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

Jul 10

MentalHospital: A Virtual Environment for Evaluating Psychiatric Clinical Encounters

arXiv:2607. 08257v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance on isolated psychiatric tasks, including dialogue, diagnosis, and treatment planning, yet existing benchmarks rarely simulate complete psychiatric clinical encounters.

By Yuming Yang, Xiao Sun, Yuanwei Zou, Zhengxiao Wu, Yun Chen, Jiang Zhong, Haoyang Zeng, Jingwang Huang, Kaiwen Wei

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jul 10

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5

arXiv:2607. 07951v1 Announce Type: new Abstract: Wildfire smoke events produce extreme PM$_{2.

By Yongcan Huang, Li Jiang, Ze Yu Liu

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 10

Modular Pretraining Enables Access Control

arXiv:2607. 08077v1 Announce Type: new Abstract: AI developers face a dual-use dilemma.

By Ethan Roland, Murat Cubuktepe, Erick Martinez, Stijn Servaes, Keenan Pepper, Mike Vaiana, Diogo Schwerz de Lucena, Judd Rosenblatt, Addie Foote, Cem Anil, Alex Cloud

fine-tuning

arXiv AI

Jul 10

COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation

arXiv:2607. 08071v1 Announce Type: cross Abstract: Online ads are essential to all businesses and ad headlines are one of their core creative component.

By Yashal Shakti Kanungo, Gyanendra Das, Pooja A, Sumit Negi

llms fine-tuning

arXiv AI

Jul 10

PS4: Proxy-Supervised Joint Training for Real Target Speaker Extraction

arXiv:2607. 08111v1 Announce Type: cross Abstract: Training target speaker extraction (TSE) models for real conversational mixtures remains challenging because large-scale training corpora and clean target speech for supervision are unavailable.

By Wanyi Ning, Wei Zhou, Yingpeng Li, Yinshang Guo, Haitao Qian, Yiming Cheng

fine-tuning multimodal benchmarks

arXiv AI

Jul 10

MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks

arXiv:2605. 21917v2 Announce Type: replace-cross Abstract: Training Vision Language Models (VLMs) for video event reasoning requires high-quality structured annotations capturing not only what happened, but when, where, why, and with what consequence, at a scale manual labelling cannot support.

By Han Zhang, Wanting Jiang, Tomasz Kornuta, Tian Zheng, Vidya Murali

llms agents reinforcement-learning fine-tuning safety data-engineering

arXiv AI

Jul 10

ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning

arXiv:2607. 07719v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning adapts a large language model to one task cheaply, but across a task sequence LoRA-style methods keep stacking low-rank updates on the same frozen weight, so each new task tends to overwrite the previous ones.

By Wentao Lu

llms fine-tuning

arXiv AI

Jul 10

Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

arXiv:2607. 08716v1 Announce Type: new Abstract: In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act.

By Yifan Wu, Lizhu Zhang, Yuhang Zhou, Mingyi Wang, Bo Peng, Serena Li, Xiangjun Fan, Zhuokai Zhao

llms agents fine-tuning

arXiv AI

Jul 10

ICDAR 2026 HIPE-OCRepair Competition on LLM-Assisted OCR Post-Correction for Historical Documents

arXiv:2607. 08143v1 Announce Type: cross Abstract: We present the results of HIPE-OCRepair-2026, an ICDAR competition on LLM-assisted OCR post-correction of historical documents.

By Maud Ehrmann, Emanuela Boros, Juri Opitz, Andrianos Michail, Florian Wagner, Simon Clematide

llms computer-vision fine-tuning

arXiv Machine Learning

Jul 10

Super Weights in LLMs and the Failure of Selective Training

arXiv:2607. 08733v1 Announce Type: new Abstract: Recent work identified Super Weights, individual parameters whose removal degrades model performance by orders of magnitude.

By Shreyas Subramanian, Adewale Akinfaderin, Akarsha Sehwag

llms fine-tuning efficiency benchmarks

arXiv AI

Jul 10

Different Teachers, Different Capabilities: Sub-1B On-Device Distillation for Structured Text Enrichment

arXiv:2607. 08268v1 Announce Type: new Abstract: High-volume structured extraction pays a large model's latency on every item, so distilling the task into a small on-device model is attractive: comparable output at a fraction of the time and cost.

By Vinay Kumar Chaganti

llms fine-tuning efficiency

arXiv Machine Learning

Jul 10

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning

arXiv:2603. 12478v2 Announce Type: replace-cross Abstract: Multimodal instruction tuning is often compute-inefficient because training budgets are spread across large mixed image-video pools whose utility is highly uneven.

By Rujie Wu, Haozhe Zhao, Hai Ci, Yizhou Wang

fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 10

Write-Protected Discrete Bottlenecks for Language-Grounded World Models: A Structural Limitation and Sufficient Fix

arXiv:2607. 08312v1 Announce Type: new Abstract: How should language interface with a world model's discrete symbol system?

By Jiayi Fang

llms robotics fine-tuning multimodal

arXiv AI

Jul 10

Concept-as-Tree: A Controllable Synthetic Data Framework Makes Stronger Personalized VLMs

arXiv:2503. 12999v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have demonstrated exceptional performance in various multi-modal tasks.

By Ruichuan An, Kai Zeng, Ming Lu, Sihan Yang, Renrui Zhang, Huitong Ji, Hao Liang, Wentao Zhang

llms fine-tuning multimodal benchmarks data-engineering

arXiv Machine Learning

Jul 10

When Does Continual Learning Require Learning

arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?

By Anne Harrington, Nayan Saxena, Michael Murphy, Anastasia Borovykh, Zeyu Yun, Sridhar Kamath, Ara Eindra Kyi, Trevor Darrell, Jitendra Malik, Yutong Bai

llms agents reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 10

DrugGen 2: A disease-aware language model for enhancing drug discovery

arXiv:2607. 08404v1 Announce Type: cross Abstract: Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease context on target behavior and therapeutic outcomes.

By Ali Motahharynia, Mohammadreza Ghaffarzadeh-Esfahani, Mahsa Sheikholeslami, Navid Mazrouei, Matin Irajpour, Yousof Gheisari, Hajar Sirous

llms reinforcement-learning fine-tuning

arXiv AI

Jul 10

Beware What You Autocomplete: Forensic Attribution of Backdoored Code Completions

arXiv:2607. 08011v1 Announce Type: cross Abstract: Large language models have enabled powerful code completion systems that assist developers by predicting subsequent lines of code.

By Anjun Gao, Yueyang Quan, Zhuqing Liu, Minghong Fang

llms fine-tuning

arXiv AI

Jul 10

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning

arXiv:2607. 08393v1 Announce Type: new Abstract: Fine-tuning LLMs to inject new knowledge faces a critical challenge: LLMs can quickly memorize new facts, yet fail to use them for downstream reasoning tasks.

By Lu Dai, Ziyang Rao, Yili Wang, Hanqing Wang, Hao Liu, Hui Xiong

llms fine-tuning

arXiv AI

Jul 10

FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning

arXiv:2607. 08368v1 Announce Type: new Abstract: With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning.

By Lingyu Qiu, Daniela Annunziata, Stefano Izzo, Fabio Giampaolo, Francesco Piccialli

fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 10

Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation

arXiv:2607. 07748v1 Announce Type: new Abstract: Large Language Models achieve strong code generation for high resource languages like Python and Java but suffer sharp performance drops on Low-Resource Programming Languages~(LRPLs) such as Julia.

By Didula Samaraweera, Anjana Supun, Srinath Perera

llms reinforcement-learning fine-tuning benchmarks

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