arXiv Machine Learning
Jun 2
Density-Aware Translation of Spurious Correlations in Zero-Shot VLMs
arXiv:2606. 01710v1 Announce Type: cross Abstract: Vision-Language models (VLMs), such as CLIP, achieve powerful zero-shot classification.
Fine-tuning and adaptation
LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv AI
Jun 2
SDR: Set-Distance Rewards for Radiology Report Generation
arXiv:2606. 00440v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has rapidly advanced reasoning in vision--language models.
arXiv AI
Jun 2
HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation
arXiv:2606. 01230v1 Announce Type: new Abstract: Large language model agents are moving beyond text-only interaction toward physical-world control, with smart homes as a representative domain.
arXiv Machine Learning
Jun 2
CANARY: Zero-Label Detection of Fine-Tuning Contamination in Language Models
arXiv:2606. 01695v1 Announce Type: new Abstract: Adversaries can implant latent harmful behavior by poisoning as few as 1% of fine-tuning examples.
arXiv Machine Learning
Jun 2
Task-Induced Representational Invariances Depend on Learning Objective in Deep RL
arXiv:2606. 01868v1 Announce Type: new Abstract: Reinforcement Learning (RL) has long served as a model for goal-directed animal behavior in neuroscience.
arXiv Machine Learning
Jun 2
Enhancing LLM Metacognition via Cognitive Pairwise Training
arXiv:2606. 00869v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to give confident answers when evidence or reasoning is unreliable.
arXiv Machine Learning
Jun 2
Reconsidering Positional Supervision in Masked Diffusion Language Model Training
arXiv:2601. 22947v2 Announce Type: replace-cross Abstract: Masked diffusion language models (MDLMs) generate text by unmasking tokens in parallel and have recently emerged as alternatives to autoregressive language models.
arXiv Machine Learning
Jun 2
LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching
arXiv:2606. 00228v1 Announce Type: new Abstract: In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask.
arXiv AI
Jun 2
Train, Test, Re-evaluate: Schedule-Sensitive Evaluation of Generative Data for Hand Detection
arXiv:2606. 01896v1 Announce Type: cross Abstract: Generated (or synthetic) image data is increasingly used to augment or replace real training datasets when target imagery is scarce, expensive, or biased.
arXiv AI
Jun 2
What Do LLMs Know About Alzheimer's Disease? Multi-loss Fine-Tuning and Probing for AD Detection
arXiv:2602. 11177v2 Announce Type: replace-cross Abstract: Reliable early detection of Alzheimer's disease (AD) is challenging, particularly due to the limited availability of labeled data.
arXiv Machine Learning
Jun 2
A Task-Centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula
arXiv:2602. 10014v3 Announce Type: replace Abstract: Iterative self-improvement fine-tunes an autoregressive large language model (LLM) on reward-verified outputs generated by the LLM itself.
arXiv Machine Learning
Jun 2
Learning Fine-grained Parameter Sharing via Sparse Tensor Decomposition
arXiv:2411. 09816v5 Announce Type: replace Abstract: Large neural networks achieve state-of-the-art performance on many tasks, yet their sheer size hinders deployment on resource-constrained devices.
arXiv AI
Jun 2
ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning
arXiv:2603. 09692v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become the standard for aligning Large Language Models (LLMs), yet its efficacy is bottlenecked by the high cost of acquiring preference data, especially in low-resource and expert domains.
arXiv Machine Learning
Jun 2
Task Structure Reverses Layerwise State Encoding in Sequence Models
arXiv:2606. 00926v1 Announce Type: new Abstract: Mechanistic studies of sequence models often treat layerwise state encodings as architectural traits: recurrent models concentrate readable state, attention-based models distribute it.
arXiv AI
Jun 2
Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift
arXiv:2606. 02045v1 Announce Type: cross Abstract: Artificial intelligence provides a practical framework for crop damage assessment from imagery data, supporting early decision-making in agricultural management.
arXiv Machine Learning
Jun 2
RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
arXiv:2505. 18877v4 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix.
arXiv Machine Learning
Jun 2
When Tabular Foundation Models Transfer Across Modalities: A Systematic Evaluation Across 95 Datasets, 7 Modalities, and Two Regimes
arXiv:2606. 02106v1 Announce Type: new Abstract: We present a single classification pipeline that combines an Equiangular Tight Frame (ETF) preprocessing stage with a tabular foundation model for in-context inference, applied identically across modalities once data is mapped to fixed vector representations.
arXiv Machine Learning
Jun 2
Decentralized Instruction Tuning: Conflict-Aware Splitting and Weight Merging
arXiv:2606. 01717v1 Announce Type: new Abstract: Instruction tuning aligns large language models, including multimodal ones, with diverse user intents, but scaling to heterogeneous mixtures is hindered by gradient interference and bandwidth-heavy synchronization.
arXiv Machine Learning
Jun 2
Latent Diffusion Pretraining for Crystal Property Prediction
arXiv:2606. 00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design.
arXiv AI
Jun 2
BAGEN: Are LLM Agents Budget-Aware?
arXiv:2606. 00198v1 Announce Type: cross Abstract: While agents are increasingly spending more resources, today agent cost is mostly measured only after execution.