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

Oct 1

More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models

arXiv:2609.38827v1 Announce Type: new Abstract: Direct-decision models turn text into low-latency structured labels and scores, making them attractive for classification and automatic evaluation. Yet...

By Tianxiang Gao, Jinzhe Li, Zhiyuan Li, Yi Chang, Yuan Wu

fine-tuning safety

arXiv AI

Oct 1

OpenJev-RLCD: A Working RLCD Implementation

arXiv:2609.38850v1 Announce Type: new Abstract: Decision models such as Jev answer questions with probabilities, which are only useful if they are calibrated. Open-source reproductions rely on superv...

By Zhimin Gao, Pichao Wang

reinforcement-learning fine-tuning

arXiv AI

Oct 1

EHR-RobustGym: Benchmarking and Training Agents for Robust Clinical Reasoning

arXiv:2609.39371v1 Announce Type: new Abstract: In hospital workflows, electronic health records (EHRs) are often noisy, and may not contain the evidence needed to confirm events or measurements refe...

By Yitong Qiao, Yancheng Jin, Lei Liu, Yue Shen, Jian Wang, Jinjie Gu, Zhixuan Chu

llms agents reinforcement-learning fine-tuning benchmarks data-engineering

arXiv AI

Oct 1

Searching for BSM Experimental Signatures with Large Lagrangian Models

arXiv:2609.38309v1 Announce Type: cross Abstract: The search for physics Beyond the Standard Model (BSM) is generally limited not by the supply of theory descriptions but by the lack of discriminatin...

By Ibrahim Elsharkawy, Victoria Knapp-Perez, Wahid Bhimji, Aishik Ghosh

llms agents reinforcement-learning fine-tuning

arXiv AI

Oct 1

From Speech to Editable Concepts: Probing Emotion Recognition with Concept Bottleneck Models

arXiv:2609.39453v1 Announce Type: cross Abstract: Speech emotion recognition (SER) is the task of assigning emotion labels to utterances. Early systems relied on acoustic features, whereas recent app...

By Hezhao Zhang, Thomas Hain

llms computer-vision fine-tuning safety

arXiv AI

Oct 1

When Masking Helps or Hurts Robustness in Compressed CLIP: A Pre-Deployment Diagnostic

arXiv:2609.39704v1 Announce Type: cross Abstract: This paper demonstrate that whether masking-based token pruning helps or hurts worst-group robustness can be predicted before deployment, without lab...

By Muhammad Zawish, Steven Davy

computer-vision fine-tuning efficiency benchmarks

arXiv AI

Oct 1

Don't Inoculate Everything: Stratified Inoculation Prompting Narrows Backdoor Triggers and Preserves Desired Traits

arXiv:2609.35356v2 Announce Type: replace Abstract: Supervised fine-tuning can teach language models undesired behaviours alongside desired ones. Inoculation prompting (IP) aims to limit unwanted gen...

By Kajetan Dymkiewicz, Tim Farrelly, Adam Prada, Ishaan Panigrahi, Srishti Gureja, Helen Yannakoudakis, Robert Mullins, Victor Gillioz, Daniel Tan, Maxime Rich\'e

llms fine-tuning

arXiv AI

Oct 1

AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD

arXiv:2505.14717v2 Announce Type: replace-cross Abstract: Scientific machine learning uses simulation data to train surrogate models for fast physical-field prediction across geometries. Local shape...

By Xigui Li, Yuanye Zhou, Feiyang Xiao, Xin Guo, Chen Jiang, Tan Pan, Xingmeng Zhang, Cenyu Liu, Zeyun Miao, Xiansheng Wang, Qimeng Wang, Yichi Zhang, Wenbo Zhang, Hongwei Zhang, Ruoxi Jiang, Fengping Zhu, Limei Han, Chensen Lin, Yuan Cheng

fine-tuning benchmarks

arXiv AI

Oct 1

From Compound Figures to Medical Multi-image Reasoning: Scaling Multimodal Large Language Models with Biomedical Literature

arXiv:2511.22232v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly capable in medical imaging, yet most focus on single-image settings. Clinical inter...

By Zhen Chen, Yihang Fu, Rong Zhou, Serina Applebaum, Min Kyu Kim, Aidan Gilson, Morten Lee, Salahudeen Mirza, Gabriel Madera, Mauro Giuffre, Yuanting Pan, Roy Jiang, Hyunjae Kim, Hua Xu, Qingyu Chen

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Oct 1

Grounded World Model: Latent Planning with Language Goals

arXiv:2604.11751v2 Announce Type: replace-cross Abstract: World models such as DINO-WM and LeWM specify the goal with an image, which is difficult to obtain in advance for novel tasks. We present the...

By Quanyi Li, Lan Feng, Haonan Zhang, Wuyang Li, Letian Wang, Alexandre Alahi, Harold Soh

rag robotics fine-tuning multimodal

arXiv AI

Oct 1

VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images

arXiv:2604.09531v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition, largely be...

By Guanyu Zhou, Yida Yin, Wenhao Chai, Shengbang Tong, Xingyu Fu, Zhuang Liu

llms diffusion reinforcement-learning fine-tuning multimodal benchmarks

arXiv Computation and Language

Oct 1

When In-Distribution Gains Fail: Evaluating Weak-to-Strong Reward Models under Preference Shift

arXiv:2605.25629v3 Announce Type: replace Abstract: Weak-to-strong (W2S) generalization is a promising framework for scalable oversight, yet existing evaluations often test students under matched tra...

By Khoi Le, Tri Cao, Phong Nguyen, Cong-Duy Nguyen, Anh Tuan Luu, Miao Chunyan, See-Kiong Ng, Thong Nguyen

reinforcement-learning fine-tuning

arXiv Computer Vision

Oct 1

Introduction to Computer Vision

arXiv:2609.39627v1 Announce Type: new Abstract: This book presents a code-first introduction to computer vision, spanning classical 2D image processing, classical 3D vision, and deep learning. Organi...

By Stan Birchfield

computer-vision fine-tuning

arXiv Computer Vision

Oct 1

BAM! Bayesian Anything Model: a foundation model for generative computational imaging

arXiv:2609.39660v1 Announce Type: new Abstract: Generative models are transforming Bayesian computational imaging, yet the field still lacks physics-aware foundation models. Current practice falls in...

By Alessio Spagnoletti, Charlesquin Kemajou Mbakam, Jonathan Spence, Andr\'es Almansa, Marcelo Pereyra

fine-tuning benchmarks safety

arXiv Computer Vision

Oct 1

MC-PanDA++: Simpler, Stronger, and More Robust Domain-Adaptive Panoptic Segmentation

arXiv:2609.39681v1 Announce Type: new Abstract: Unsupervised domain adaptation (UDA) reduces the annotation burden in panoptic segmentation by leveraging a cost-effectively labeled source domain (e.g...

By Ivan Martinovi\'c, Josip \v{S}ari\'c, Yuki M. Asano, Sini\v{s}a \v{S}egvi\'c

llms computer-vision fine-tuning benchmarks safety

arXiv Computer Vision

Oct 1

BTC3D: Blended Tile Conditioning for Detail-Enhancing Image-to-3D Generation

arXiv:2609.39709v1 Announce Type: new Abstract: Recent diffusion-based pipelines have achieved promising progress in image-to-3D synthesis. However, generating high-fidelity details remains challengi...

By Junyu Li, Qiuyu Chen, Pengcheng Wang, Shiqi Yang, Alexandra Gomez-Villa, Joost van de Weijer, Ruilin Li, Kai Wang

rag diffusion fine-tuning

arXiv Computer Vision

Oct 1

Depth Anything in $360^\circ$: Towards Scale Invariance in the Wild

arXiv:2512.22819v2 Announce Type: replace Abstract: Panoramic depth estimation captures the complete 360$^\circ$ scene geometry, being essential for robotics and AR/VR applications. While perspective...

By Hualie Jiang, Ziyang Song, Zhiqiang Lou, Rui Xu, Minglang Tan

computer-vision robotics fine-tuning benchmarks

arXiv Statistics ML

Oct 1

Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models

arXiv:2609.39445v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head to a frozen backbone, a one-size-fits-all setup...

By Hung Phan, Thuy T. Nguyen, Minh Ngoc Dinh, Nhat-Quang Tran

fine-tuning

arXiv AI

Oct 1

Learning to Route in Visual Space via Multi-Step Embedding Retrieval

The paper introduces VHOP, a data generation framework and benchmark for visual agentic search, and VHOP-Router, an end‑to‑end training pipeline that turns a standard embedding model into an autoregressive multi‑step retriever operating directly in visual latent space. VHOP-Router eliminates the need for intermediate text queries, boosting retrieval accuracy from under 5% to 76.3% and improving task success rates by 52.7% while dramatically reducing token usage and API payloads. The approach generalizes to unseen difficulty levels and realistic test sets, offering an efficient solution that preserves native LLM capabilities.

By Tianyu Chen, Mingyuan Zhou, Jiaxing Wu

llms rag agents diffusion reinforcement-learning fine-tuning benchmarks

arXiv Computation and Language

Oct 1

CATCH: A Controllable Analysis Testbed for Reward Hacking in Coding RL

arXiv:2609.39533v1 Announce Type: new Abstract: During reinforcement learning with verifiable rewards (RLVR), large language models (LLMs) can exploit loopholes in their environments to obtain high r...

By Shouli Wang, Yanfeng Jia, Zhihao Ou, Zitao Su, Ruize He, Haotong Xie, Hao Peng, Juanzi Li, Xiaozhi Wang

llms reinforcement-learning fine-tuning safety

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