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Open-vocabulary object detection (OVD) aims to detect arbitrary objects with user-provided text labels. Although recent progress has enhanced zero-shot detection ability, current techniques handicap themselves with three important challenges. They heavily depend on expensive and large-scale region-level annotations, which are hard to scale. Their captions are typically short and not contextually rich, which makes them…
Developing AI systems that learn from their surroundings during execution involves creating models that adapt dynamically based on new information. In-Context Reinforcement Learning (ICRL) follows this approach by allowing AI agents to learn through trial and error while making decisions. However, this method has significant challenges when applied to complex environments with various tasks. It…
Text-to-speech (TTS) technology has made significant strides in recent years, but challenges remain in creating natural, expressive, and high-fidelity speech synthesis. Many TTS systems struggle to replicate the nuances of human speech, such as intonation, emotion, and accent, often resulting in artificial-sounding voices. Additionally, precise voice cloning remains difficult, limiting the ability to generate personalized…
The International Mathematical Olympiad (IMO) is a globally recognized competition that challenges high school students with complex mathematical problems. Among its four categories, geometry stands out as the most consistent in structure, making it more accessible and well-suited for fundamental reasoning research. Automated geometry problem-solving has traditionally followed two primary approaches: algebraic methods, such as…
Large language models (LLMs) must align with human preferences like helpfulness and harmlessness, but traditional alignment methods require costly retraining and struggle with dynamic or conflicting preferences. Test-time alignment approaches using reward models (RMs) avoid retraining but face inefficiencies due to reliance on trajectory-level rewards, which evaluate full responses rather than guiding token-by-token generation. Existing…
In this tutorial, we demonstrate the workflow for fine-tuning Mistral 7B using QLoRA with Axolotl, showing how to manage limited GPU resources while customizing the model for new tasks. We’ll install Axolotl, create a small example dataset, configure the LoRA-specific hyperparameters, run the fine-tuning process, and test the resulting model’s performance. Step 1: Prepare the…
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly in mathematical problem-solving and coding applications. Research has shown a strong correlation between the length of reasoning chains and improved accuracy in problem-solving outcomes. However, they face significant challenges: while extended reasoning processes enhance problem-solving capabilities, they often lead to inefficient solutions.…
Large language models (LLMs) are the foundation for multi-agent systems, allowing multiple AI agents to collaborate, communicate, and solve problems. These agents use LLMs to understand tasks, generate responses, and make decisions, mimicking teamwork among humans. However, efficiency lags while executing these types of systems as they are based on fixed designs that do not…
Brain-computer interfaces (BCIs) have seen significant progress in recent years, offering communication solutions for individuals with speech or motor impairments. However, most effective BCIs rely on invasive methods, such as implanted electrodes, which pose medical risks including infection and long-term maintenance issues. Non-invasive alternatives, particularly those based on electroencephalography (EEG), have been explored, but they…
As the need for high-quality training data grows, synthetic data generation has become essential for improving LLM performance. Instruction-tuned models are commonly used for this task, but they often struggle to generate diverse outputs, which is crucial for model generalization. Despite efforts such as prompting techniques that encourage variation—like conditioning on past outputs or assuming…