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12 papers in the last four weeks, up 300% on the four weeks before. 0.1% of all new papers.

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SeekerGym: A Benchmark for Reliable Information Seeking
Apr 18, 2026Remy Kim, Minseung Lee, Shuo Li +1Skillgym Completion
University of Pennsylvania, Curation Labs · Curation Labs · Google DeepMind +1
Inductive Convolution Nuclear Norm Minimization for Tensor Completion with Arbitrary Sampling
Apr 18, 2026Wei Li, Yuyang Li, Kaile Du +2Tensor Completion Convolutional
The authors are with the School of Automation, Southeast University, Nanjing, China 210018.
FrameCrafter: Novel View Synthesis as Video Completion
Apr 9, 2026Qi Wu, Khiem Vuong, Minsik Jeon +2Video Diffusion Models Novel View Synthesis
Carnegie Mellon University
From Guessing to Placeholding: A Cost-Theoretic Framework for Uncertainty-Aware Code Completion
Apr 2, 2026Liang Zhu, Haolin Chen, Lidong Zhao +1Code Completion Completion
1Tencent, Shenzhen, China
OSCAR: Occupancy-based Shape Completion via Acoustic Neural Implicit Representations
Mar 9, 2026Magdalena Wysocki, Kadir Burak Buldu, Miruna-Alexandra Gafencu +2Ultrasound Completion
Chair for Computer Aided Medical Procedures (CAMP) Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany · Munich Center for Machine Learning (MCML), Munich, Germany · Konrad Zuse School of Excellence in Reliable AI (relAI), Germany
BRIDGE: Predicting Human Task Completion Time From Model Performance
Feb 6, 2026Fengyuan Liu, Jay Gala, Nilaksh +3Artificial Intelligence Benchmarks Psychometric Properties
Mila - Quebec AI Institute · McGill University · Polytechnique Montréal +3
Complete Identification of Deep ReLU Networks through Łukasiewicz Logic
Jan 30, 2026Yani Zhang, Helmut BölcskeiRectified Linear Unit Networks Logic
Chair for Mathematical Information Science, ETH Zürich
The $\mathbf{P}$ -Completeness of Inverted Index Traversal: On the Complexity of Evaluating Boolean Query DAGs
Jan 26, 2026Amir AavaniDirected Acyclic Graph Completion
Apple Inc., USA
ADMC: Attention-based Diffusion Model for Missing Modalities Feature Completion
Jul 8, 2025Yuhan Li, Wei Zhang, Juan Chen +3Missing Modalities Cross-Modal
National University of Defense Technology · The Fifth Electronic Research Institute of MIIT · University of Chinese Academy of Sciences +2
FastMap: Real-Time Semantic Map Completion via Bitwise Masked Modeling
Date pendingYijie Deng, Shuaihang Yuan, Congcong Wen +2Semantic Mapping Robot Navigation
NYUAD Center for Artificial Intelligence and Robotics, Abu Dhabi, UAE. · New York University Abu Dhabi, Electrical Engineering, Abu Dhabi 129188, UAE. · New York University, Electrical & Computer Engineering Dept., Brooklyn, NY 11201, USA. +1
Mr.LHDR: A Benchmark for Multimodal Real-World Long-Horizon Deep Research Agents
Date pendingMinghao Guo, Meng Cao, Sui Zhao +10Deep Research Completion
Mohamed bin Zayed University of Artificial Intelligence · University of Science and Technology of China · Zhejiang University +1
Gaussian Belief Propagation Network for Depth Completion
Date pendingJie Tang, Pingping Xie, Jian Li +1Depth Estimation Dense Prediction
National University of Defense Technology, Changsha, China · Hong Kong University of Science and Technology, Hong Kong, China

Apr 18, 2026, cs.LGJ/K move · Enter open · S save

SeekerGym: A Benchmark for Reliable Information Seeking

Remy Kim, Minseung Lee, Shuo Li, Osbert Bastani

University of Pennsylvania, Curation Labs · Curation Labs · Google DeepMind+1

Skillgym Completion

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Despite their substantial successes, AI agents continue to face fundamental challenges in terms of trustworthiness. Consider deep research agents, tasked with searching for information relevant to a given topic-while AI agents can perform effective information retrieval, there is little guarantee regarding the completeness of this information. Gaps in retrieved information can leave biases that mislead users even if the information they are given is correct and relevant. We introduce SeekerGym, a benchmark designed to evaluate the completeness of information retrieved by AI agents. In addition, SeekerGym also measures how well agents quantify their uncertainty in the completeness of their information; if an agent fails to retrieve all relevant information, it is useful for it to at least quantify how much might be missing. At a high level, each task in SeekerGym is a document (e.g., a Wikipedia article), and the AI agent must issue queries to retrieve passages from that document. Intuitively, the document comprehensively covers a topic, so the ability to retrieve its sections directly measures completeness of information retrieval. In addition to Wikipedia, we also consider machine learning survey papers, where the goal is to retrieve relevant sections of a survey paper. We benchmark several models and algorithms; the best approaches retrieve 42.5% of passages on Wikipedia and 29.2% on ML Surveys, leaving substantial room for improvement.

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