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

Sep 15

Parameter-Efficient Adaptation of Pretrained Language Models for Time-Series Forecasting

arXiv:2609.15344v1 Announce Type: new Abstract: We study the adaptation of pretrained language models to univariate time-series forecasting through a parameter-efficient transfer learning framework,...

By Tamanna Kumavat, Georg Brunner, Kyriakos Flouris

llms rag nlp fine-tuning multimodal benchmarks

arXiv AI

Sep 15

Shallow Beliefs: Synthetic document finetuning does not inoculate against emergent misalignment from reward hacking

arXiv:2609.14998v1 Announce Type: new Abstract: Recent work shows that models that learn to reward hack on RL environments can become broadly misaligned, and that reframing reward hacking as acceptab...

By Arun Jose, Julian Stastny

llms reinforcement-learning fine-tuning

arXiv Computer Vision

Sep 15

Parameter-Efficient Fine-Tuning of Foundation Models for Liver Tumor Segmentation in CT

arXiv:2609.14106v1 Announce Type: new Abstract: We evaluated parameter-efficient fine-tuning (PEFT) of the Segment Anything Model (SAM) for liver tumor segmentation in abdominal CT of colorectal live...

By Ramtin Mojtahedi, Mohammad Hamghalam, Jacob J. Peoples, Richard K. G. Do, Amber L. Simpson

llms computer-vision fine-tuning efficiency

arXiv Computer Vision

Sep 15

Vision-Language Models for Criterion-Level Grading of Handwritten Examinations in Outcome-Based Education

arXiv:2609.14284v1 Announce Type: new Abstract: Criterion-level grading connects examination performance to learning outcomes, but manual marking introduces workload and variation between markers. Th...

By Md Khalid Syfullah, Asif Hasan Tonmoy, Saad Ahmed, S. M. Jahangir Alam

llms fine-tuning multimodal

arXiv Computer Vision

Sep 15

Two-Stage Mixture-of-LoRA for Multi-Task Medical Vision-Language Learning

arXiv:2609.14350v1 Announce Type: new Abstract: Medical vision-language models (VLMs) allow a single model to perform clinical image analysis tasks ranging from diagnosis classification to report gen...

By Zhanghao Chen, Yuanyuan Li, Zhenyu Lu, Shuo Gao, Guangquan Zhou, Yikun Zhang

llms fine-tuning multimodal

arXiv Computation and Language

Sep 15

IROH: Insightful Ranking Of Humor using Multi-Stage Hybrid Retrieval with Rationale-Distilled LLM Judges for JOKER 2026 Track Task 1 English

arXiv:2609.15618v1 Announce Type: cross Abstract: Our team, VANGUARD, presents IROH (Insightful Ranking of Humor), a three-stage retrieval system for JOKER Task 1 English at CLEF 2026, achieving firs...

By Ana-Maria Luisa Mocanu, Sebastian Mocanu, Ciprian-Octavian Truic\u{a}, Elena-Simona Apostol

llms rag fine-tuning benchmarks

arXiv Computation and Language

Sep 15

A Controlled Reevaluation of Coreference Resolution Models

arXiv:2404.00727v3 Announce Type: replace Abstract: All state-of-the-art coreference resolution (CR) models involve finetuning a pretrained language model. Whether the superior performance of one CR...

By Ian Porada, Xiyuan Zou, Jackie Chi Kit Cheung

llms fine-tuning efficiency benchmarks

arXiv Computer Vision

Sep 15

WaRA: Wavelet Low-Rank Adaptation for Medical Image Classification

arXiv:2506.24092v3 Announce Type: replace Abstract: Adapting large pretrained vision models to medical image classification is often limited by memory, computation, and task-specific specializations....

By Moein Heidari, Yijin Huang, Yasamin Medghalchi, Alireza Rafiei, Roger Tam, Ilker Hacihaliloglu

computer-vision fine-tuning

arXiv Computer Vision

Sep 15

Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds

arXiv:2603.25165v3 Announce Type: replace Abstract: Recent advances in self-supervised learning (SSL) for point clouds have substantially improved 3D scene understanding without human annotations. Ex...

By Bin Yang, Mohamed Abdelsamad, Miao Zhang, Alexandru Paul Condurache

computer-vision fine-tuning

arXiv AI

Sep 15

Disentangling Topology and Diversity in Multi-Agent LLMs for Multilingual Low-Resource Emotion Detection

arXiv:2609.14570v1 Announce Type: cross Abstract: Multi-agent LLM systems combine multiple inference calls, but prior work often confounds how calls are connected with how they are diversified. We st...

By Ulugbek Shernazarov, Charitha Ruwansiri Weerakon Basnayake, Abdelkhaleq El Jarjini, Noel Crespi, Praboda Rajapaksha

llms agents fine-tuning

arXiv AI

Sep 15

An Efficient and Modular Framework for Targeted Harm Mitigation in LLMS

The paper introduces a modular correction framework for large language models that uses Activated LoRA adapters and a context-aware routing mechanism to mitigate harmful outputs. By allowing expert adapters to activate mid-sequence without invalidating the KV cache, the system achieves low-latency, targeted correction during generation. Experiments show improved alignment on safety benchmarks while maintaining task performance, presenting a lightweight, scalable approach to safer LLM deployments.

By Roberto Campbell, Momin Abbass, Muneeza Azmat, Michal Ulewicz, Raya Horesh, Kristjan Greenewald, Rog\'erio Abreu de Paula, Nathalie Baracaldo

llms fine-tuning efficiency benchmarks safety

arXiv Computer Vision

Sep 15

Don't Just Look, Intervene: Perturbation Based Region Labeling for VQA Images

arXiv:2609.13228v1 Announce Type: new Abstract: Vision Language Models (VLMs) should rely on visual evidence that directly determines the correct answer, but supervision for grounding visual reasonin...

By Marko Jojic, Zhaonan Li, Ben Zhou

llms fine-tuning

arXiv Computer Vision

Sep 15

RIGOR: Rig-Informed Geometry for Omnidirectional Reconstruction

arXiv:2609.13504v1 Announce Type: new Abstract: Recent developments in feed-forward 3D reconstruction resulted in models which can recover dense scene representations and camera motion solely from an...

By Tingjun Huang, Dmitry Rudshin, Mathieu Meyer, Pietro Bonazzi, Marc Pollefeys, Emilia Szyma\'nska

fine-tuning

arXiv Computation and Language

Sep 15

Clinical Reasoning Under a Partially Observed Objective in Cone Beam CT Report Generation

arXiv:2609.13238v1 Announce Type: new Abstract: Maxillofacial report generation from cone beam computed tomography is scored here by a composite objective placing 80% of its weight on a large languag...

By Ajo Babu George, Govind Arun, Sidharth N Krishna, Uma Ranjan

llms fine-tuning

arXiv Computation and Language

Sep 15

Measuring the Cost of Variety Conflation in Multilingual MT Evaluation: Adding Mozambican Xichangana, Nyanja and Sena to FLORES+

arXiv:2609.13847v1 Announce Type: new Abstract: In this paper, we extend FLORES+ with Portuguese-source evaluation sets for three Mozambican Bantu varieties: Xichangana, Mozambican Nyanja, and Sena....

By Felermino D. M. A. Ali, Delfina L\'azaro Mateus, Manuel Valente Mangue

llms fine-tuning

arXiv Machine Learning

Sep 15

Data-Efficient Agentic Graph Domain Adaptation via Reliability-Aware Prototype Learning

arXiv:2609.14045v1 Announce Type: new Abstract: Agentic learning systems are often required to adapt after deployment by observing new data and reusing prior knowledge under limited supervision or fe...

By Yingxu Wang, Kunyu Zhang, Siyang Gao

rag agents fine-tuning benchmarks

arXiv AI

Sep 15

LLM-Based Schema-Aware Split Learning for Privacy-Preserving Mental Distress Prediction Across Heterogeneous Surveys

arXiv:2609.15871v1 Announce Type: cross Abstract: Rising societal and lifestyle complexity has been linked to a growing prevalence of mental distress worldwide. Educational institutions, workplaces,...

By Md Khalid Syfullah, Alvi Ataur Khalil

llms fine-tuning safety

arXiv AI

Sep 15

Predictive audio representations for early detection and tracking of hidden dynamic objects

arXiv:2609.13595v1 Announce Type: cross Abstract: Predicting potential dangers is core to safety. Forecasting the presence of other traffic agents is core to danger prediction. Occluded traffic agent...

By Katerina Vinciguerra, Moritz Brandes, Danilo Hollosi, Letizia Marchegiani

rag agents fine-tuning safety

arXiv AI

Sep 15

Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Contact-Rich Assembly

arXiv:2609.07534v2 Announce Type: replace-cross Abstract: Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sus...

By Yuhan Wang, Yurou Chen, Hongye Jiang, Wenzhao Lian

reinforcement-learning robotics fine-tuning multimodal safety

arXiv AI

Sep 15

Exploring Automated Vulnerability Identification in JavaScript Code Using Large Language Models

arXiv:2609.13816v1 Announce Type: cross Abstract: JavaScript powers approximately 98.8% of all websites, making vulnerabilities in its code a significant security risk, yet existing detection approac...

By Manit Kaushik, Ishir Bhardwaj, Pranav Gupta, Pankaj Jalote, Arun Balaji Buduru

llms fine-tuning data-engineering

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