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

FraQ: Efficient Coordinate-Space Recompression for Federated Low-Rank Adaptation

arXiv:2608. 03605v1 Announce Type: new Abstract: Federated fine-tuning with Low-Rank Adaptation (LoRA) enables efficient collaborative adaptation of Large Language Models (LLMs) without centralizing private data.

By Shenghui Li, Thiemo Voigt

llms nlp fine-tuning benchmarks

arXiv AI

Aug 5

Evading Chain-of-Thought Monitoring Through Model Poisoning

arXiv:2608. 02820v1 Announce Type: cross Abstract: Chain-of-thought (CoT) monitoring is an increasingly important component of AI safety stacks but relies on the assumption that a model's reasoning trace is informative about its actions.

By Giorgio Severi, Shujaat Mirza, Blake Bullwinkel, Amanda Minnich

fine-tuning safety

arXiv Machine Learning

Aug 5

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model

arXiv:2608. 02946v1 Announce Type: new Abstract: Accurate detection of sedentary behavior is important for studying health risks related to prolonged sitting, but posture-based classification remains challenging with wearable sensors, especially at the wrist.

By Yuliang Chen, Weiwei Shi, Jingjing Zou, Rong Zablocki, Animesh Kumar, Jordan A. Carlson, Sheri J. Hartman, Mikael Anne Greenwood-Hickman, Paul R. Hibbing, Marta Jankowska, Jay Yang, Arun Kumar, Loki Natarajan

llms fine-tuning

arXiv Machine Learning

Aug 5

Don't Walk the Line: Boundary Guidance for Filtered Generation

arXiv:2510. 11834v3 Announce Type: replace Abstract: Generative models are increasingly paired with safety classifiers that filter harmful or undesirable outputs.

By Sarah Ball, Andreas Haupt

llms reinforcement-learning 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 Machine Learning

Aug 5

Evaluation Blindness: How Silent Measurement Failures Corrupt AI Systems from Training to Deployment

arXiv:2608. 02786v1 Announce Type: new Abstract: AI systems can fail silently.

By Priyanka Bajaj (Independent Researcher)

llms fine-tuning 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

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

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

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 AI

Aug 5

Distilled Roads: Generalisable Road Network Extraction Across Sensors, Resolutions, and Region

arXiv:2608. 03407v1 Announce Type: cross Abstract: Road network segmentation from satellite imagery remains challenging due to large geographic variation in road appearance, occlusions, and domain shifts introduced by differing resolutions and sensors.

By Sanayya, Rakshith Sathish, Ashwathi Nambiar

computer-vision fine-tuning efficiency benchmarks

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 Machine Learning

Aug 5

Sparse Weight Decomposition for Efficient Circuit Extraction

arXiv:2608. 03913v1 Announce Type: new Abstract: Dense pretrained transformers do not naturally expose interpretable units for circuit extraction.

By Chuanhao Yan, Xuhan Huang, Yawen Duan, Zhenfei Yin, Hang Zhao, Bryan Dai, Jie Fu

llms fine-tuning benchmarks safety

arXiv AI

Aug 5

SP3O: Reinforcement Learning from Segment Preferences without Reward Modeling

arXiv:2608. 02951v1 Announce Type: cross Abstract: Preference-based reinforcement learning (PbRL) for general stochastic MDPs often requires training a reward model.

By Evan Assmus, Qining Zhang, Lei Ying

llms reinforcement-learning robotics fine-tuning

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

Where Knowledge Collides: A Mechanistic Study of Intra-Memory Knowledge Conflict in Language Models

arXiv:2601. 09445v2 Announce Type: replace-cross Abstract: In language models (LMs), intra-memory knowledge conflict arises when inconsistent information about the same subject is encoded within the model's parametric knowledge.

By Minh Vu Pham, Hsuvas Borkakoty, Yufang Hou

llms fine-tuning

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

Standalone DINOv3 for Training-Free Open-Vocabulary Semantic Segmentation in Remote Sensing

arXiv:2608. 03023v1 Announce Type: cross Abstract: Remote sensing semantic segmentation is hindered by costly pixel-level annotations, motivating training-free open-vocabulary methods.

By Changhao Zhao, Haoxiang Li, Yuke Li, Hai Liu, LingLin Zeng

computer-vision fine-tuning multimodal

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 Machine Learning

Aug 5

MedCRP-CL: Continual Medical Image Segmentation via Bayesian Nonparametric Semantic Modality Discovery

arXiv:2605. 20297v2 Announce Type: replace-cross Abstract: Medical image segmentation faces a fundamental challenge in continual learning: data arrives sequentially from heterogeneous sources, yet effective continual learning requires discovering which tasks share sufficient structure to benefit from joint learning.

By Ziyuan Gao

computer-vision fine-tuning

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