arXiv Machine Learning
Jul 8
Auditing of Unlearning Algorithms
arXiv:2607. 05898v1 Announce Type: new Abstract: Evaluating whether unlearning algorithms truly remove training data influence remains an open challenge.
Fine-tuning and adaptation
LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv AI
Jul 8
Cross-Receiver Open-Set Radio Frequency Fingerprinting via Structure-First Adaptation
arXiv:2607. 02567v2 Announce Type: replace-cross Abstract: Radio frequency fingerprint identification (RFFI) provides a physical-layer credential for Internet of Things devices, but open-set decisions become fragile when a threshold calibrated on a source receiver is applied to a target receiver.
arXiv AI
Jul 8
PolicyShiftGuard: Benchmarking and Improving Policy-Adaptive Image Guardrails
arXiv:2607. 05910v1 Announce Type: cross Abstract: Image guardrails are typically trained and evaluated under a fixed safety policy, implicitly treating safety as an intrinsic property of an image.
arXiv AI
Jul 8
Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design
arXiv:2607. 06175v1 Announce Type: cross Abstract: Large language models (LLMs) can generate BPMN process models from natural-language descriptions, yet supervised fine-tuning (SFT) limits their output quality to the patterns present in the training data.
arXiv AI
Jul 8
Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety
arXiv:2607. 05407v1 Announce Type: cross Abstract: Modern artificial intelligence (AI) systems present profound new risks to child safety.
arXiv Machine Learning
Jul 8
FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models
arXiv:2607. 05711v1 Announce Type: new Abstract: Diffusion models have become a dominant paradigm for high-quality generative modeling, while post-training is essential for adapting them to diverse downstream applications.
arXiv Machine Learning
Jul 8
Performance Optimization and Comparative Analysis of Generative AI Models on Advanced Accelerators
arXiv:2607. 05400v1 Announce Type: cross Abstract: Generative AI models, such as Large Language Models (LLMs) and diffusion models, have demonstrated impressive performance across a wide range of tasks.
arXiv Machine Learning
Jul 8
FedDAF: Federated Domain Adaptation Using Model Functional Distance
arXiv:2509. 11819v2 Announce Type: replace Abstract: Federated Domain Adaptation (FDA) is a federated learning (FL) approach that improves model performance at the target client by collaborating with source clients while preserving data privacy.
arXiv Machine Learning
Jul 8
Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift
arXiv:2607. 05645v1 Announce Type: new Abstract: Deep-learning-based climate downscaling aims to learn relationships from historical low-resolution (LR) and high-resolution (HR) climate data to generate HR climate projections.
arXiv Machine Learning
Jul 8
How Can Mamba Learn In Context with Outliers and Generalize Provably?
arXiv:2510. 00399v2 Announce Type: replace Abstract: The Mamba model has gained significant attention for its computational advantages over Transformer-based models, while achieving comparable performance across a wide range of language tasks.
arXiv AI
Jul 8
Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology
arXiv:2605. 18419v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology.
arXiv AI
Jul 8
Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolation
arXiv:2607. 06481v1 Announce Type: cross Abstract: We present PACR-Video, a parameter-efficient framework for multi-shot long video extrapolation that preserves recurring entities, scene structure, visual style, and causal progression without full generator fine-tuning.
arXiv AI
Jul 8
Pitwall: Faithful Natural-Language Race-Strategy Briefings from a Calibrated Real-Time Monte Carlo Engine
arXiv:2607. 06495v1 Announce Type: cross Abstract: Live sports commentary is grounded generation under a deadline: statements concern real, named athletes, the grounding state changes every few seconds, and no reference text exists at generation time.
arXiv AI
Jul 8
SCOReD: Student-Aware CoT Optimization for Recommendation Distillation
arXiv:2607. 05734v1 Announce Type: cross Abstract: Chain-of-thought (CoT) distillation in the recommendation domain is a necessary precursor to RL training, but raw teacher traces are ill-suited to this task.
arXiv AI
Jul 8
Base Models Know How to Reason, Thinking Models Learn When
arXiv:2510. 07364v4 Announce Type: replace Abstract: What do thinking language models learn during training that their base models lack?
arXiv Machine Learning
Jul 8
Fine-Tuning Regimes Define Distinct Continual Learning Problems
arXiv:2604. 21927v3 Announce Type: replace Abstract: Continual learning (CL) studies how models acquire tasks sequentially while retaining previously learned knowledge.
arXiv Machine Learning
Jul 8
Temporal Variational Implicit Neural Representations
arXiv:2506. 01544v2 Announce Type: replace Abstract: We introduce Temporal Variational Implicit Neural Representations (TV-INRs), a probabilistic framework for modeling irregular multivariate time series that enables efficient and accurate individualized imputation and forecasting.
arXiv Machine Learning
Jul 8
Energy-Efficient GPU DVFS for Fine-Tuning of SLMs on Resource-constrained Embedded Devices
arXiv:2607. 05933v1 Announce Type: cross Abstract: Dynamic Voltage Frequency Scaling (DVFS) on resource-constrained embedded GPU platforms is essential for energy-efficient small language model (SLM) fine-tuning, as privacy- and personalization-driven adaptation increasingly requires local execution and involves repeated forward-backward optimization over many mini-batches, making it substantially more time- and energy-intensive than single-pass inference.
arXiv Machine Learning
Jul 8
Integrating GNSS-Derived Zenith Wet Delay into a Weather Foundation Model Improves Precipitation Forecasting
arXiv:2607. 05658v1 Announce Type: cross Abstract: Global Navigation Satellite Systems (GNSS), best known for positioning, also serve weather science, as atmospheric water vapour delays their signals.
Hugging Face Trending Papers
Jul 8
GemNav: Discrete-Token Visual Robot Navigation using a Multimodal Large Language Model
Visual navigation policies built on large pretrained models have so far followed a common recipe: a dedicated visual encoder, a bespoke action head, and training on thousands of hours of cross-embodiment datasets. We ask whether this recipe is necessary.