As the adoption of generative AI continues to expand, developers face mounting challenges in building and deploying robust applications. The complexity of managing diverse infrastructure, ensuring compliance and safety, and maintaining flexibility in provider choices has created a pressing need for unified solutions. Traditional approaches often involve tight coupling with specific platforms, significant rework during…
Comprehension and management of large-scale software repositories is a recurring problem in contemporary software development. Although current tools shine when summarizing small code entities such as functions, they struggle to scale to repository-level artifacts such as files and packages. These more abstract summaries are vital for comprehending the intent and behavior of entire codebases, particularly…
Artificial intelligence models have advanced significantly in recent years, particularly in tasks requiring reasoning, such as mathematics, programming, and scientific problem-solving. However, these advancements come with challenges: computational inefficiency and a tendency to overthink. Overthinking in AI occurs when models engage in overly lengthy reasoning, leading to increased inference costs and slower response times without…
Text-to-speech (TTS) technology has emerged as a critical tool for bridging the gap between human and machine interaction. The demand for lifelike, emotionally resonant, and linguistically versatile voice synthesis has grown exponentially across entertainment, accessibility, customer service, and education. Traditional TTS systems, while functional, often fall short of delivering the nuanced realism required for immersive…
Heuristic designing is a practical and indispensable tool leveraged in standard fields like artificial intelligence and operations research to find satisfactory solutions to complex optimisation problems. Experts usually design them by hand, which makes the process expensive and slow. A simplification of heuristics design, without a reduction in performance, was subsequently achieved through the Automatic…
Sequences are a universal abstraction for representing and processing information, making sequence modeling central to modern deep learning. By framing computational tasks as transformations between sequences, this perspective has extended to diverse fields such as NLP, computer vision, time series analysis, and computational biology. This has driven the development of various sequence models, including transformers,…
By intertwining the development of artificial intelligence combined with large language models with reinforcement learning in high-performance computation, the newly developed Reasoning Language Models may leap beyond traditional ways of limitation applied to processing by language systems toward explicit and even structured mechanisms, enabling complex reasoning solutions across diverse realms. Such model development achievement is…
Academic paper search represents a critical yet intricate information retrieval challenge within research ecosystems. Researchers require complex search capabilities that can navigate complex, specialized knowledge domains and address nuanced, fine-grained queries. Current academic search platforms like Google Scholar struggle to handle intricate research-specific investigations. For example, specialized query-seeking studies on non-stationary reinforcement learning (RL) using…
The advancement of artificial intelligence (AI) and machine learning (ML) has enabled transformative progress across diverse fields. However, the “system domain,” which focuses on optimizing and managing foundational AI infrastructure, remains relatively underexplored. This domain involves critical tasks such as diagnosing hardware issues, optimizing configurations, managing workloads, and evaluating system performance. These tasks often present…
Large language models (LLMs) have introduced impressive capabilities, particularly in reasoning tasks. Models like OpenAI’s O1 utilize “long-thought reasoning,” where complex problems are broken into manageable steps and solutions are refined iteratively. While this approach enhances problem-solving, it comes at a cost: extended output sequences lead to increased computational time and energy use. These inefficiencies…