Trending research and the full catalog - each paper linked to the benchmarks, methods, and models it introduces.
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22 May 2026
This paper proposes a multi-layer AI framework for information landscape analysis in the context of information disorder. Rather than treating misinformation detection as a binary fact-checking task, the framework analyzes political and media content across multiple dimensions,…
22 May 2026
Query understanding in large-scale industrial search systems is typically implemented as a cascade of disparate, task-specific components. While individually optimizable, this fragmented architecture incurs high maintenance overhead and results in inconsistent behaviors,…
21 May 2026
Edge deployments of generative inference increasingly face two practical realities: per-device per-model performance is often unknown at deployment time, and it is non-stationary due to user-driven semantic events, background load, and device churn.
Eunsu Kim, Jessica R. Mindel, Kyungjin Kim · 20 May 2026
As large language models (LLMs) increasingly shape how users form, refine, and extend their goals, attributing contributions in human-AI collaboration becomes critical for users calibrating their own reliance and for evaluators assessing AI-assisted work.
19 May 2026
The growing adoption of AI in healthcare, particularly in preventive care, highlights the critical need for accessibility and precision in Medical Question Answering (MedQA).
Yang Liu, Yu-Gang Jiang, YuXuan Li · 18 May 2026
Large Audio Language Models exhibit significant trustworthiness challenges despite performance advances, requiring comprehensive frameworks addressing security vulnerabilities and defensive strategies.
Shijie Li, Kevin Qinghong Lin, Lingdong Kong · 18 May 2026
AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and simulate critique with minimal human input.
17 May 2026
We apply the Weibull distribution -- a two-parameter family from extreme-value theory -- as a diagnostic framework for element-wise weight magnitude distributions in transformers. At initialization, i.i.d.
Rongrong Ji, Xiawu Zheng, Yongkang Xie · 17 May 2026
Large language models demonstrate significant limitations in abstract reasoning abilities compared to human performance, particularly in complex 3D task understanding, while an automated benchmark pipeline shows that programmatic verification ensures solution uniqueness through cycle consistency.
15 May 2026
How should we make sense of people's interactions with AI companions-conversational systems built for ongoing, emotionally meaningful relationships? First, I argue these interactions should be understood as attachment relationships, since users display all four established…
15 May 2026
There are now multiple proposals for systems based on Large Language Models (LLMs) to conduct automated qualitative interviews, but most of the current solutions rely on proprietary LLMs, which compromises reproducibility and data security.
14 May 2026
In professional sports, a team has clinched the playoffs if they are guaranteed a postseason spot, regardless of the outcomes of any remaining games. As the season progresses, sports fans and other stakeholders are interested in precisely when, and under what conditions, their…
14 May 2026
In this work, we locate and analyze existing undergraduate Artificial Intelligence (AI) programs in the United States in Spring 2026, creating a historic record at a time of great change in this area.
12 May 2026
Imbuing Large Language Models (LLMs) with specific personas is prevalent for tailoring interaction styles, yet the impact on underlying cognitive capabilities remains unexplored.
Nithin Somasekharan, Rabi Pathak, Manushri Dhanakoti · 12 May 2026
Recent LLM-based agents have closed substantial portions of the scientific discovery loop in software-only machine-learning research, in chemistry, and in biology.
11 May 2026
Automated coaching for oral presentations sits at the intersection of computer-assisted pronunciation training (CAPT), prosody modeling, and speech synthesis, yet no prior work has systematically surveyed and compared existing systems along these dimensions.
10 May 2026
As generative AI and low-code workflow platforms become routine in software practice, a key educational question is whether the next generation of computer engineers will accept these tools as useful, usable, and worthy of sustained engagement.
Xiaobin Hu, Xinlei Yu, Xiang An · 7 May 2026
4DThinker enables vision-language models to perform dynamic spatial reasoning through 4D latent mental imagery, using scalable data generation and novel fine-tuning methods that outperform existing approaches.
7 May 2026
Autonomous agents based on Large Language Models (LLMs) are increasingly being utilized in complex software systems. However, reliability remains a significant challenge due to unpredictable failures such as hallucinations, execution errors, and inconsistent reasoning.
7 May 2026
Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating strong performance through input-dependent state transitions, albeit at considerable complexity.
7 May 2026
This research aims to solve the challenge of video retrieval from massive datasets, caused by ambiguous user queries. Prevailing single-round retrieval paradigms face a performance bottleneck, as they lack effective feedback mechanisms to handle complex search intentions.
6 May 2026
Recent research has revived and amplified interest in algorithms for undiscounted average reward reinforcement learning in infinite-horizon, non-episodic (continuing) tasks. Semi-Markov decision processes (SMDPs) are of particular interest.
6 May 2026
Students at all levels of higher education face a significant barrier in the form of information overload, which often paralyzes the initial stages of the research process and suppresses motivation.
Stefanos Pasios · 4 May 2026
A hybrid approach combining diffusion-based and image-to-image translation methods improves photorealism in synthetic datasets while maintaining semantic consistency.