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 Computation and Language

Oct 15

ANCHOR-RE: An Agentic Biomedical Relation Extraction Framework with Retrieval Augmentation and Error-Driven Verification

arXiv:2608.03154v2 Announce Type: replace Abstract: Biomedical relation extraction (BioRE) supports knowledge graph construction and hypothesis generation. In real-world settings, most candidate enti...

By Shufan Ming, Yikun Han, Gibong Hong, Rui Zhang, Halil Kilicoglu

llms agents fine-tuning benchmarks safety

arXiv Computation and Language

Oct 15

Off-the-Shelf Language-Vision Encoders Predict Human Gaze Preferences in Visual World Experiments

arXiv:2608.07282v2 Announce Type: replace Abstract: A central question for computational psycholinguistics is to what extent neural language models can serve as cognitively adequate models of human l...

By Rahul Murali Shankar, Titus von der Malsburg, Sebastian Pad\'o

llms fine-tuning multimodal

arXiv Computation and Language

Oct 15

Draft-Thinking: Learning Efficient Reasoning in Long Chain-of-Thought LLMs

arXiv:2603.00578v2 Announce Type: replace-cross Abstract: Long chain-of-thought~(CoT) reasoning improves the problem-solving capability of large reasoning models~(LRMs), but often incurs substantial...

By Jie Cao, Tianwei Lin, Juekai Lin, Zhenxuan Fan, Yunqi Cao, Bo Yuan, Ziyuan Zhao, Rolan Yan, Wenqiao Zhang, Siliang Tang

llms reinforcement-learning fine-tuning benchmarks

arXiv Computation and Language

Oct 15

Fish Audio Drama 3 Technical Report

arXiv:2610.18010v2 Announce Type: replace-cross Abstract: We introduce Fish Audio Drama 3, an open-source speech generation model that turns text into directed performances, built on a 35B-parameter...

By Dawei Han, Shidong Li, Shijia Liao, Jiahua Liu, Songting Liu, Qingzheng Wang, Yuxuan Wang, Xin Chen, Yifan Cheng, Tianyu Li, Gangin Park, Xuehao Yang, Chang Zeng, Haoyang Zhang, Ruoyi Zhang, Xiangyu Zhang

reinforcement-learning fine-tuning

arXiv Computer Vision

Oct 15

NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation

arXiv:2610.19330v1 Announce Type: new Abstract: Reliable recognition of accessibility-critical objects (e.g., audible pedestrian signals, door-activation buttons, and handrails) is essential for assi...

By Hochul Hwang, Jahir Sadik Monon, Soowan Yang, Shiven Umeshbhai Patel, Anh Nhat Hong Nguyen, Kien Nguyen, Keshav Garg, Dylan Gage, Duretti Hordofaa, Anshu Anjna, Eshed Ohn-Bar, Donghyun Kim

computer-vision robotics fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 15

Holi-Spatial 360: Generative Reconstruction from Sparse Panoramas for Spatial Rendering and Intelligence

arXiv:2610.19502v1 Announce Type: new Abstract: Spatial intelligence requires scalable 3D environments that are both photorealistic and semantically grounded, yet existing indoor datasets are dominat...

By Yuanyuan Gao, Zhirui Liu, Yuning Gong, Yifei Liu, Yifan Zhan, Kuixuan Jiao, Xiaolong Zhou, Yifan Yang, Hongjie Zhang, Xue Yang, Dan Xu, Zhihang Zhong

fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 15

MagicGarment: Benchmarking Single-Image Standalone 3D Garment Reconstruction

arXiv:2610.19623v1 Announce Type: new Abstract: Single-image 3D reconstruction has advanced rapidly, yet standalone garments remain challenging due to thin-shell geometry, open boundaries, and layere...

By Jia Chen, Ruyang Zhao, Jun Bai, Chao Huang, Saishang Zhong, Xinrong Hu

fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 15

Reliability-Aware Multimodal Sentiment Prediction Under Local Modality Missingness

arXiv:2610.20543v1 Announce Type: new Abstract: Multimodal sentiment models are usually evaluated on complete utterances, yet streams often lose a contiguous span of text, audio, or video. We study l...

By Minghao Kong, Deng Yao, Xi Zhang, Ying Gao, Jiurun Chen, Zirong Liu, Rongjie Wang

fine-tuning multimodal safety

arXiv Computer Vision

Oct 15

DarKnight: Retrieval-Guided Agentic Framework for Breaking Dark Web CAPTCHA Gates

arXiv:2610.19830v1 Announce Type: cross Abstract: Cyber Threat Intelligence (CTI) investigators who monitor dark web marketplaces for leaked credentials and breach listings must pass the diverse CAPT...

By Binghui Wu, Yeo Zi Yi, Darren Tan, Manit Baser, Levente Csikor, Dinil Mon Divakaran

llms rag agents fine-tuning multimodal

arXiv Computer Vision

Oct 15

Enabling Validation for Robust Few-Shot Recognition

arXiv:2506.04713v4 Announce Type: replace Abstract: A key challenge in Few-Shot Recognition (FSR) is the scarcity of labeled task-specific data for training. Contemporary methods mitigate this challe...

By Hanxin Wang, Tian Liu, Shu Kong

llms fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 15

Chain of Time: In-Context Physical Simulation with Image Generation Models

arXiv:2511.00110v2 Announce Type: replace Abstract: We propose a novel cognitively-inspired prompting method to stabilize and interpret the physical simulation produced by autoregressive image genera...

By YingQiao Wang, Eric Bigelow, Boyi Li, Tomer Ullman

llms diffusion fine-tuning benchmarks

arXiv Computer Vision

Oct 15

EvoSpatial: Experience-Driven Skill and Tool Evolution for Spatial Reasoning Agents

arXiv:2610.16679v2 Announce Type: replace Abstract: Spatial reasoning agents can inspect tool outputs and repair errors, but reusing these lessons requires preserving both the computation and the con...

By Bin Yu, Shijie Lian, Zhaolong Shen, Xiaopeng Lin, Yao Zhang, Haishan Liu, Haotian Li, Yuliang Wei, Kai Hu, Yichao Du, Zhirui Zhang, Bailing Wang, Kai Chen

agents fine-tuning benchmarks

arXiv AI

Oct 15

Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs

arXiv:2610.18818v2 Announce Type: cross Abstract: Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource langu...

By Divyanshu Bathla, Parag Singla, Jitin Singla

llms nlp fine-tuning

arXiv Machine Learning

Oct 15

Weighted Adjoint Matching: Finetuning Flow Matching Models with Time-Dependent Control Cost

arXiv:2610.20035v1 Announce Type: cross Abstract: We introduce Weighted Adjoint Matching, a method to finetune a pretrained diffusion or flow matching model given a differentiable reward. We derive i...

By Eliot Beyler, Francis Bach

diffusion fine-tuning

arXiv AI

Oct 15

Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models

arXiv:2506.17585v4 Announce Type: replace Abstract: Large language models can answer questions correctly and still hallucinate their citations. Most citation systems address this by retrieving docume...

By Yukun Huang, Sanxing Chen, Jian Pei, Manzil Zaheer, Bhuwan Dhingra

llms fine-tuning benchmarks

arXiv AI

Oct 15

LLMRec: Benchmarking Large Language Models on Recommendation Task

arXiv:2308.12241v2 Announce Type: replace-cross Abstract: Recently, the fast development of Large Language Models (LLMs) such as ChatGPT has significantly advanced NLP tasks by enhancing the capabili...

By Junling Liu, Chao Liu, Peilin Zhou, Qichen Ye, Dading Chong, Kang Zhou, Yueqi Xie, Yuwei Cao, Shoujin Wang, Chenyu You, Philip S. Yu

llms nlp fine-tuning benchmarks

arXiv AI

Oct 15

ClawLoop: Self-Improving Agents via Harness-Native Training Loop

arXiv:2610.14118v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has driven their deployment toward general-purpose harness environments, where LLM-based agents o...

By Hansi Yang, Linji Hao, Hongyan Hao, An Wang, Han Wu, Qi Gu, Xunliang Cai

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

arXiv AI

Oct 15

Learning to GUIDE the Group: Self-Learning LLM Facilitation Across Multi-Party Tasks

arXiv:2610.16231v1 Announce Type: new Abstract: As large language models are increasingly adopted as multi-party communication facilitators, there is a growing demand for agents capable of balancing...

By Ziyi Liu, Tzu-Sheng Kuo, Jiale Liu, Deqing Fu, Nithum Thain, Crystal Qian

llms agents fine-tuning safety

arXiv AI

Oct 15

Prompt Interference in LLM Post-Training: Why Pass@k Optimization Can Degrade Pass@1

arXiv:2602.21189v3 Announce Type: replace-cross Abstract: Pass@$k$ is a widely used performance metric for verifiable large language model tasks, including mathematical reasoning, code generation, an...

By Anas Barakat, Souradip Chakraborty, Khushbu Pahwa, Amrit Singh Bedi

llms fine-tuning

arXiv AI

Oct 15

Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs

arXiv:2605.15077v2 Announce Type: replace-cross Abstract: Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution sem...

By Guangyu Feng, Huanzhi Mao, Prabal Dutta, Joseph E. Gonzalez

llms agents fine-tuning

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