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 AI

Jul 29

Instruction-Tuned Language Models Cannot Sample from Distributions They Can Describe

arXiv:2607. 25292v1 Announce Type: new Abstract: Silicon sampling uses language models as proxies for human survey respondents, treating each model call as an independent draw from the persona's response distribution.

By Chaemin Jang, Dongman Lee, Jihee Kim

llms fine-tuning benchmarks safety

arXiv AI

Jul 29

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance

arXiv:2607. 24791v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for applying large language models (LLMs) to enterprise document corpora, yet naive implementations encounter hard limits as corpus scale and query complexity grow.

By Mishca de Costa, Muhammad Saleh Anwar, Dave Mercier, Issam Hammad

llms rag agents nlp fine-tuning

arXiv AI

Jul 29

ScaleResfusion: Residual Rectified Flow based on Residual Vector Field

arXiv:2607. 25275v1 Announce Type: cross Abstract: Real-world Image Restoration (Real-IR) aims to recover high-quality (HQ) images from complex and unknown degradations.

By Zhenning Shi, Chen Xu, Junhao Zhang, Kefei Zhang, Linjie Liu, Zhedong Zheng, Tao Li

diffusion fine-tuning efficiency benchmarks

arXiv AI

Jul 29

DocAnnot -- Accelerating the Creation of Key Information Extraction Datasets with GenAI-Powered Auto-annotation

arXiv:2607. 24745v1 Announce Type: cross Abstract: Key Information Extraction (KIE) is vital for many document applications, but creating training datasets is traditionally a time-consuming manual process.

By Siddartha Reddy, Harikrishnan P M, Goutham Vignesh, Varun V, Vishal Vaddina

llms computer-vision fine-tuning benchmarks

arXiv AI

Jul 29

PreDiff-LM: Pretrained Discrete Masked Diffusion Language Modeling with Hybrid Attention

arXiv:2607. 25157v1 Announce Type: new Abstract: Discrete masked diffusion language models support bidirectional generation and infilling, but adapting pretrained autoregressive (AR) transformers requires reconciling causal pretraining with bidirectional denoising.

By Zhengtao Yao, Runhao Li, Xupeng Chen, Jiayi Cheng, Chenqian Le, Michael Yue, Jesson Wang, Siheng Wang, Guang Yang, Haoyan Xu, Chenhao Wei, Zhengqing Yuan, Youran Shen, Yanfang Ye, Junhao Dong

llms diffusion fine-tuning

arXiv AI

Jul 29

Towards Robust Reinforcement Learning for Small-Scale Language Model Agents

arXiv:2607. 25091v1 Announce Type: new Abstract: The alignment of Small Language Models (SLMs) in the 70--500M parameter range using reinforcement learning is often considered unstable, though the underlying failure mechanisms have not been systematically investigated.

By Md Rezwanul Haque, Md. Milon Islam, Fakhri Karray

llms agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 29

Early Detection of Distributed Backdoors in Multi-Agent LLM Systems: A Characterization Study

arXiv:2607. 24893v1 Announce Type: cross Abstract: Multi-agent LLM systems can be attacked by a payload that no single agent ever holds in full: a poisoned tool hides encrypted fragments in its observations, spreads them across several agents, and an external step reassembles and executes them after the run.

By Diego Fernandez Arias, Dev Prashant Mistry, Ren Wang, Yibo Hu

llms agents fine-tuning safety

arXiv AI

Jul 29

Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller

arXiv:2607. 25728v1 Announce Type: cross Abstract: This paper presents a cooperative indoor UAV guidance framework that combines a shared voxel-map world model with a multi-agent Soft Actor-Critic (MASAC) controller.

By Thomas Hickling, Dylan Wynne, Yu Su, Nabil Aouf

agents robotics fine-tuning

arXiv AI

Jul 29

Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn

arXiv:2607. 24783v1 Announce Type: new Abstract: Job understanding is critical to LinkedIn's mission of connecting talent with opportunity.

By Dan Xu, Baofen Zheng, Jianqiang Shen, Qi Xiao, Benjamin Hoan Le, Wen Pu, Saurabh Gupta, Ran Zhou, Neha Saraf, Alice Leung, Qianqi Shen, Liangjie Hong, Jingwei Wu, Wenjing Zhang

llms fine-tuning

arXiv AI

Jul 29

Cognivia: A Cognitive Behavioral Therapy Copilot for Evidence-Based Mental Healthcare

arXiv:2607. 25681v1 Announce Type: new Abstract: Cognitive distortion amplifies negative emotions and contributes to mental health disorders.

By Qi Chen, Siria Xiyueyao Luo, Jian Wang, Yuan Shi, Haocong Rao, Xuejiao Zhao

llms fine-tuning

arXiv AI

Jul 29

Multi-Sensor Alignment for Weather Simulations

arXiv:2607. 25612v1 Announce Type: new Abstract: Perception tasks for autonomous vehicles need to work satisfactorily in adverse weather conditions.

By Samsad Alam, Devyani Lambhate, Aditya Mohan, Vishal Kumar, Vaibhav Katewa

agents computer-vision fine-tuning safety

arXiv AI

Jul 29

How Small Can You Go? A Controlled Study of LoRA Rank, Target Modules, and Quantization Trade-offs for Text-to-SQL on a 60M-Parameter Model

arXiv:2607. 25583v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) and low-bit quantization are now standard tools for adapting language models under tight compute budgets, yet their interaction is most often studied on billion-parameter models where the design space is expensive to explore.

By Mahendra Singh Rathor, Anagheem Azzam

llms fine-tuning efficiency benchmarks data-engineering

arXiv AI

Jul 29

Architectural Backdoors in Vision-Language Model Supply Chains via Representation Steering

arXiv:2607. 25479v1 Announce Type: cross Abstract: Vision--Language Models (VLMs) are increasingly deployed through a model supply chain in which pretrained checkpoints, architecture definitions, text encoders, and exported computation graphs are distributed by third parties and reused across downstream services.

By Maria Rosaria Briglia, Igor Maljkovic, Antonio Emanuele Cin\`a, Luca Oneto, Iacopo Masi, Fabio Roli

llms rag diffusion nlp fine-tuning multimodal safety

arXiv AI

Jul 29

Aletheia: An Offline-First Clinical Decision Support System for Differential Diagnosis in Low-Resource Healthcare Settings

arXiv:2607. 24814v1 Announce Type: new Abstract: Access to specialist clinical expertise remains severely limited across sub-Saharan Africa, where physician-to-patient ratios can fall below 1:25,000 in rural settings.

By Joseph Walusimbi, Ann Move Oguti, Abubakhari Sserwadda, Precious Boss Kasasira, Charles Brian Okoboi

llms fine-tuning benchmarks

arXiv AI

Jul 29

Why Does Grounding Hurt Medical VQA? Benchmarking, Diagnosis, and Fine-Tuning of Vision-Language Models

arXiv:2604. 27720v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly applied to medical visual question answering (Med-VQA), yet whether they can \emph{localize} the evidence behind their answers---a prerequisite for clinical auditability---is poorly characterized.

By Xupeng Chen, Binbin Shi, Chenqian Le, Qifu Yin, Lang Lin, Haowei Ni, Ran Gong, Panfeng Li

llms nlp fine-tuning multimodal benchmarks

arXiv AI

Jul 29

Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMs

arXiv:2607. 24799v1 Announce Type: cross Abstract: Large Language Models tend to hallucinate when answering domain-specific ques tions from scientific documents without prior fine-tuning.

By Alexandru-Andrei Sauc\u{a}, Ana-Luiza Rusnac

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jul 29

OPERA: Offline Policy-guided Expert Routing and Adaptation for Universal Biomedical Image Analysis

arXiv:2607. 25108v1 Announce Type: cross Abstract: Biomedical image analysis spans diverse modalities and tasks, yet real-world deployment is hindered by severe distribution shifts across scanners, protocols, and patient populations.

By Zihan Li, Feiyang Liu, Dandan Shan, Ruibo Wang, Qingqi Hong

agents computer-vision fine-tuning multimodal benchmarks safety

arXiv AI

Jul 29

DeepVRegulome: DNABERT-based deep-learning framework for predicting the functional impact of short genomic variants on the human regulome

arXiv:2511. 09026v2 Announce Type: replace-cross Abstract: Whole-genome sequencing (WGS) has revealed numerous non-coding short variants whose functional impacts remain poorly understood.

By Pratik Dutta, Matthew Obusan, Rekha Sathian, Max Chao, Pallavi Surana, Nimisha Papineni, Yanrong Ji, Zhihan Zhou, Han Liu, Alisa Yurovsky, Ramana V Davuluri

fine-tuning

arXiv AI

Jul 29

Retraction-Free Optimization over the Stiefel Manifold for the LoRA Fine-Tuning

arXiv:2607. 25299v1 Announce Type: cross Abstract: Optimization over the Stiefel manifold plays a significant role in various machine learning tasks.

By Yuan Zhang, Jiang Hu, Zhijian Lai, Lin Lin, Zaiwen Wen

llms fine-tuning benchmarks

arXiv AI

Jul 29

AIriskEval-edu Demo: Auditing of Pedagogical Risks in Educational Explanations

arXiv:2607. 25634v1 Announce Type: new Abstract: We present AIriskEval-edu Demo, a platform that audits the pedagogical quality of instructional explanations and provides explainable audit results.

By Javier Irigoyen, Roberto Daza, Francisco Jurado, Julian Fierrez, Ruben Tolosana, Alvaro Ortigosa, Miguel Lopez-Duran, Aythami Morales

llms fine-tuning safety

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