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

Sep 10

Event Interaction in Low-Rank Bottlenecks for Temporal Relation Extraction

arXiv:2609.06731v1 Announce Type: cross Abstract: Temporal relation extraction determines whether an event occurs before, after, or simultaneously with another event, and therefore relies on accurate...

By Wei Sun, Tingyu Qu, Jesse Davis, Marie-Francine Moens

fine-tuning

arXiv AI

Sep 10

LoGAN: Multilingual Font Localization with Generative Agents

LoGAN is a VLM-based agentic framework designed for few-shot multilingual font localization. It takes a handful of glyphs or logo letters and generates complete character sets across many languages, including CJK, by combining a glyph-level diffusion model, style finetuning, spacing/kerning transfer, and texture expansion. The method outperforms specialized font generators and state‑of‑the‑art image editors in glyph fidelity, style, texture, and kerning consistency on datasets covering more than 27 languages.

By Zhuoning Yuan, Ta-Ying Cheng, Benjamin Klein

agents diffusion fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 10

How (Mis)calibrated is your Federated CLIP and what to do about it?

arXiv:2512.04305v3 Announce Type: replace Abstract: Vision-language models (VLMs) such as CLIP are increasingly adapted across decentralized data silos, yet the reliability of their predictions under...

By Mainak Singha, Masih Aminbeidokhti, Paolo Casari, Gianni Franchi, Elisa Ricci, Subhankar Roy

llms fine-tuning multimodal

arXiv AI

Sep 10

The Geometry of Refusal: Why Post-Hoc Safety Is Fragile and Pretraining-Time Safety Persists

arXiv:2609.06934v1 Announce Type: cross Abstract: Post-hoc safety training (RLHF, DPO) is the dominant way to align large language models, yet jailbreaks (Zou et al., 2023b), fine-tuning attacks (Qi...

By Srikanth Malla, Chiho Choi, Joon Hee Choi

llms reinforcement-learning fine-tuning safety

arXiv AI

Sep 10

Foundation Models for Generalizable Semantic and Goal-Oriented Communication

The paper introduces FMSGOC, a framework that leverages visual‑linguistic foundation models to improve semantic and goal‑oriented communication for 6G. By transmitting a sparse set of semantic anchors and using a pretrained diffusion model for masked completion, it reduces overfitting and achieves high rate efficiency, reaching 0.039 BPP while maintaining strong semantic fidelity and robustness on unseen data.

By Boliang Liu, Wint Yi Poe, Riccardo Trivisonno, Giuseppe Caire

diffusion fine-tuning multimodal

arXiv Machine Learning

Sep 10

PatchFormer: A Patch-Based Time Series Foundation Model with Hierarchical Masked Reconstruction and Cross-Domain Transfer Learning for Zero-Shot Multi-Horizon Forecasting

arXiv:2601.20845v2 Announce Type: replace Abstract: Time series forecasting is a fundamental problem with applications in climate, energy, healthcare, and finance. Many existing approaches require do...

By Olaf Yunus Laitinen Imanov, Derya Umut Kulali, Taner Yilmaz

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Sep 10

Data Efficient Sample Selection for In-Context Learning

arXiv:2609.06670v1 Announce Type: new Abstract: The In-context learning (ICL) paradigm aids large language models (LLMs) to adapt to new tasks without need for fine-tuning. However, selecting an opti...

By V Venktesh, Cem levi, Avishek Anand

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Sep 10

Eliciting Self-Verification in Multimodal Reasoning Agents with Reinforcement Learning

arXiv:2609.08025v1 Announce Type: new Abstract: Reasoning agents increasingly rely on external tools such as web search to answer complex queries. Reinforcement learning (RL) finetuning algorithms su...

By Vishwas Sathish, Viresh Ranjan, Xinliang Zhu, Arnab Dhua, Douglas Gray

llms agents nlp reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Sep 10

Good Pretraining, Bad SFT: Checkpoint Quality Across the Training Stack

arXiv:2609.08966v1 Announce Type: new Abstract: Language-model checkpoints are commonly selected by pretraining loss or benchmark scores, assuming that the highest-scoring checkpoint will remain the...

By Sohir Maskey, Philipp Scholl, Jonas Knupp, Pit Neitemeier, Sascha Wirges

fine-tuning benchmarks

arXiv AI

Sep 10

Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model

arXiv:2609.06656v1 Announce Type: cross Abstract: Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load foreca...

By Varsha Pendyala, Yiwei Fu, Weizhong Yan, Nurali Virani

fine-tuning benchmarks

arXiv AI

Sep 10

Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models

arXiv:2609.06967v1 Announce Type: cross Abstract: Ensuring effective transfer learning for vision-language models without compromising their generalization performance is crucial. However, many exist...

By Seungmin Oh, Seunghun Kang, Jongbin Ryu

llms fine-tuning multimodal

arXiv AI

Sep 10

Zero-Shot Sim-to-Real Contact-Rich Assembly via Proprioception-Anchored Cross-Modal Pretraining

arXiv:2609.07534v1 Announce Type: cross Abstract: Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sustained c...

By Yuhan Wang, Yurou Chen, Hongye Jiang, Wenzhao Lian

reinforcement-learning robotics fine-tuning multimodal safety

arXiv AI

Sep 10

TRACE: Trajectory Correction from Cross-layer Evidence for Hallucination Reduction

arXiv:2605.18163v2 Announce Type: replace Abstract: Hallucination correction is not a one-direction problem. We show that intermediate layers are neither uniformly more truthful than final layers nor...

By Tej Sanibh Ranade

llms fine-tuning benchmarks safety

arXiv AI

Sep 10

LitSeg: Narrative-Aware Document Segmentation for Literary RAG

arXiv:2605.27156v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, particularly for long-tail do...

By Ruikang Zhang, Zhanni Chen, Yiqiao Cai, Qi Su

llms rag computer-vision fine-tuning efficiency benchmarks

arXiv Machine Learning

Sep 10

One Rate Is Not Enough: Adaptive Anisotropic Learning Rates for LoRA Fine-Tuning

arXiv:2609.05885v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has become the standard for parameter-efficient fine-tuning of large language models. Most LoRA variants follow a uniform-LR...

By Huiyi Wang, Daijiao Liu, Lina Yao, Dong Gong

llms fine-tuning benchmarks

arXiv Machine Learning

Sep 10

Train Smarter, Not Harder: Switching Signal-Guided Training in Active Learning

arXiv:2609.06806v1 Announce Type: new Abstract: Training strategy, namely whether to retrain from scratch or fine-tune from the previous checkpoint, is an overlooked decision variable in active learn...

By Nagham Omar, Maya Rozenshtein, Evgeny Mishlyakov, Avigdor Gal

fine-tuning

arXiv Machine Learning

Sep 10

EMBLEM: Enhancing Multi-script Table Detection through Masking

arXiv:2609.08330v1 Announce Type: new Abstract: Table detection is a core task in document analysis, supporting downstream applications such as information retrieval, document reconstruction, and vis...

By Dhruv Kudale, Udhay Brahmi, Ganesh Ramakrishnan

computer-vision nlp fine-tuning benchmarks

arXiv Computer Vision

Sep 10

Anchored, Not Graded: Vision-Language Models Fail at Slant-from-Texture Perception

arXiv:2606.06714v3 Announce Type: replace Abstract: Human perception of surface slant from texture exhibits systematic, graded biases that emerge reliably in psychophysical experiments. Prior work sh...

By Qian Zhang, Michal Golovanevsky, Fulvio Domini, James Tompkin

llms fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 10

One Loop, Two Gains: Can Active Learning win the Lottery for Free?

arXiv:2609.10311v1 Announce Type: cross Abstract: The lottery ticket hypothesis posits the existence of winning tickets: sparse subnetworks that, when trained in isolation from their original initial...

By Benedikt Tscheschner, Eduardo Veas, Marc Masana

computer-vision fine-tuning efficiency

arXiv Computation and Language

Sep 10

Why Do LLM Agents Fail in Exploring New Environments? A World-Modeling Perspective

arXiv:2510.15047v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) as agents often fail to improve in new environments. We identify and characterize a failure mode we call explora...

By Shiqi Chen, Tongyao Zhu, Zian Wang, Jinghan Zhang, Kangrui Wang, Ruochen Zhou, Siyang Gao, Teng Xiao, Yee Whye Teh, Junxian He, Manling Li

llms agents reinforcement-learning fine-tuning

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