Trending research and the full catalog - each paper linked to the benchmarks, methods, and models it introduces.
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1 May 2026
Federated differentially private protocols can communicate over many adaptive rounds and reuse each client's local samples. Existing lower bound arguments for federated DP are often restricted to noninteractive protocols or fresh batch decompositions, so the fundamental…
23 Apr 2026
This paper aims to synthesize current knowledge on generative AI in IT project management using the PRISMA methodology to provide researchers with a comprehensive perspective on techniques, applications, adoption trends, limitations, and integration across project management…
Jian Yang, Ruibin Yuan, Yizhi Li · 21 Apr 2026
TACO is a self-evolving compression framework that automatically discovers and refines compression rules from interaction trajectories to improve long-horizon agent performance while reducing token overhead.
18 Apr 2026
The present study aims to investigate a cluster cleaning algorithm that is both computationally simple and capable of solving the PU classification when the SCAR condition is unsatisfied.
Mingqiao Ye, Afshin Dehghan, Roman Bachmann · 16 Apr 2026
Tokenization is a key component of autoregressive (AR) generative models, converting raw data into more manageable units for modeling.
Stefan Schulz, Fernando Edelstein, Hannah Dröge · 13 Apr 2026
Real-time free-viewpoint rendering requires balancing multi-camera redundancy with the latency constraints of interactive applications.
Eslam Reda, Sara El-Metwally · 13 Apr 2026
Large Language Models have demonstrated remarkable capabilities in generating contextually relevant and grammatically correct text.
10 Apr 2026
Topic modeling is a branch of Natural Language Processing (NLP) that aims to organize large collections of texts into coherent groups according to word co-occurrence patterns, with Latent Dirichlet Allocation (LDA) remaining one of the most widely used and interpretable…
9 Apr 2026
Agents increasingly modify the procedures by which they solve tasks and improve themselves. Autonomy over improvement, gains in practical capability, and enlargement of computational reach are distinct properties.
7 Apr 2026
Constructing artificial lexicons that are pronounceable, typologically plausible, and semantically structured remains an open challenge in computational linguistics. Existing conlang generators either lack formal phonotactic guarantees or delegate generation to opaque,…
Qihang Yu, Ju He, Liang-Chieh Chen · 6 Apr 2026
Anticipating diverse future states is a central challenge in video world modeling.
Ziwei Liu, Jingkang Yang, Shulin Tian · 2 Apr 2026
Recent streaming video understanding methods increasingly rely on complex memory mechanisms to handle long video streams.
1 Apr 2026
Supervised fine-tuning (SFT) is a common first stage of LLM post-training, teaching the model to follow instructions and shaping its behavior as a helpful assistant. At the same time, SFT may harm the fundamental capabilities of an LLM, particularly after long pretraining: a…
1 Apr 2026
Prompt optimization improves language models without updating their weights by searching for a better system prompt, but its effectiveness varies widely across tasks. We study what makes a task amenable to prompt optimization.
1 Apr 2026
Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction. However, practical dLLM decoding still suffers from high inference latency, which limits deployment.
1 Apr 2026
We study a QA curation pipeline for improving long-context complex reasoning in large language models (LLMs). Our approach, $π^2$, constructs high-quality reasoning data through rigorous QA curation: 1) extracting and expanding tables from Wikipedia, 2) from the collected tables…
1 Apr 2026
RAG-based question-answering (QA) in specialist domains faces a cold-start problem: lack of evaluative benchmarks and absence of labeled data for post-training. We present DoRA (Domain-oriented RAG Assessment), a novel benchmark construction and evaluation framework using only a…
1 Apr 2026
End-to-end OCR for historical newspapers remains challenging, as models must handle long text sequences, degraded print quality, and complex layouts. While Transformer-based recognizers dominate current research, their quadratic complexity limits efficient paragraph-level…
1 Apr 2026
Context: Schools, training platforms, and technology firms increasingly need to assess programming proficiency at scale with transparent, reproducible methods that support personalized learning pathways.
1 Apr 2026
Deep learning-based modeling of multimodal Electronic Health Records (EHRs) has become an important approach for clinical diagnosis and risk prediction. However, due to diverse clinical workflows and privacy constraints, raw EHRs are inherently multi-level incomplete, including…
1 Apr 2026
Edge computing environments impose strict constraints on energy consumption and latency, making the deployment of deep neural networks a significant challenge. Therefore, smart and adaptive inference strategies that dynamically balance computational cost or latency with…
1 Apr 2026
Parameter space is not function space for neural network architectures. This fact, investigated as early as the 1990s under terms such as ``reverse engineering," or ``parameter identifiability", has led to the natural question of parameter space symmetries\textemdash the…
1 Apr 2026
Data assimilation (DA) integrates observational information with model predictions to improve state estimation in complex systems. While filtering provides the basis for online forecasts by using only past and present observations, it can exhibit delays and biases when the…
1 Apr 2026
Efficiently solving the Fokker-Planck equation (FPE) is central to analyzing complex parameterized stochastic systems. However, current numerical methods lack parallel computation capabilities across varying conditions, severely limiting comprehensive parameter exploration and…