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

Jun 24

Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation

arXiv:2606. 24515v1 Announce Type: new Abstract: Computer-Use Agents (CUAs) execute high-level user goals by perceiving and acting directly within graphical user interfaces.

By Marta Sumyk, Oleksandr Kosovan

llms agents reinforcement-learning fine-tuning multimodal

arXiv AI

Jun 24

CineCap: Structured Reasoning with Spatio-Temporal Anchors for Cinematographic Video Captioning

arXiv:2606. 24636v1 Announce Type: new Abstract: Cinematographic captioning aims to describe how a video is filmed using professional film-language concepts such as camera movement, shot size, depth of field, composition, and shooting angle.

By Xinyu Mao, Yuhui Zeng, Xiaokun Liu, Wenyu Qin, Meng Wang, Xin Tao, Pengfei Wan, Xiaohan Xing, Max Meng

llms diffusion reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 24

Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules

arXiv:2508. 10971v2 Announce Type: replace-cross Abstract: Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs.

By Nasim Shirvani-Mahdavi, Chengkai Li

llms fine-tuning

arXiv AI

Jun 24

WAND: Windowed Attention and Knowledge Distillation for Efficient Autoregressive Text-to-Speech Models

arXiv:2604. 08558v2 Announce Type: replace-cross Abstract: Recent decoder-only autoregressive text-to-speech (AR-TTS) models produce high-fidelity speech, but their memory and compute costs scale quadratically with sequence length due to full self-attention.

By Hanna Lee, Tan Dat Nguyen, Jaehoon Kang, Kyuhong Shim

fine-tuning efficiency multimodal

arXiv AI

Jun 24

Grounded Chess Reasoning in Language Models via Master Distillation

arXiv:2603. 20510v2 Announce Type: replace Abstract: Language models often lack grounded reasoning capabilities in specialized domains where training data is scarce but bespoke systems excel.

By Zhenwei Tang, Qianfeng Wen, Seth Grief-Albert, Yahya Elgabra, Blair Yang, Honghua Dong, Ashton Anderson

llms reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Jun 24

An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data

arXiv:2606. 24459v1 Announce Type: new Abstract: Bearing fault diagnosis faces critical challenges when dataset heterogeneity, operating condition variations, and limited labeled data occur simultaneously in industrial environments.

By Jinghan Wang, Feng Cheng, Wentao Wu, Hang Li, Gaoliang Peng, Tianchen Liu

llms rag fine-tuning benchmarks safety

arXiv Machine Learning

Jun 24

Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training

arXiv:2602. 09303v2 Announce Type: replace Abstract: We propose a physics-informed consistency modeling framework for solving partial differential equations (PDEs) via fast, few-step generative inference.

By Che-Chia Chang, Chen-Yang Dai, Te-Sheng Lin, Ming-Chih Lai, Chieh-Hsin Lai

diffusion fine-tuning

arXiv Machine Learning

Jun 24

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective

arXiv:2512. 10244v2 Announce Type: replace-cross Abstract: Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled and abundant unlabeled task-specific examples to annotate the unlabeled ones.

By Tian Liu, Anwesha Basu, James Caverlee, Shu Kong

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 24

OrbitForge: Text-to-3D Scene Generation via Reconstruction-Anchored Video Synthesis

arXiv:2606. 24799v1 Announce Type: cross Abstract: Generic text-to-video models can be used as rich open-world scene priors.

By Chenrui Fan, Paolo Favaro

diffusion fine-tuning efficiency

arXiv AI

Jun 24

Self-Recognition Finetuning can Prevent and Reverse Emergent Misalignment

arXiv:2606. 23700v1 Announce Type: cross Abstract: Emergent misalignment (EM) has been linked to the activation of misaligned persona vectors and evil character traits, suggesting that EM operates through disruption of the model's aligned character rather than direct learning of harmful content.

By Arush Tagade, Shaoheng Zhou, Jiaxin Wen, Shi Feng

llms fine-tuning

arXiv AI

Jun 24

ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning

arXiv:2606. 24601v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) addresses the problem of training multiple agents that pursue collaborative, competitive, or mixed objectives.

By Anurag Akula, Satheesh K. Perepu, Abhishek Sarkar, Kaushik Dey

rag agents reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 24

Sesame: Structure-Aware Molecular Generation via Spatial Density-Map Conditioning

arXiv:2606. 23856v1 Announce Type: new Abstract: Generative molecular models for drug design are a promising direction with much active research.

By Konstantin Yatsenko, Arvind Thiagarajan

diffusion fine-tuning

arXiv Machine Learning

Jun 24

Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding

arXiv:2606. 23706v1 Announce Type: cross Abstract: The development of generalizable electroencephalography (EEG) decoding models is essential for robust brain-computer interfaces (BCI) and objective neural biomarkers in mental health.

By Baimam Boukar Jean Jacques, Brandone Fonya, Nchofon Tagha Ghogomu, Pauline Nyaboe, Kipngeno Koech

llms fine-tuning benchmarks

arXiv AI

Jun 24

When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models

arXiv:2605. 08245v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate, confidently describing content not present in the input.

By Harshvardhan Saini, Samyak Jha, Yiming Tang, Dianbo Liu

llms rag agents fine-tuning multimodal benchmarks safety

arXiv AI

Jun 24

AI-Driven Predictive Maintenance with Environmental Context Integration for Connected Vehicles: Simulation, Benchmarking, and Field Validation

arXiv:2603. 13343v3 Announce Type: replace-cross Abstract: Predictive maintenance for connected vehicles offers the potential to reduce unexpected breakdowns and improve fleet reliability, but most existing systems rely exclusively on internal diagnostic signals and are validated on simulated or industrial benchmark data.

By Kushal Khemani (Independent Researcher, India), Anjum Nazir Qureshi (Rajiv Gandhi College of Engineering Research,Technology)

fine-tuning benchmarks

arXiv AI

Jun 24

Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering

arXiv:2505. 17338v3 Announce Type: replace-cross Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitive per-scan optimization (hours for NeRF, about 30 minutes for 3DGS), making them impractical in clinical settings.

By Zhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri, Van Nguyen Nguyen, Terrence Chen, Ziyan Wu

computer-vision fine-tuning

arXiv AI

Jun 24

SURGELLM: Rethinking Multi-Task Evaluation through Task-Aware Feature Gating with Class-Balanced Normalization

arXiv:2606. 24259v1 Announce Type: cross Abstract: Fine-tuned encoders deployed across heterogeneous NLP tasks face three compounding problems: mismatched inductive biases, class-imbalance corruption of feature statistics, and no mechanism to condition attention on external lexical knowledge.

By Noor Islam S. Mohammad, Ulug Bayazit

llms nlp fine-tuning efficiency safety

arXiv AI

Jun 24

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

arXiv:2606. 23993v1 Announce Type: cross Abstract: High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage.

By Zixin Ding, Shaghayegh Emam, Giovanna Salvi, Cecilia Tosciri, Abhijith Gandrakota, Jennifer Ngadiuba, Nhan Tran, Christian Herwig, David W. Miller, Yuxin Chen

agents reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jun 24

SP-Mind: An Autonomous Reasoning Agent for Spatial Proteomics Analysis

arXiv:2606. 24235v1 Announce Type: new Abstract: Spatial proteomics enables single-cell-resolution characterization of protein expression within tissue architecture, playing a critical role in understanding tumor microenvironments and guiding precision medicine.

By Yucheng Yuan, Yuanfeng Ji, Zhongxiao Li, Ruijiang Li

agents fine-tuning benchmarks

arXiv AI

Jun 24

Weight-Space Geometry of Offline Reasoning Training

arXiv:2606. 23740v1 Announce Type: cross Abstract: Offline reinforcement-learning losses (RFT, RIFT, DFT, Offline GRPO, DPO) are widely used to distill reasoning from large teachers into smaller students, and are typically compared on downstream accuracy alone.

By Aleksandr Nikolich, Igor Kiselev, Vladimir Platonov, Karina Romanova

reinforcement-learning fine-tuning

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