Recent advancements in large language models (LLMs) have significantly enhanced their ability to handle long contexts, making them highly effective in various tasks, from answering questions to complex reasoning. However, a critical bottleneck has emerged: the memory requirements for storing key-value (KV) caches escalate significantly as the number of model layers and the length of… →

Large language models (LLMs) have demonstrated significant reasoning capabilities, yet they face issues like hallucinations and the inability to conduct faithful reasoning. These challenges stem from knowledge gaps, leading to factual errors during complex tasks. While knowledge graphs (KGs) are increasingly used to bolster LLM reasoning, current KG-enhanced approaches—retrieval-based and agent-based—struggle with either accurate knowledge… →

Training and deploying large-scale language models (LLMs) is complex, requiring significant computational resources, technical expertise, and access to high-performance infrastructure. These barriers limit reproducibility, increase development time, and make experimentation challenging, particularly for academia and smaller research institutions. Addressing these issues requires a lightweight, flexible, and efficient approach that reduces friction in LLM research. Meta… →

Deep neural networks are powerful tools that excel in learning complex patterns, but understanding how they efficiently compress input data into meaningful representations remains a challenging research problem. Researchers from the University of California, Los Angeles, and New York University propose a new metric, called local rank, to measure the intrinsic dimensionality of feature manifolds… →

Recent advancements in Large Language Models (LLMs) have reshaped the Artificial intelligence (AI)landscape, paving the way for the creation of Multimodal Large Language Models (MLLMs). These advanced models expand AI capabilities beyond text, allowing understanding and generation of content like images, audio, and video, signaling a significant leap in AI development.  Despite the remarkable progress… →

Agentic systems have evolved rapidly in recent years, showing potential to solve complex tasks that mimic human-like decision-making processes. These systems are designed to act step-by-step, analyzing intermediate stages in tasks like humans do. However, one of the biggest challenges in this field is evaluating these systems effectively. Traditional evaluation methods focus only on the… →

One of the primary challenges in developing advanced text-to-speech (TTS) systems is the lack of expressivity when transcribing and generating speech. Traditionally, large language models (LLMs) used for building TTS pipelines convert speech to text using automatic speech recognition (ASR), process it using an LLM, and then convert the output back to speech via TTS.… →

The rapid growth of large language models (LLMs) has brought impressive capabilities, but it has also highlighted significant challenges related to resource consumption and scalability. LLMs often require extensive GPU infrastructure and enormous amounts of power, making them costly to deploy and maintain. This has particularly limited their accessibility for smaller enterprises or individual users… →

The study investigates the emergence of intelligent behavior in artificial systems by examining how the complexity of rule-based systems influences the capabilities of models trained to predict those rules. Traditionally, AI development has focused on training models using datasets that reflect human intelligence, such as language corpora or expert-annotated data. This method assumes that intelligence… →