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🔍 tom rainforth 📂 AI & Data Science
Showing 2022 results for "tom rainforth" in AI & Data Science
AI & Data Science Preprint PDF DOI

Post-Hoc Inference of Cross-Classified Statistics from Hierarchical Bayes Survey Weights

Siu-Ming Tam · 2026

Tam [2026] shows that combining Bethel multivariate allocation with Hierarchical Bayes (HB) small area models can substantially reduce survey sample sizes while maintaining domain-level precision and …

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Prior-Aligned Data Cleaning for Tabular Foundation Models

Laure Berti-Equille · 2026

Tabular Foundation Models (TFMs) achieve state-of-the-art zero-shot accuracy on small tabular datasets by meta-learning over synthetic data-generating processes -- making them highly attractive for pr…

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Contrastive Image-Metadata Pre-Training for Materials Transmission Electron Microscopy

Georgia Channing, Debora Keller, Marta D. Rossell, Philip Torr, Rolf Erni, Stig Helveg, Henrik Eliasson · 2026

The vast majority of transmission electron microscopy (TEM) data never gets published and ends up on a backup drive until deleted to free up space. These left-over datasets are rich in detail and vari…

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Crystal structure prediction using graph neural combinatorial optimization

Stavros Gerolymatos, J. Kyle Brubaker, Martin J. A. Schuetz, Vladimir V. Gusev · 2026

Crystalline materials are widely used in technological applications, yet their discovery remains a significant challenge. As their properties are driven by structure, crystal structure prediction (CSP…

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Comparative Study of Weighted and Coupled Second- and Fourth-Order PDEs for Image Despeckling in Grayscale, Color, SAR, and Ultrasound

Manish Kumar, Rajendra K. Ray · 2026

Partial Differential Equation (PDE)-based approaches have gained significant attention in image despeckling due to their strong capability to preserve structural details while suppressing noise. Howev…

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StoryTR: Narrative-Centric Video Temporal Retrieval with Theory of Mind Reasoning

Xuanyue Zhong, Yuqiang Xie, Guanqun Bi, Jiangping Yang, Guibin Chen · 2026

Current video moment retrieval excels at action-centric tasks but struggles with narrative content. Models can see \textit{what is happening} but fail to reason \textit{why it matters}. This semantic …

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h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network

Yanru Qu, Yijie Zhang, Wenjuan Tan, Xiangzhe Kong, Xiangxin Zhou, Chaoran Cheng, Mathieu Blanchette, Jiaxuan You, Ge Liu · 2026

Accurate molecular representations are critical for drug discovery, and a central challenge lies in capturing the chemical environment of molecular fragments, as key interactions, such as H-bond and {…

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Don't Make the LLM Read the Graph: Make the Graph Think

Yuqi Sun, Tianqin Meng, George Liu, Yashraj Panwar, Lakshya Chaudhry, Munasib Ilham, Aman Chadha · 2026

We investigate whether explicit belief graphs improve LLM performance in cooperative multi-agent reasoning. Through 3,000+ controlled trials across four LLM families in the cooperative card game Hanab…

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Avionic Main Fuel Pump Simulation and Fault-Diagnosis Benchmark

Felix Leonhard Janzen, Lukas Moddemann, Alexander Diedrich, Oliver Niggemann · 2026

In many cyber-physical systems, especially in critical applications such as aeroplanes, data to train anomaly detection and diagnosis algorithms is lacking due to data protection issues and partial ob…

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Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling

Yiming Bian, Joshua M. Akey · 2026

The scalability of long-context large language models is fundamentally limited by the quadratic memory cost of exact self-attention, which often leads to out-of-memory (OOM) failures on modern hardwar…

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Enhancing Research Idea Generation through Combinatorial Innovation and Multi-Agent Iterative Search Strategies

Shuai Chen, Chengzhi Zhang · 2026

Scientific progress depends on the continual generation of innovative re-search ideas. However, the rapid growth of scientific literature has greatly increased the cost of knowledge filtering, making …

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Video-ToC: Video Tree-of-Cue Reasoning

Qizhong Tan, Zhuotao Tian, Guangming Lu, Jun Yu, Wenjie Pei · 2026

Existing Video Large Language Models (Video LLMs) struggle with complex video understanding, exhibiting limited reasoning capabilities and potential hallucinations. In particular, these methods tend t…

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DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories

Neemesh Yadav, Palakorn Achananuparp, Jing Jiang, Ee-Peng Lim · 2026

Large Language Models (LLMs) have been shown to possess Theory of Mind (ToM) abilities. However, it remains unclear whether this stems from robust reasoning or spurious correlations. We introduce Dial…

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Remask, Don't Replace: Token-to-Mask Refinement in Masked Diffusion Language Models

Lin Yao · 2026

Masked diffusion language models such as LLaDA2.1 rely on Token-to-Token (T2T) editing to correct their own generation errors: whenever a different token crosses a confidence threshold, the committed …

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PDDL-Mind: Large Language Models are Capable on Belief Reasoning with Reliable State Tracking

Wang Bill Zhu, Qiutong Tony Yi, Robin Jia, Jesse Thomason · 2026

Large language models (LLMs) perform substantially below human level on existing theory-of-mind (ToM) benchmarks, even when augmented with chain-of-thought prompting or probabilistic belief updates. W…

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Re$^2$MoGen: Open-Vocabulary Motion Generation via LLM Reasoning and Physics-Aware Refinement

Jiakun Zheng, Ting Xiao, Shiqin Cao, Xinran Li, Zhe Wang, Chenjia Bai · 2026

Text-to-motion (T2M) generation aims to control the behavior of a target character via textual descriptions. Leveraging text-motion paired datasets, existing T2M models have achieved impressive perfor…

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UniMesh: Unifying 3D Mesh Understanding and Generation

Peng Huang, Yifeng Chen, Zeyu Zhang, Hao Tang · 2026

Recent advances in 3D vision have led to specialized models for either 3D understanding (e.g., shape classification, segmentation, reconstruction) or 3D generation (e.g., synthesis, completion, and ed…

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Tabular foundation models for in-context prediction of molecular properties

Karim K. Ben Hicham, Jan G. Rittig, Martin Grohe, Alexander Mitsos · 2026

Accurate molecular property prediction is central to drug discovery, catalysis, and process design, yet real-world applications are often limited by small datasets. Molecular foundation models provide…

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Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs

Ziyang Luo, Nian Liu, Junwei Han · 2026

Omni-modal Large Language Models (Omni-MLLMs) promise a unified integration of diverse sensory streams. However, recent evaluations reveal a critical performance paradox: unimodal baselines frequently…

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MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals

Adam Lahouari, Shen Ai, Jihye Han, Jillian Hoffstadt, Philipp Hoellmer, Charlotte Infante, Pulkita Jain, Sangram Kadam, Maya M. Martirossyan, Amara McCune, Hypatia Newton, Shlok J. Paul, Willmor Pena, Jonathan Raghoonanan, Sumon Sahu, Oliver Tan, Andrea Vergara, Jutta Rogal, Mark E. Tuckerman · 2026

We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs. The first release comprises fine-tuned MACE models for nine molecular crystal…

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