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

Aug 5

On the Limits of Layer Pruning for Generative Reasoning in Large Language Models

arXiv:2602. 01997v3 Announce Type: replace-cross Abstract: Recent work has shown that layer pruning can effectively compress large language models (LLMs) while retaining strong performance on classification benchmarks, often with little or no finetuning.

By Safal Shrestha, Anubhav Shrestha, Minwu Kim, Aadim Nepal, Keith Ross

llms fine-tuning efficiency benchmarks

arXiv AI

Aug 5

When Refusal Looks Safe: The Refusal-Cue Shortcut in Safety Guard Models

arXiv:2608. 03201v1 Announce Type: new Abstract: Safety guards are widely used to filter harmful content and are typically trained via supervised fine-tuning on labeled prompt-response pairs.

By Yu Feng, Chunting Zang, Chen Shen, Rui Miao, Ge Teng, Weidong Cai, Jieping Ye

fine-tuning benchmarks safety

arXiv Machine Learning

Aug 5

Attention is Case-Sensitive

arXiv:2608. 03711v1 Announce Type: cross Abstract: In human visual perception, uppercase lettering serves as a natural salience cue that captures attention within lowercase text.

By Maximilian Dillitzer, Tin Stribor Sohn, Jason J. Corso, Michael Auerbach

llms nlp fine-tuning multimodal

arXiv AI

Aug 5

Getting the Parameters Right: A Difficulty-Graded Benchmark and Probe-Guided Training for LLM Tool Calls

arXiv:2608. 03071v1 Announce Type: new Abstract: Large language model agents derive much of their capability from tool use.

By Guoyao Yu, Xiaoqing Sun, Ziqi Huang, Shaojing Fan, Zhongyi Zhang, Xiaomeng Hu, Xiaobo Xue, Yangyang Shi, Xiong Xiao, Yang Song, Biao Lyu, Rong Wen, Xing Li, Qinming He, Shunming Zhu, Zhenguang Liu

llms rag agents fine-tuning benchmarks

arXiv AI

Aug 5

OncoTriad-QA: A Patient-Level Radiology-Pathology-Genomics Benchmark for Pan-Cancer Reasoning

arXiv:2608. 02615v1 Announce Type: cross Abstract: Cancer diagnosis and characterization require integrating complementary evidence from radiology, pathology, genomics, and clinical metadata.

By Ahnaf Munir, Dannong Wang, Michael W. McDonald, Mubarak Shah, Pegah Khosravi, Yu Tian

llms nlp fine-tuning multimodal benchmarks

arXiv AI

Aug 5

Taming the Implicit: Dual-Channel Risk-Aware Reinforcement Fine-Tuning for Continual Multimodal Post-Training

arXiv:2608. 03660v1 Announce Type: new Abstract: Reinforcement fine-tuning (RFT) is widely believed to inherently resist catastrophic forgetting in continual post-training of multimodal large language models.

By Yibei Liu, Jiajun Chen, Qianle Zhang, Tangyue Jin, Mengying Zhu, Meng Xi, Yangyang Wu

llms fine-tuning multimodal benchmarks

arXiv AI

Aug 5

Training Documents Reranker with Search Rubrics for Deep Research Agent

arXiv:2608. 03527v1 Announce Type: cross Abstract: Retrieval systems help deep research agents generate high-quality answers by providing relevant documents.

By Wenhan Liu, Yu Lu, Qiaolin Xia, Hui Xu, Tong Zhao, Jian Xi, Yutao Zhu, Haijin Liang, Haibo Shi, Hao Wang, Zhicheng Dou

llms rag agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 5

A Unified Framework for Human AI Collaboration in Security Operations Centers with Trusted Autonomy

arXiv:2505. 23397v3 Announce Type: replace Abstract: This article presents a structured framework for Human-AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and Human-in-the-loop decision making.

By Ahmad Mohsin, Helge Janicke, Ahmed Ibrahim, Iqbal H. Sarker, Seyit Camtepe

llms agents fine-tuning

arXiv AI

Aug 5

LoCA: Forward-Only LLM Tuning after One-Shot Calibration with Local Credit Assignment

arXiv:2608. 03020v1 Announce Type: new Abstract: Parameter-efficient post-training reduces the number of trainable parameters, but still requires repeated end-to-end backpropagation through the frozen backbone.

By Linhan Xia, Rui Liu, Zhaofeng Zhang, Yihao Wang, Binrui Shen, Shengxin Zhu

llms fine-tuning benchmarks

arXiv AI

Aug 5

Verifiable Memory: Learning Unified Memory Management with Local and Global Verifiers for Large Language Model Agents

arXiv:2608. 03137v1 Announce Type: new Abstract: Large language model (LLM) agents must retain reusable information, control a bounded active context, and recover earlier evidence during long-horizon interaction.

By Xiaolong Sun, Qichao Wang, Hangyu Li, Liang Chen

llms agents fine-tuning benchmarks

arXiv AI

Aug 5

Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent

arXiv:2608. 03979v1 Announce Type: cross Abstract: We introduce Video-DeepResearch (Video-DR), extending multimodal agents from static images to continuous video streams, a setting that demands dense spatiotemporal grounding coupled with open-web exploration.

By Zhen Fang, Yu Zeng, Wenxuan Huang, Yiming Zhao, Shiting Huang, Tianfei Ren, Qi Lu, Qingnan Ren, Qisheng Su, Lionel Z. Wang, Qingyu Yin, Shuang Chen, Zehui Chen, Lin Chen, Zhenfei Yin, Yao Hu, Shaohui Lin, Wanli Ouyang, Shaosheng Cao, Feng Zhao

llms agents fine-tuning multimodal benchmarks safety

arXiv AI

Aug 5

Adaptive Two-Stage Visual Token Pruning for Efficient Inference in Video-Language Models

arXiv:2608. 03112v1 Announce Type: cross Abstract: Vision-language models excel at image and video understanding but suffer from high inference latency due to the need to process thousands of tokens per image, limiting their deployment on resource-constrained edge devices and in real-time surveillance applications.

By Paribesh Regmi, Qingshuang Chen, Chi Zhang, Heba Aly, Yelin Kim, Hongda Mao

llms rag fine-tuning efficiency multimodal benchmarks

arXiv AI

Aug 5

UniPASE: A Generative Model for Universal Speech Enhancement with High Fidelity and Low Hallucinations

arXiv:2604. 14606v2 Announce Type: cross Abstract: Universal speech enhancement (USE) aims to restore speech signals from diverse distortions across multiple sampling rates.

By Xiaobin Rong, Zheng Wang, Yushi Wang, Jun Gao, Jing Lu

fine-tuning efficiency benchmarks safety

arXiv AI

Aug 5

TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring

arXiv:2608. 03952v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to provide conversational practice for English-as-a-second-language (ESL) learners.

By Dongjie Yang, Siyan Lin, Leixian Shen, Rui Sheng, Huamin Qu, Zixin Chen

llms fine-tuning benchmarks

arXiv AI

Aug 5

LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models

arXiv:2608. 03457v1 Announce Type: new Abstract: Diffusion language models (dLLMs) offer an alternative to autoregressive (AR) language modeling, yet the scaling behavior of Mixture-of-Experts (MoE) dLLMs remains poorly understood.

By Fengqi Zhu, Shaoxuan Xu, Jingyang Ou, Zebin You, Yipeng Xing, Huabin Liu, Xiaolu Zhang, Jun Zhou, Zhenzhong Lan, Yankai Lin, Wayne Xin Zhao, Jianguo Li, Chongxuan Li, Ji-Rong Wen

llms diffusion fine-tuning benchmarks

arXiv AI

Aug 5

CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting

arXiv:2608. 03031v1 Announce Type: new Abstract: Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations but also by evolving contextual features.

By Xiaoyu Tao, Mingyue Cheng, Bokai Pan, Chuang Jiang, Huanjian Zhang, Tian Gao, Yaguo Liu, Qi Liu, Enhong Chen

llms agents reinforcement-learning fine-tuning

arXiv AI

Aug 5

BAP-SQL: Budget-Aware Observation Planning for Agentic Text-to-SQL

arXiv:2608. 02876v1 Announce Type: new Abstract: Tool-using agents do not merely consume observations: their actions determine what arrives next.

By Chong Peng, Pin Qian, Su Wang, Yihang Chen, Varun Sah

agents fine-tuning data-engineering

arXiv AI

Aug 5

PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detection

arXiv:2608. 03926v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) underpins applications in predictive maintenance, finance, and cloud computing, however performance remains sensitive to representation choices, especially in multivariate settings.

By Mateusz Smendowski, Kamil Faber, Piotr Nawrocki, Nathalie Japkowicz, Roberto Corizzo

fine-tuning

arXiv AI

Aug 5

SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them

arXiv:2607. 27703v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly used in embodied agents to interpret visual inputs, reason about spatial relationships, and make task-level decisions based on that reasoning.

By Yang Zhou, Zixuan Huang, Sunzhu Li, Zhuo Yang, Chen Zhang, Shunian Chen, Caijun Yan, Jianyao Xu, Shunyu Liu, Weijie Fu, Peiliang Li, Xiaozhi Chen, Yuxiang Cai

llms agents computer-vision reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Aug 5

Particle-based Generalised Stochastic Optimisation

arXiv:2608. 02844v1 Announce Type: cross Abstract: We develop a class of diffusion-based stochastic particle optimisation methods for loss functions with intractable gradients.

By Jiechen Jackie Zhang, O. Deniz Akyildiz

diffusion fine-tuning