Bilim ve Araştırma

New research reveals how Stochastic Gradient Descent (SGD) exhibits a pronounced bias toward frequent tokens in language model training, potentially hindering performance on rare but meaningful words. The adaptive Adam optimizer appears to mitigate this issue through its momentum-based updates and per-parameter learning rate adjustments. This fundamental difference in implicit bias could explain Adam's dominance in modern deep learning applications.

New research reveals that advanced Large Language Models (LLMs) simulate complex 'societies of thought' with multiple internal perspectives to solve difficult problems. This emergent reasoning is being rigorously tested against new, challenging benchmarks for AI-aided chip design and kernel generation, where even top models struggle. The findings suggest a fundamental shift in how we understand and measure artificial creativity and reasoning.

A new wave of advanced AI models, termed 'Nuclear LLMs,' is emerging alongside China's release of a major AI benchmark. These developments are intensifying the global AI race and raising critical questions about measurement, policy, and the future of artificial intelligence.

DeepSeek, the Chinese AI research company, has emerged as a formidable competitor to established industry giants through its groundbreaking 2025 research papers. The company's focus on reasoning systems and autonomous agents has reportedly put major players on alert. This shift signifies a broader industry move beyond basic chatbots toward more sophisticated artificial intelligence.

A new framework called FedRE, presented at CVPR's Federated Learning workshop, addresses the critical trilemma in distributed AI. The approach uses entanglement techniques to simultaneously enhance privacy, model performance, and communication efficiency. This breakthrough could accelerate secure AI development across healthcare and other sensitive domains.

A new AI benchmark for mathematics reveals that while models like Google's Gemini can solve complex problems, they struggle to identify tasks with no solution. The SOOHAK benchmark, created by a consortium of mathematicians, highlights a critical gap in AI's research-level reasoning. No model scored above 50% in detecting deliberately broken problems.

A new research breakthrough using self-distillation techniques has shown promising results in enabling continual learning for artificial intelligence systems. This approach addresses fundamental roadblocks that have long prevented AI models from learning sequentially without forgetting previous knowledge. The development could significantly advance machine learning capabilities.

The SFT protocol is a framework of Ethereum-based smart contracts designed to tokenize debt and equity securities while maintaining regulatory compliance. It builds upon the ERC20 standard, integrating permissioning modules for KYC/AML and jurisdictional rules. This system aims to facilitate secure primary issuance and cross-border secondary trading of digital assets.

Researchers have unveiled Lighthouse Attention, a novel training-only mechanism that dramatically speeds up the pre-training of large language models on extremely long sequences. This symmetrical hierarchical approach achieves state-of-the-art efficiency while maintaining model quality. The breakthrough promises to make training on million-token contexts significantly more accessible.

Researchers from the Allen Institute for AI and UC Berkeley have developed a new mixture-of-experts model called EMO that achieves near-full performance using only 12.5 percent of its experts. This breakthrough could make large AI models practical for memory-constrained devices for the first time. The innovation centers on training experts to specialize in content domains rather than generic word types.

Researchers have developed a novel Mixture-of-Experts AI model that retains nearly full performance using only 12.5 percent of its specialized components. This breakthrough, called EMO, could make large AI models practical for memory-limited environments like smartphones and edge devices.

More than 200 academic teams from 156 universities worldwide have launched a major research initiative on how artificial intelligence will transform workplace collaboration. This global AI Grand Challenge focuses on organizational alignment, human impact, and synthetic team behavior. The initiative marks a significant step in understanding the future of work in the age of intelligent systems.

A modern re-implementation of DeepMind's AlphaGo from scratch is providing fresh insights into the core principles of artificial intelligence. The project, undertaken by researcher Eric Jang, uses contemporary AI coding tools to revisit the landmark system. This deep dive highlights how AlphaGo's fusion of search, learning, and self-play remains a crucial blueprint for understanding today's large language models and future general AI.

The proliferation of AI-generated research papers is creating a crisis of citation integrity within academia. Scientists report a surge in citations from fabricated studies, undermining trust in foundational research. This phenomenon threatens to erode the very currency of scientific discourse.

A new sheaf-theoretic framework enables AI agents to detect scientific theory shift by measuring the transport and obstruction of representational frameworks. The method identifies when an existing language fails in new data regimes, ranking potential extensions by their coherence. This diagnostic tool aims to move AI beyond simple data fitting towards more robust scientific reasoning.

A novel research framework is bridging a critical gap in AI development by steering Large Language Model-based agents to align with complex human social values and emotions. The method, which outperforms existing prompting techniques, provides a basis for the emergence of self-emotion in AI systems. This advancement tackles core deficiencies in AI self-cognition and dilemma decision-making.

Artificial intelligence is revolutionizing astronomy, helping scientists analyze vast datasets from NASA missions to uncover dark matter, exoplanets, and the universe's strangest phenomena. This article explores how AI for the universe is transforming our understanding of the cosmos.

A solo researcher trained a small AI model exclusively on its own coding and math mistakes, achieving an 80% score on HumanEval and outperforming GPT-3.5 on math benchmarks. The breakthrough reveals a low-cost path to self-improvement without human-annotated data.

Artificial intelligence is reshaping the life sciences, from drug discovery to genomic analysis. This article explores how AI for life sciences accelerates breakthroughs in biology and medicine, synthesizing insights from leading journals and educational resources.

Artificial intelligence is revolutionizing Earth science, enabling researchers to analyze vast datasets from NASA satellites and geological surveys. This article explores how AI for Earth applications are accelerating discoveries in climate modeling, natural hazard prediction, and resource management.

A groundbreaking study reveals that neural stem cell injections can reverse stroke damage in mice, restoring movement and coordination. With one in four adults over 25 facing a stroke in their lifetime, this breakthrough offers new hope for regenerative medicine.

Enterprise AI systems face a hidden crisis: inference bottlenecks that throttle performance and inflate costs. This article reveals why optimizing the model alone fails and how organizations can redesign their inference infrastructure for scale.

OpenAI’s massive 131,000-GPU training fabric relies on three counterintuitive networking decisions that challenge conventional wisdom. Meanwhile, a fresh wave of authors has sued the company over training data copyrights, raising questions about the legal foundation of AI infrastructure.

New research reveals that large language models lose track of instructions in long conversations due to a measurable 'attention closure' mechanism. The study introduces the Goal Accessibility Ratio (GAR) to predict when and why models fail, with implications for agentic AI systems.