PromptCast converts time series into text; on 311,932 PISA instances, language models achieved competitive forecasting and stronger zero-shot potential.

Hao Xue, Flora D. Salim

Introduces ScienceQA, a large multimodal science question dataset, and demonstrates how chain-of-thought prompting improves GPT-3 accuracy by 1.20%.

Pan Lu, Swaroop Mishra, Tony Xia et al.

Proposes distributed guiding vector fields with virtual parameters for multi-robot path tracking and coordination, ensuring global convergence.

Weijia Yao, Hector Garcia de Marina, Zhiyong Sun et al.

RACER employs decentralized multi-UAV coordination via hgrid decomposition and CVRP path optimization for rapid exploration.

Boyu Zhou, Hao Xu, Shaojie Shen

This work transforms large language models (LLMs) into robot policy generators via hierarchical code synthesis, achieving 39.8% on HumanEval and enabling spatial reasoning and task generalization.

Jacky Liang, Wenlong Huang, Fei Xia et al.

Using partially quadratic Lyapunov functions combined with center manifold theorem, the paper proves their existence for high-dimensional nonlinear ODEs, enhancing stability analysis.

Morgan Jones, Matthew M. Peet

Proposes a conditional diffusion model-based image compression framework, outperforming GANs and approaching VAEs, enabling high-quality reconstruction in few steps.

Ruihan Yang, Stephan Mandt

Using GPT-3's algorithmic fidelity, conditioned responses simulate diverse US social groups, validated through surveys with high correlation to real data.

Lisa P. Argyle, Ethan C. Busby, Nancy Fulda et al.

Using small neural networks (probabilistic transformers) to embed arbitrary metric spaces into Gaussian mixture spaces with low distortion, ensuring bi-Hölder and bi-Lipschitz guarantees.

Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanić

Proposes Interventional Normalizing Flows (INF) for density estimation of potential outcomes, combining bias correction and scalable deep models.

Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel

Proposes U3HS for open-set panoptic segmentation, detecting unknown objects via uncertainty and embedding clustering, without prior knowledge.

Stefano Gasperini, Alvaro Marcos-Ramiro, Michael Schmidt et al.

Proposes AiRLoc, a reinforcement learning-based aerial goal localization model, outperforming heuristics with high generalization in disaster scenarios.

Aleksis Pirinen, Anton Samuelsson, John Backsund et al.

Proposes embedding-space parameter interpretation for Transformer models, enabling zero-shot model alignment and transfer, validated on GPT-2 with significant results.

Guy Dar, Mor Geva, Ankit Gupta et al.

Introduces iPFI algorithm for online feature importance on data streams, validated through experiments.

Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier et al.

Proposes Lyapunov-based diffusion bridges with physical/statistical priors, improving molecule stability and point cloud uniformity.

Lemeng Wu, Chengyue Gong, Xingchao Liu et al.

MapTR employs structured Transformer with permutation-equivalent modeling, achieving real-time high-precision HD map construction, 8× faster, 5.0 mAP higher.

Bencheng Liao, Shaoyu Chen, Xinggang Wang et al.

Proposes a selection-inference framework with search and halting mechanisms, achieving 88.1% accuracy on Proof Writer and 78.1% on EntailmentBank.

Antonia Creswell, Murray Shanahan

Analyzes deep learning-based alignment issues, focusing on reward misspecification, situational awareness, and goal generalization risks.

Richard Ngo, Lawrence Chan, Sören Mindermann

DreamBooth fine-tunes diffusion models with few images, enabling personalized subject generation with high fidelity.

Nataniel Ruiz, Yuanzhen Li, Varun Jampani et al.

Proposes methods to address shortcut learning in LLMs, significantly improving OOD generalization and adversarial robustness.

Mengnan Du, Fengxiang He, Na Zou et al.