Organizations face significant challenges when deploying LLMs in today's technology landscape. The primary issues include managing the enormous computational demands required to process high volumes of data, achieving low latency, and ensuring optimal balance between CPU-intensive tasks, such as scheduling and memory allocation, and GPU-intensive computations. Repeatedly processing similar inputs further compounds the inefficiencies in many systems, leading to redundant computations that slow down overall performance. Also, generating structured outputs like JSON or XML in real-time introduces further delays, making it difficult for applications to deliver fast, reliable, cost-effective performance at scale. SGLang is an open-source inference engine designed by

Modern vision-language models have transformed how we process visual data, yet they often fall short when it comes to fine-grained localization and dense feature extraction. Many traditional models focus on high-level semantic understanding and zero-shot classification but struggle with detailed spatial reasoning. These limitations can impact applications that require precise localization, such as document analysis or object segmentation. Moreover, models that primarily rely on contrastive loss sometimes do not perform well in tasks needing refined spatial cues. There is also a challenge in supporting multiple languages and ensuring fair representation across diverse cultural contexts. Addressing these issues is essential to

Large language models (LLMs) have shown remarkable advancements in reasoning capabilities in solving complex tasks. While models like OpenAI's o1 and DeepSeek's R1 have significantly improved challenging reasoning benchmarks such as competition math, competitive coding, and GPQA, critical limitations remain in evaluating their true reasoning potential. The current reasoning datasets focus on problem-solving tasks but fail to encompass domains that require open-ended reasoning. Moreover, these datasets suffer from limited diversity in both scale and difficulty levels, making it challenging to evaluate and enhance the reasoning capabilities of LLMs across different domains and complexity levels. Previous attempts to enhance LLM reasoning

While LLMs have shown remarkable advancements in general-purpose applications, their development for specialized fields like medicine remains limited. The complexity of medical knowledge and the scarcity of high-quality, domain-specific data make creating highly efficient medical LLMs challenging. Although models like GPT-4 and DeepseekR1 have demonstrated impressive capabilities across industries, their adaptation to the medical domain is hindered by the intricate nature of medical terminology, diverse disciplines, and constantly evolving literature. Unlike general applications, medical AI must interpret highly technical language and provide precise, contextually relevant responses, which traditional LLMs struggle to achieve. One major obstacle in building effective medical LLMs

Large Language models (LLMs) operate by predicting the next token based on input data, yet their performance suggests they process information beyond mere token-level predictions. This raises questions about whether LLMs engage in implicit planning before generating complete responses. Understanding this phenomenon can lead to more transparent AI systems, improving efficiency and making output generation more predictable. One challenge in working with LLMs is predicting how they will structure responses. These models generate text sequentially, making controlling the overall response length, reasoning depth, and factual accuracy challenging. The lack of explicit planning mechanisms means that although LLMs generate human-like responses,

Hypothesis validation is fundamental in scientific discovery, decision-making, and information acquisition. Whether in biology, economics, or policymaking, researchers rely on testing hypotheses to guide their conclusions. Traditionally, this process involves designing experiments, collecting data, and analyzing results to determine the validity of a hypothesis. However, the volume of generated hypotheses has increased dramatically with the advent of LLMs. While these AI-driven hypotheses offer novel insights, their plausibility varies widely, making manual validation impractical. Thus, automation in hypothesis validation has become an essential challenge in ensuring that only scientifically rigorous hypotheses guide future research. The main challenge in hypothesis validation is

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