Tag
Delineate Anything v2 is a globally scalable foundation model for agricultural field boundary mapping, outperforming state-of-the-art by 103.3% relative gain in [email protected], using a 73-million-instance multi-resolution dataset spanning 61 countries and a manually curated 100-country evaluation benchmark.
The paper presents a scalable framework for multi-domain dialogue state tracking using BERT, achieving zero-shot generalization and improving performance on the SGD dataset.
This paper proposes a Context-Augmented Prompting framework that uses a GNN expert model to provide predictive hints and explanatory subgraphs to improve molecular property prediction in small language models. Experiments on MUTAG and Tox21 show accuracy gains of up to 74% over SMILES-only baselines.
SUFLECA is a weakly-supervised framework for zero-shot CAD-to-image alignment, achieving state-of-the-art accuracy on ScanNet25k by scaling up geometry-grounded feature learning from pretrained visual representations.
This study proposes a feature-guided zero-shot framework using LLMs for early chronic kidney disease screening, achieving consistent improvements with minimal community-accessible features across heterogeneous datasets.
This paper introduces RINO (RGB In and RGB Out), a unified framework that represents diverse visual information (masks, depth, etc.) as RGB images and converts visual tasks into RGB-to-RGB image editing, enabling a single model to perform zero-shot transfer across tasks.
This paper introduces SpectraReward, a training-free reward function that leverages pretrained multimodal large language models (MLLMs) as zero-shot reward models for reinforcement learning in text-to-image generation, demonstrating consistent improvements over prior methods.
Zer0Fit provides an MCP server that wraps Google's TabFM and TimesFM foundation models for zero-shot forecasting, classification, and regression tasks, running entirely locally.
Jet-Long introduces a tuning-free zero-shot method for extending LLM context length by dynamically adjusting RoPE scaling, achieving strong performance on benchmarks up to 128K context with minimal inference overhead.
MASTE is a four-stage multi-agent pipeline for zero-shot aspect sentiment triplet extraction that outperforms zero-shot and chain-of-thought baselines without using labeled data.
Introduces RMISC, a large-scale real-world multivariate time series corpus with around 200 datasets and 142 billion time points, and demonstrates that pretraining time series foundation models on real-world multivariate data improves zero-shot generalization compared to synthetic data.
Introduces Jet-Long, a zero-shot method for long-context extension that dynamically adapts rescaling factors and uses a bifocal attention mechanism, achieving efficient and high-performance processing across varying sequence lengths without retraining.
GRAFT is a per-word pronunciation conditioning mechanism for zero-shot text-to-speech that uses a spoken sample of a target word to control its pronunciation, achieving significant improvements in target-word phoneme error rates across multiple languages while preserving speaker similarity.
The paper introduces Telescope Perplexity, a metric that measures token repetition probability to detect LLM-generated text in a zero-shot manner, achieving state-of-the-art or competitive performance across diverse datasets.
This paper investigates using LLMs to rewrite fragmentary dialogue utterances for improving frozen discourse parsers, finding that zero-shot clarification is unreliable and that error repair through rewriting has a practical ceiling, suggesting rewritability prediction as a key missing capability.
This paper presents a zero-shot time-series foundation model applied to prediction and causal analysis of functional MRI and synthetic signals.
This paper presents a zero-shot evaluation of three LLMs (Claude, GPT-5.4, Gemini) on a 13-class emotion classification task, finding no model exceeds 39.9% accuracy and revealing systematic failures on specific emotions such as love, confusion, and shame.
Introduces PEC-CIR, a training-free zero-shot composed image retrieval framework that uses a Planner-Executor-Critic architecture to improve retrieval precision by structuring query construction as a multi-stage reasoning pipeline.
This paper investigates cross-lingual relation extraction for Romanian by translating the SemEval-2010 Task 8 benchmark and evaluating Gemma 4 under zero-shot, few-shot, and QLoRA fine-tuning, comparing with smaller encoder baselines.
Introduces MetaFlow, a method that trains large language models to generate zero-shot workflows for tasks by combining supervised fine-tuning and reinforcement learning with execution feedback, achieving strong generalization to untrained tasks and operator sets.