Trending research and the full catalog - each paper linked to the benchmarks, methods, and models it introduces.
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1 Feb 2026
Several recent contributions have evaluated the Poverty of the Stimulus Hypothesis (PoSH) using Artificial Neural Networks (ANNs). The results suggest that ANN-based language models can acquire certain structure-dependent generalizations from limited input without structural…
1 Feb 2026
Scalable methods for networked multi-agent reinforcement learning let each agent plan using only a small neighborhood of the agent graph. This works only when the system is value-local, meaning a perturbation at one agent affects the long-run value at another agent weakly when…
1 Feb 2026
Physics-Informed Neural Networks present a novel approach in SciML that integrates physical laws in the form of partial differential equations directly into the NN through soft constraints in the loss function.
1 Feb 2026
Ptychographic imaging at synchrotron and X-ray free-electron laser sources requires densely overlapping scans, which limits throughput and increases dose; extending coherent diffractive imaging to overlap-free operation on extended samples remains an open problem.
1 Feb 2026
Formal semantics and distributional semantics are distinct approaches to linguistic meaning: the former models meaning as reference via model-theoretic structures; the latter as vectors in high-dimensional spaces shaped by usage.
1 Feb 2026
While Large Language Model (LLM) agents have made remarkable progress on complex reasoning, evaluating them in real-world environments remains an open problem. Existing benchmarks are largely confined to idealized simulations and fail to capture specialized domains such as…
Zhi Zheng, Meng Jiang, Yiyu Shi · 1 Feb 2026
Large language models (LLMs) have shown great potential for healthcare applications. However, existing evaluation benchmarks provide minimal coverage of Alzheimer's Disease and Related Dementias (ADRD).
Li Dong, Ming Hu, Zhonghua Wang · 31 Jan 2026
RetSAM is a comprehensive retinal imaging framework that provides accurate segmentation and standardized biomarker extraction from fundus images, supporting large-scale ophthalmic research and disease correlation studies.
31 Jan 2026
Incorporating Machine Learning (ML) into material property prediction has become a crucial step in accelerating materials discovery. A key challenge is the severe lack of training data, as many properties are too complicated to calculate with high-throughput first principles…
30 Jan 2026
Prostate cancer (PCa) is one of the most common cancers in men worldwide. Bi-parametric MRI (bp-MRI) and clinical variables are crucial for PCa identification and improving treatment decisions. However, this process is subjective to expert interpretations.
Bo Zheng, Helin Wang, Kai Li · 30 Jan 2026
Automated pipeline for sound separation using high-purity single-event segments from in-the-wild datasets achieves competitive performance with significantly reduced data requirements.
28 Jan 2026
Photoacoustic computed tomography (PACT) is a promising imaging modality that combines the advantages of optical contrast with ultrasound detection. Utilizing ultrasound transducers with larger surface areas can improve detection sensitivity.
28 Jan 2026
This paper presents a comprehensive methodology for implementing knowledge graphs in ROS 2 systems, aiming to enhance the efficiency and intelligence of autonomous robotic missions.
27 Jan 2026
We introduce the Unicamp-NAMSS dataset, a large, diverse, and geographically distributed collection of migrated 2D seismic sections designed to support modern machine learning research in geophysics.
Lei Zhang, Minghui Yu, Yongda Yu · 27 Jan 2026
AACR-Bench addresses limitations in automated code review benchmarks by providing multi-language, cross-file context with expert-verified defect annotations, revealing significant gaps in prior LLM evaluation and demonstrating that context granularity and retrieval methods critically impact ACR performance across different models and paradigms.
Xing Zhu, Yujun Shen, Wei Wu · 26 Jan 2026
Offering great potential in robotic manipulation, a capable Vision-Language-Action (VLA) foundation model is expected to faithfully generalize across tasks and platforms while ensuring cost efficiency (e.g., data and GPU hours required for adaptation).
Wenhang Ge, Pengfei Wan, Peiran Ren · 22 Jan 2026
Video generation models are categorized based on state construction and dynamics modeling approaches, with emphasis on transitioning evaluation metrics from visual quality to functional capabilities like physical persistence and causal reasoning.
Luciano del Corro, Gonzalo Ariel Meyoyan · 19 Jan 2026
Lightweight probes trained on hidden states of LLMs enable efficient classification tasks without additional computational overhead, improving safety and sentiment analysis performance.
19 Jan 2026
Predicting problem-difficulty in large language models (LLMs) refers to estimating how difficult a task is according to the model itself, typically by training linear probes on its internal representations.
Heng Ji, Yu Wang, Cheng Qian · 18 Jan 2026
Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, they struggle in open-ended and dynamic environments.
Xipeng Qiu, Yuxin Wang, Shuo Zhang · 16 Jan 2026
The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-driven problem solving.
Yi Liu, Guanghui Ren, Maoqing Yao · 16 Jan 2026
Vision-Language-Action models are enhanced by incorporating action-space reasoning through a structured sequence of coarse action intents, improving manipulation task performance in both simulation and real-world environments.
Zilong Wang, Xin Wang, Zuxuan Wu · 15 Jan 2026
The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has produced substantial gains in reasoning, perception, and generative capability across language and vision.
Caiming Xiong, Yu Wang, Xin Eric Wang · 14 Jan 2026
Research in artificial intelligence is shifting from model innovations and benchmark scores towards problem definition and rigorous real-world evaluation. As the field enters the "second half," the central challenge becomes real utility in long-horizon, dynamic, and…