A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Hongyin Zhu
This paper systematically categorizes and answers practical questions about LLMs across five dimensions: computing power, software architecture, data, applications, and brain science.
Komal Kumar, Tajamul Ashraf, Omkar Thawakar, et al.
A systematic survey of post-training techniques for LLMs, covering fine-tuning, reinforcement learning, and test-time scaling, with a public repository for tracking developments.
Haitao Zhao, Tiandi Xiong, Yun Chu, et al.
This paper develops biomimetic dual-network porous collagen fibers (PCFS) with optimized mechanical properties that reconstruct the neural stem cell niche via AKT/YAP mechanotransduction, promoting nerve regeneration and functional recovery after spi
Muthukuda Arachchige Dona Shiroma Jeeva Shirajanie Niriella
This paper examines AI's integration into Sri Lankan sentencing, highlighting risks of bias and proposing regulatory frameworks for equitable judicial use.
Arian Hosseini, Alessandro Sordoni, Daniel Toyama, et al.
This paper reveals a significant reasoning gap in LLMs when solving compositional math problems, showing that performance on standard benchmarks masks systematic differences in reasoning abilities.
Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.
This survey systematically categorizes and reviews methods for efficient reasoning in LLMs, addressing the overthinking phenomenon by optimizing models, outputs, and input prompts.
Ryan Sze-Yin Chan, Federico Nanni, Tomas Lazauskas, et al.
This paper presents a retrieval-augmented reasoning system using a lean Qwen2.5-Instruct model fine-tuned with synthetic reasoning traces, achieving near-frontier performance on domain-specific queries while remaining locally deployable.
Jos'ephine Raugel, Marc Szafraniec, Huy V. Vo, et al.
This paper systematically varies model size, training amount, and image type in DINOv3 vision transformers to disentangle how these factors independently and interactively drive brain-like representations, revealing a developmental chronology aligned
Neura Market
81% of n8n workflow templates that call an external system declare no error handling at all — no retry, no timeout, no error branch. A structural analysis of 5,147 deduplicated automation templates, read from their own executable specifications.
Devvrit Khatri, Lovish Madaan, Rishabh Tiwari, et al.
This paper presents the first large-scale systematic study (400,000+ GPU-hours) defining a framework for predicting RL scaling in LLMs, and proposes a best-practice recipe, ScaleRL, enabling extrapolation from small runs.
Plamen Angelov, Dimitar Filev
This paper introduces the Evolving Takagi-Sugeno (ETS) model, a recursive learning algorithm that dynamically updates fuzzy rule structure and parameters for online system identification.
Frank L. Lewis, Draguna Vrabie, Kyriakos G. Vamvoudakis
This paper unifies adaptive and optimal control using reinforcement learning to design optimal adaptive controllers for discrete- and continuous-time systems without full dynamics knowledge.