arXiv AI
Aug 13
CoAdapt-GUI: Joint Workflow Context and Policy Adaptation for Unseen GUI Applications
arXiv:2608. 11588v1 Announce Type: new Abstract: Mobile GUI agents remain brittle when deployed to applications absent from source training.
Fine-tuning and adaptation
LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv Machine Learning
Aug 13
Heterogeneous transfer learning for high-dimensional regression with feature mismatch
arXiv:2412. 18081v3 Announce Type: replace-cross Abstract: We study Heterogeneous Transfer Learning (HTL) for high-dimensional regression with differing feature sets.
arXiv AI
Aug 13
Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks
arXiv:2608. 11532v1 Announce Type: cross Abstract: In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy.
arXiv AI
Aug 13
Fingerprinting Text-to-Image Diffusion Models via Collapsed Generation
arXiv:2608. 11732v1 Announce Type: cross Abstract: Proprietary text-to-image diffusion models are increasingly distributed as hosted services and downloadable checkpoints, making their intellectual property (IP) protection an increasingly critical concern when model leakage, copying, or unauthorized fine-tuning is disputed.
arXiv AI
Aug 13
VLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation
arXiv:2603. 17450v2 Announce Type: replace-cross Abstract: Sequential Recommendation (SR) in multimodal settings typically relies on small frozen pretrained encoders, which limits semantic capacity and prevents Collaborative Filtering (CF) signals from being fully integrated into item representations.
arXiv AI
Aug 13
A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery
arXiv:2608. 12274v1 Announce Type: cross Abstract: Background: Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose sparing in thoracic radiotherapy.
arXiv AI
Aug 13
TELLME: Test-Enhanced Learning for Language Model Enrichment
arXiv:2608. 11788v1 Announce Type: cross Abstract: Continual pre-training (CPT) has been widely adopted as a method for domain adaptation in large language models.
arXiv Machine Learning
Aug 13
LoRAQuant: Mixed-Precision Quantization of LoRA to Ultra-Low Bits
arXiv:2510. 26690v3 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a popular technique for parameter-efficient fine-tuning of large language models (LLMs).
arXiv AI
Aug 13
Conflict and Congruency Effects in Large Language Models: In-Weight and In-Context Competition in a Verbal Conflict Task
arXiv:2608. 11510v1 Announce Type: cross Abstract: Congruency effects, observed in conflict tasks such as Stroop and flanker tasks, have been investigated for nearly a century in psychology and neuroscience, but their mechanistic basis is not fully understood.
arXiv AI
Aug 13
Program Semantic Inequivalence Game with Large Language Models
arXiv:2505. 03818v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.
arXiv Machine Learning
Aug 13
ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening
arXiv:2608. 12219v1 Announce Type: new Abstract: Treating patients with combinations of drugs reduces the risk of resistance to any individual drug.
arXiv Machine Learning
Aug 13
Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
arXiv:2608. 11544v1 Announce Type: cross Abstract: We propose CVaR-penalized Generative Particle Algorithm (CVaR-GPA), a robust, tail-agnostic algorithm for fine-tuning generative models to learn heavy-tailed distributions and capture extreme events, requiring no prior knowledge or estimation of the target's tail characteristics.
arXiv Machine Learning
Aug 13
Transferable Above-Ground Biomass (AGB) Estimation Model from Multi-Sensor Data with Sparse Field Calibration
arXiv:2608. 11638v1 Announce Type: new Abstract: Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable.
arXiv Machine Learning
Aug 13
Distillation of Foundation Models for Time-dependent PDEs
arXiv:2608. 11937v1 Announce Type: new Abstract: Foundation models for time-dependent partial differential equations (PDEs) are trained on large and diverse collections of physical systems and can generalize effectively to new downstream tasks.
arXiv Machine Learning
Aug 13
Patch-based Memory Gate Model in Time Series Foundation Model
arXiv:2509. 18751v4 Announce Type: replace Abstract: Recently reconstruction-based deep models have been widely used for time series anomaly detection, but as their capacity and generalization capability increase, these models tend to over-generalize, often reconstructing unseen anomalies accurately.
arXiv Machine Learning
Aug 13
Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport
arXiv:2608. 11342v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is a standard approach for adapting LLMs to a target distribution, but in settings such as personalization, where each author requires separate weight access, optimization, storage, and retraining, its costs become prohibitive.
arXiv AI
Aug 13
Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge
arXiv:2608. 12218v1 Announce Type: cross Abstract: Large language models are increasingly trained and deployed with long contexts that span documents, code repositories, and interaction histories.
Hugging Face Trending Papers
Aug 13
Dual-Stream Cross-Anchor Correction Grounding Long-Form Captions and the Domain Limits of Object-Level Anchors
Object hallucination in multimodal large language models arises when language priors and corpus co-occurrence bias outweigh the visual evidence, with nothing tying an individual object mention to what the image shows. Most remedies intervene at decoding time without training, yet under a unified protocol their benefit is confined to short captions;supervised fine-tuning (SFT) on a detail- rich corpus lengthens captions, but over forty percent still name absent objects.
Hugging Face Trending Papers
Aug 12
Can Vision-Language Models Assess Proxemic Risk from Egocentric Robot Images?
Assessing proxemic danger from a robot's egocentric perspective is critical for safe embodied navigation in human environments and requires both visual and contextual reasoning. We evaluate three opensource vision-language models (VLMs) (\textit{InternVL}, \textit{Qwen-VL}, and \textit{SmolVLM}) on the classification of egocentric robot images into four danger levels, comparing three prompting strategies and two rounds of QLoRA fine-tuning against a stratified random baseline.
Hugging Face Trending Papers
Aug 12
GRPO for Financial Advice Generation: Outperforming Commercial LLMs under CATE Evaluation
Generating actionable financial advice from business records demands that models integrate numerical reasoning, domain knowledge, and sound judgment, while avoiding recommendations that could harm the business. Direct supervision is difficult: historical decisions are not necessarily optimal, and high-quality free-form labels are expensive to obtain.