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 16

Open-SWE-Traces: Advancing Dual-Mode Multilingual Distillation for Software Engineering Agents

arXiv:2606. 16038v1 Announce Type: cross Abstract: The path toward autonomous software engineering is currently bottlenecked by a severe deficit of diverse, large-scale trajectory data.

By Wasi Uddin Ahmad, Nikolai Ludwig, Somshubra Majumdar, Boris Ginsburg

llms agents fine-tuning efficiency

arXiv AI

Jun 16

G-Loss: Graph-Guided Fine-Tuning of Language Models

arXiv:2604. 25853v3 Announce Type: replace-cross Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for the global semantic structure.

By Aditya Sharma, Vinti Agarwal, Rajesh Kumar

llms rag nlp fine-tuning benchmarks

arXiv AI

Jun 16

PACT: Privileged Trace Co-Training for Multi-Turn Tool-Use Agents

arXiv:2606. 16215v1 Announce Type: cross Abstract: Multi-turn tool-use agents must reason, call tools, and adapt to observations across several interaction turns.

By Zhenbang Du, Jun Luo, Zhiwei Zheng, Xiangchi Yuan, Kejing Xia, Dachuan Shi, Qirui Jin, Qijia He, Shaofeng Zou, Yingbin Liang, Wenke Lee

agents reinforcement-learning fine-tuning

arXiv AI

Jun 16

Temporally Consistent and Controllable Video Generation of 2D Cine CMR via Latent Space Motion Modeling

arXiv:2606. 14759v1 Announce Type: cross Abstract: Cine cardiac magnetic resonance is the gold standard for assessing cardiac function, but the scarcity of public datasets limits the development of advanced data-driven models.

By Yiheng Cao (SyCoIA - IMT Mines Al\`es), Gustavo Andrade-Miranda (SyCoIA - IMT Mines Al\`es), Jiatian Zhang, Guillaume Sall\'e, Xin Gao

rag diffusion fine-tuning safety

arXiv AI

Jun 16

Pixel-TTS: Image based Text Rendering for Robust Text-to-Speech

arXiv:2606. 14750v1 Announce Type: cross Abstract: Recent advances in pixel-based text modeling show that representing text as images enables models to exploit visual cues for language understanding.

By Adarsh Arigala, Arjun Gangwar, S Umesh, Yova Kementchedjhieva

rag fine-tuning multimodal

arXiv Machine Learning

Jun 16

Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts

arXiv:2606. 15625v1 Announce Type: new Abstract: The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation.

By Yijun Lu, Zihan Fang, Pengpeng Qiao, Zheng Lin, Jing Yang, Yuxin Zhang, Por Lip Yee, Zhe Chen, Jun Luo

llms fine-tuning safety

arXiv AI

Jun 16

Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection

arXiv:2606. 15117v1 Announce Type: cross Abstract: The rapid advancement of generative AI models is leading to more realistic deepfake media, encompassing the manipulation of audio, video, or both.

By Elham Abolhasani, Maryam Ramezani, Hamid R. Rabiee

robotics fine-tuning multimodal safety

arXiv AI

Jun 16

FasterPy: An LLM-based Code Execution Efficiency Optimization Framework

arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.

By Yue Wu, Minghao Han, Ruiyin Li, Peng Liang, Amjed Tahir, Zengyang Li, Qiong Feng, Mojtaba Shahin

llms rag fine-tuning benchmarks

arXiv AI

Jun 16

Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts

arXiv:2606. 15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation.

By Aniq Ahmad, Heather Bedle, Ahmad Mustafa

llms computer-vision fine-tuning

arXiv Machine Learning

Jun 16

AME: A Multi-Type Contributor Attribution Framework in Generative AI Markets

arXiv:2606. 16075v1 Announce Type: new Abstract: Generative AI enables value creation through multi-stage collaboration among heterogeneous contributors, including training data, base models, fine-tuning behaviors, and prompts.

By Yang Shi, Songwen Pei, Yang Gao, Bingxue Zhang

fine-tuning

arXiv AI

Jun 16

LLM4RTL: Tool-Assisted LLM for RTL Generation

arXiv:2606. 15500v1 Announce Type: cross Abstract: Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems.

By Jing Jin, Robert Chu, Ning Yan, Masood S. Mortazavi

llms fine-tuning benchmarks

arXiv AI

Jun 16

QPILOTS: Efficient Test-Time Q-Steering for Flow Policies

arXiv:2606. 14801v1 Announce Type: cross Abstract: Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult.

By Yifan Ruan, Chenyang Cao, Andreas Burger, Ali Pesaranghader, Kaveh Kamali, Jaehong Kim, Nandita Vijaykumar, Alan Aspuru-Guzik, Igor Gilitschenski, Nicholas Rhinehart

diffusion reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 16

Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts

arXiv:2606. 16845v1 Announce Type: cross Abstract: Large Language Models (LLMs) natively default to literal semantic interpretations, making zero-shot irony detection a persistent challenge.

By Ankit Bhattacharjee, Krityapriya Bhaumik

llms fine-tuning benchmarks safety

arXiv AI

Jun 16

Scaling LLM Reasoning from Minimal Labels: A Semi-Supervised Framework with a Lightweight Verifier

arXiv:2606. 16811v1 Announce Type: new Abstract: For the development of Large language models (LLMs), recent approaches to generating pseudo intermediate reasoning have shown remarkable progress.

By Keizo Kato, Chenhui Chu, Yugo Murawaki, Sado Kurohashi

llms agents nlp fine-tuning benchmarks

arXiv AI

Jun 16

Latent Thought Flow: Efficient Latent Reasoning in Large Language Models

arXiv:2606. 16222v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly rely on intermediate reasoning, yet explicit Chain-of-Thought (CoT) suffers from a linguistic space bottleneck: each thought must be decoded into tokens, causing high inference overhead.

By Xiandong Zou, Jing Huang, Jianshu Li, Pan Zhou

llms fine-tuning

arXiv Machine Learning

Jun 16

PhysGuard: Fisher-Guided Gradient Projection for Sim-to-Real Neural PDE Surrogates

arXiv:2606. 16602v1 Announce Type: new Abstract: Neural operator models trained on simulation data often lose accuracy when applied to experimental measurements due to the sim-to-real gap.

By Changjian Zhou, Junfeng Fang, Negin Yousefpour, Peng Wu, Bin Yan, Guillermo A Narsilio

robotics fine-tuning benchmarks

arXiv AI

Jun 16

NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models

arXiv:2606. 15646v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but their lack of interpretable reasoning and tendency to hallucinate pose significant challenges for legal applications.

By Deepa Tilwani, Yash Saxena, Ankur Padia, Srinivasan Parthasarathy, Manas Gaur

llms rag nlp fine-tuning

arXiv AI

Jun 16

Cross-modal Identity Mapping: Minimizing Information Loss in Modality Conversion via Reinforcement Learning

arXiv:2603. 01696v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) often omit or misrepresent critical visual content in generated image captions.

By Haonan Jia, Shichao Dong, Xin Dong, Zenghui Sun, Jin Wang, Jinsong Lan, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 16

Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data

arXiv:2502. 19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL).

By Yi Zhao, Aidan Scannell, Wenshuai Zhao, Yuxin Hou, Tianyu Cui, Le Chen, Dieter B\"uchler, Arno Solin, Juho Kannala, Joni Pajarinen

reinforcement-learning robotics fine-tuning

arXiv AI

Jun 16

OmniTraffic: A Controllable Generation Pipeline and Benchmark for Spatio-Temporal Traffic Reasoning

arXiv:2606. 15749v1 Announce Type: cross Abstract: Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics.

By Maonan Wang, Zhengyan Huang, Kemou Jiang, Yuhang Fu, Jiayue Zhu, Yuxin Cai, Xingchen Zou, Qiaosheng Zhang, Yi Yu, Ding Wang, Xi Chen, Ben M. Chen, Yuxuan Liang, Zhiyong Cui, Man On Pun, Yirong Chen

fine-tuning multimodal benchmarks

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