tuning benchmarks

arXiv Machine Learning

Sep 23

Extending FunctionGemma for Practical On-Device Mobile Function Calling

arXiv:2609.25373v1 Announce Type: new Abstract: On-device assistants require function-calling models that map natural language to local system actions, but existing resources emphasize web APIs or na...

By Ali Rezagholizadeh, Soheila Samiee

agents fine-tuning safety

arXiv Machine Learning

Sep 23

From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs

arXiv:2609.25655v1 Announce Type: new Abstract: As large language models (LLMs) scale rapidly, dense full-parameter adaptation becomes increasingly expensive, motivating sparse and modular architectu...

By Zhentao Tan, Chang Liu, Yao Liu, Yue Wu, Jieping Ye

llms fine-tuning benchmarks

arXiv Machine Learning

Sep 23

Graph Domain Adaptation Does Not End with Representation Learning

arXiv:2609.25692v1 Announce Type: new Abstract: Graph domain adaptation (GDA) transfers knowledge from a labeled source graph to an unlabeled target graph under shifts in both node attributes and gra...

By Ziqian Liu, Yongxue Xu, Enze Zhang, Jiaqi Zhang, Hao Wang, Maolin Wang

fine-tuning benchmarks safety

arXiv Machine Learning

Sep 23

Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing

arXiv:2609.26214v1 Announce Type: new Abstract: We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. Th...

By \'Eloi Sainte-Beuve (Orange Research), Guillaume Larue (Orange Research), Louis-Adrien Dufr\`ene (Orange Research), Quentin Lampin (Orange Research), Ali Al Khansa (Orange Research)

fine-tuning

arXiv Machine Learning

Sep 23

MSA-CITE: A Co-Adapted LoRA Specialist Ecology for Fixed-Budget Small-Model Inference

arXiv:2609.26217v1 Announce Type: new Abstract: Compact language models are typically deployed by retaining a single post-training checkpoint and sampling it repeatedly. In this work, we challenge th...

By Ruitong Li, Binjie Guo, Aisheng Mo, Guowei Su, Jie Li, Ru Zhang

llms rag fine-tuning

arXiv Machine Learning

Sep 23

JAMPR+/L2D: scalable neural heuristic for constrained vehicle routing problems in dynamic environment

arXiv:2609.26275v1 Announce Type: new Abstract: The vehicle routing problems with real-world constraints (we consider vehicles capacity limits, time windows constrains, pickup-and-delivery multi-depo...

By Andrew Soroka, Alex Meshcheryakov

reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Sep 23

One Domain, Many Tongues: Composing Domain and Language LoRAs for Cross-Lingual Remote-Sensing MLLMs without Paired Data

arXiv:2609.26097v1 Announce Type: cross Abstract: Remote-sensing (RS) multimodal large language models (MLLMs) are trained and evaluated only in English, while text-only instruction data covers over...

By Xuechen Li

llms fine-tuning multimodal

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