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 Machine Learning

Jun 2

PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials

arXiv:2601. 07742v4 Announce Type: replace-cross Abstract: Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on energy, force, and stress errors can exhibit error in curvature, degrading the prediction of vibrational properties.

By Teddy Koker, Abhijeet Gangan, Mit Kotak, Jaime Marian, Tess Smidt

fine-tuning benchmarks

arXiv AI

Jun 2

Distilling Neuro-Symbolic Programs into 3D Multi-modal LLMs

arXiv:2606. 01215v1 Announce Type: cross Abstract: Current 3D spatial reasoning methods face a fundamental trade-off: neuro-symbolic 3D (NS3D) concept learners achieve interpretable reasoning through compositional programs but are constrained to closed-set concept vocabularies and simple programs; end-to-end 3D multi-modal LLMs (3D MLLMs) could handle complex natural language and open-vocabulary concepts but suffer from black-box reasoning without explicit spatial verification.

By Wentao Mo, Yang Liu

llms nlp reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 2

Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimization

arXiv:2510. 09330v3 Announce Type: replace Abstract: Ensuring that large language models (LLMs) comply with safety requirements is a central challenge in AI deployment.

By Tuan Nguyen, Long Tran-Thanh

llms agents reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 2

Scalable Inference-Time Annealing with Surrogate Likelihood Estimators

arXiv:2605. 31498v2 Announce Type: replace Abstract: A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules.

By Daniel Pe\~naherrera, Rishal Aggarwal, David Ryan Koes

diffusion fine-tuning benchmarks

arXiv Machine Learning

Jun 2

OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction

arXiv:2510. 17532v2 Announce Type: replace-cross Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data.

By Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai, Anna Ewa Choromanska

llms nlp reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 2

Task Structure Reverses Layerwise State Encoding in Sequence Models

arXiv:2606. 00926v1 Announce Type: new Abstract: Mechanistic studies of sequence models often treat layerwise state encodings as architectural traits: recurrent models concentrate readable state, attention-based models distribute it.

By Yuhang Jiang

llms fine-tuning benchmarks

arXiv AI

Jun 2

U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster

arXiv:2604. 09041v2 Announce Type: replace-cross Abstract: AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and massive computational budgets, creating a high barrier to entry.

By Salva R\"uhling Cachay, Duncan Watson-Parris, Rose Yu

diffusion fine-tuning efficiency benchmarks

arXiv AI

Jun 2

CoMIC: Collaborative Memory and Insights Circulation for Long-Horizon LLM Agents in Cloud-Edge Systems

arXiv:2606. 00756v1 Announce Type: new Abstract: Deploying lightweight Large Language Model (LLM) agents on edge servers can reduce latency and move agentic services closer to users, but resource-constrained edge models often struggle with long-horizon tasks that require persistent memory, subgoal tracking, and reflection.

By Yannan Wang, Longli Yang, Zhen Liu, Abhishek Kumar, Carsten Maple

llms agents fine-tuning

arXiv AI

Jun 2

SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning

arXiv:2512. 00062v2 Announce Type: replace-cross Abstract: Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to collect demonstrations through human operation.

By Taewook Nam, Junmo Cho, Youngsoo Jang, Sung Ju Hwang

reinforcement-learning robotics fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 2

ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning

arXiv:2606. 02576v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real-world deployment requires them to continually acquire new vision-language capabilities, making Multimodal Continual Instruction Tuning (MCIT) essential.

By Yu-Cheng Shi, Zhen-Hao Xie, Jun-Tao Tang, Da-Wei Zhou

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 2

EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction

arXiv:2606. 02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.

By Vigneshwar Hariharan (National University of Singapore), Chithra Reghuvaran (University College Dublin), Arlene John (University of Twente), Nhat Pham (Cardiff University), Omer Rana (Cardiff University), Deepu John (University College Dublin), Ganesh Neelakanta Iyer (National University of Singapore)

llms fine-tuning benchmarks

arXiv AI

Jun 2

CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding in Large VIsion-Language Models

arXiv:2605. 13178v2 Announce Type: replace-cross Abstract: In large vision-language models, visual tokens typically constitute the majority of input tokens, leading to substantial computational overhead.

By Sangin Lee, Yukyung Choi

llms fine-tuning efficiency multimodal

arXiv Machine Learning

Jun 2

Understanding LoRA as Knowledge Memory: An Empirical Analysis

arXiv:2603. 01097v3 Announce Type: replace Abstract: Continuous knowledge updating for pre-trained large language models (LLMs) is increasingly necessary yet remains challenging.

By Seungju Back, Dongwoo Lee, Naun Kang, Taehee Lee, S. K. Hong, Youngjune Gwon, Sungjin Ahn

llms rag fine-tuning

arXiv AI

Jun 2

Self-Trained Verification for Training- and Test-Time Self-Improvement

arXiv:2605. 30290v2 Announce Type: replace-cross Abstract: Self-improvement at scale has been a longstanding goal for reasoning models, and there are two natural places to do it: at test time, through verification-refinement (V-R) loops; and at training time, through self-training methods.

By Chen Henry Wu, Aditi Raghunathan

reinforcement-learning fine-tuning

arXiv AI

Jun 2

Capability Self-Assessment: Teaching LLMs to Know Their Limits

arXiv:2606. 00251v1 Announce Type: new Abstract: The ability to recognize one's own limitations and decide whether to solve a problem or delegate is fundamental for reliable intelligent systems.

By Haoyan Yang, Reza Shirkavand, Yukai Jin, Jiawei Zhou, Shangqian Gao, Heng Huang

llms reinforcement-learning fine-tuning

arXiv AI

Jun 2

Safe-FedLLM: Delving into the Safety of Federated Large Language Models

arXiv:2601. 07177v5 Announce Type: replace-cross Abstract: Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs).

By Mingxiang Tao, Yu Tian, Wenxuan Tu, Yue Yang, Xue Yang, Xiangyan Tang

llms fine-tuning safety

arXiv AI

Jun 2

Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation

arXiv:2602. 07083v2 Announce Type: replace-cross Abstract: Structural modeling is a fundamental component of computational engineering science, in which even minor physical inconsistencies or specification violations may invalidate downstream simulations.

By Yongqing Jiang, Jianze Wang, Zhiqi Shen, Zhenghong Lin, Jiayuan Wang, Yijian Yang, Kaoshan Dai, Haoran Luo

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jun 2

Enhancing LLM Metacognition via Cognitive Pairwise Training

arXiv:2606. 00869v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to give confident answers when evidence or reasoning is unreliable.

By Weitao Li, Hao Zhou, Xuanyu Lei, Fandong Meng, Yuanhang Liu, Jingyi Ren, Ante Wang, Xiaolong Wang, Yuanchi Zhang, Fuwen Luo, Guangwen Yang, Lin Gan, Weizhi Ma, Yang Liu

llms reinforcement-learning fine-tuning safety

arXiv AI

Jun 2

Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation

arXiv:2512. 16310v3 Announce Type: replace-cross Abstract: LLM-based agents increasingly use multiple external tools to complete complex tasks.

By Yuxuan Qiao, Dongqin Liu, Hongchang Yang, Wei Zhou, Songlin Hu

llms agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 2

GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks

arXiv:2506. 16114v3 Announce Type: replace-cross Abstract: Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scenarios.

By Yejing Wang, Shengyu Zhou, Jinyu Lu, Qidong Liu, Xinhang Li, Wenlin Zhang, Feng Li, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng, Xiangyu Zhao

llms reinforcement-learning fine-tuning safety

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