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Introduces ReliableTableQA, a framework for training LLMs to annotate statistical reliability of tabular QA results, showing that a small SFT set is sufficient and GRPO only helps when SFT is under-trained.
ILLUME-X is a unified multimodal model for free-form interleaved text-image generation, featuring improved data efficiency, stable training, and a comprehensive evaluation metric called ILScore. It outperforms previous models on tasks like style transfer, image decomposition, and storytelling.
CLI-Universe is a synthesis engine that generates verifiable terminal-agent tasks via multi-dimensional capability taxonomy and evidence-guided research, producing a distilled dataset of 6,000 trajectories. Fine-tuning Qwen3-32B on this dataset achieves 33.4% on Terminal-Bench 2.0, setting a new state-of-the-art for open-source models at or below 32B parameters.
This paper proposes offline preference-based trajectory evaluation for agentic systems, which compares trajectories via temporal preferences rather than binary success metrics. It shows that this approach reduces ties from roughly 75% to 35%, improving discriminative power and data efficiency across diverse benchmarks.
APEX introduces a dynamic data selection strategy for automatic prompt optimization, stratifying datasets into easy, hard, and mixed tiers to improve data efficiency, achieving significant performance gains over initial prompts on multiple benchmarks.
This paper proposes a novel active learning framework that leverages foundation model priors to jointly address class imbalance and label noise, achieving over 50% annotation savings compared to baselines across image and text domains.
This paper proposes a framework for applying tabular foundation models to industrial time series for prognostics and health management, demonstrating strong performance and data efficiency across multiple PHM tasks.
This paper proposes a domain-aware coreset construction pipeline that enables a tabular foundation model to predict flood depth with only 0.7% of the training data, achieving 98.5% of the supervised reference accuracy and allowing transfer across watersheds without retraining.
This paper empirically measures the symmetry–data exchange rate predicted by equivariance theory, finding that wrong-group symmetry constraints are actively harmful, augmentation with test-time orbit averaging matches equivariant architectures, and the theoretical |G|-fold sample complexity reduction is only weakly confirmed with wide confidence intervals. The study is explicitly exploratory and not pre-registered.
This paper proposes Decoupled Residual Denoising Diffusion Models (DRDD) for unified and data-efficient image-to-image translation, decoupling noise diffusion for domain harmonization from residual diffusion for semantic mapping.
This paper investigates the 'small-vs-large gap', where training on fewer samples with more repetitions can lead to faster learning and compute savings compared to using larger datasets, attributing the speedup to layer-wise growth enabled by sampling biases. The findings suggest that smaller datasets with repetition can be proactively leveraged as favorable inductive biases, particularly in reasoning tasks.
This paper introduces a context optimization method that uses active information seeking via Wikipedia search and browser tools, combined with a search-based training procedure, to achieve robust performance improvements across diverse domains without updating model weights.
RoboEvolve is a framework that co-evolves a VLM planner and VGM simulator for robotic manipulation, achieving data efficiency with only 500 unlabeled seed images and robust continual learning.
Researchers introduce Zero-shot World Models (ZWM), an approach that achieves visual competence comparable to state-of-the-art models while trained on minimal data (single child's visual experience) without task-specific training. This work demonstrates a path toward more data-efficient AI systems that match human developmental learning efficiency.