Scaling inference-time computation has enabled Large Language Models (LLMs) to achieve strong reasoning performance, but inherently sequential decoding leads to substantial latency, especially on complex tasks. Recent work on adaptive parallel reasoning aims to improve inference efficiency by decomposing the problem-solving process into concurrent reasoning threads when beneficial. However, existing methods on realistic tasks are either limited to supervised behavior cloning or exhibit significant accuracy drops compared to widely-used sequential long chain-of-thought (CoT) baselines. Moreover, many require customized inference engines, complicating deployment. We introduce ThreadWeaver, a framework for adaptive parallel reasoning that achieves accuracy on par with popular sequential reasoning models of comparable size while significantly reducing inference latency. ThreadWeaver's performance stems from three key innovations: 1) a two-stage parallel trajectory generator that produces large-scale, high-quality CoT data with parallel annotations for supervised fine-tuning; 2) a trie-based training-inference co-design that enables parallel reasoning on any off-the-shelf autoregressive inference engine without modifying position embeddings or KV caches; and 3) a parallelization-aware reinforcement learning framework that teaches the model to balance accuracy with effective parallelization. Across six challenging mathematical reasoning benchmarks, ThreadWeaver trained atop Qwen3-8B achieves accuracy comparable to cutting-edge sequential reasoning models (71.9% on average and 79.9% on AIME24) while delivering up to 1.53x average speedup in token latency, establishing a new Pareto frontier between accuracy and efficiency.

State-of-the-art (SOTA) LLMs have progressed from struggling on proof-based Olympiad problems to solving most of the IMO 2025 problems, with leading systems reportedly handling 5 of 6 problems. Given this progress, we assess how well these models can grade proofs: detecting errors, judging their severity, and assigning fair scores beyond binary correctness. We study proof-analysis capabilities using a corpus of 90 Gemini 2.5 Pro-generated solutions that we grade on a 1-4 scale with detailed error annotations, and on MathArena solution sets for IMO/USAMO 2025 scored on a 0-7 scale. Our analysis shows that models can reliably flag incorrect (including subtly incorrect) solutions but exhibit calibration gaps in how partial credit is assigned. To address this, we introduce agentic workflows that extract and analyze reference solutions and automatically derive problem-specific rubrics for a multi-step grading process. We instantiate and compare different design choices for the grading workflows, and evaluate their trade-offs. Across our annotated corpus and MathArena, our proposed workflows achieve higher agreement with human grades and more consistent handling of partial credit across metrics. We release all code, data, and prompts/logs to facilitate future research.

Mathematics is essentially a matter of patience. It should be understood not by memorizing, but by discovering." The machine learning workflow is a crucial process in developing machine learning models. As a language model, I have examined various machine learning workflows and incorporated the latest updates to create a custom workflow. This workflow is continually being updated with new topics and information to ensure it remains relevant. To make the workflow easier to follow, I have created a diagram that outlines each step of the process. Additionally, I have included guiding questions to help users understand each step and ensure they are on the right track. Finally, users can access detailed pages for each topic covered in the workflow. This enables them to delve deeper into each topic and gain a more thorough understanding of the entire process. Overall, this machine learning workflow provides a comprehensive guide for users to develop effective machine learning models.

Whereas data scientists in the past have had to use quite a bit of code to come up with testing, comparing, and evaluating machine learning algorithms, there has recently been an emergence of libraries in Python that reduce that work significantly. One of those libraries is PyCaret [2], by Moez Ali, an open-source library with small amounts of code required that ultimately allows you to quickly prepare data to deploy your final model in minutes. There are several benefits, which are native to the functions of PyCaret. Some of those benefits include ease of use, efficiency, and learning about new machine learning algorithms. In addition to those more board benefits, there are also around four main steps that all PyCaret models follow that serve as easy ways to execute a process that otherwise, could take more time without this library.

To create a cartoon effect, we need to pay attention to two things; edge and color palette. Those are what make the differences between a photo and a cartoon. Before jumping to the main steps, don't forget to import the required libraries in your notebook, especially cv2 and NumPy. The first main step is loading the image. Define the read_file function, which includes the cv2_imshow to load our selected image in Google Colab.

Artificial Intelligence and machine learning have been increasingly helpful in creating and rating visual contents and stories. In this article, we list down five AI-based text-to-video products that will help any storyteller put forward their best content. About: This is a video creation platform by artificial intelligence that enables anyone without training or experience to easily create engaging video content within minutes. There are mainly three categories for this product. The Comunity category is cost-free and will provide you with 480p resolution including standard library and popular themes.

There are many different ways to do image recognition. Google recently released a new Tensorflow Object Detection API to give computer vision everywhere a boost. I added a second phase for this project where I used the Tensorflow Object Detection API on a custom dataset to build my own toy aeroplane detector. So what was the experience like? First lets understand the API.

We propose a new polynomial-time deterministic algorithm that produces an approximated solution for the traveling salesperson problem. The proposed algorithm ranks cities based on their priorities calculated using a power function of means and standard deviations of their distances from other cities and then connects the cities to their neighbors in the order of their priorities. When connecting a city, a neighbor is selected based on their neighbors' priorities calculated as another power function that additionally includes their distance from the focal city to be connected. This repeats until all the cities are connected into a single loop. The time complexity of the proposed algorithm is $O(n^2)$, where $n$ is the number of cities. Numerical evaluation shows that, despite its simplicity, the proposed algorithm produces shorter tours with less time complexity than other conventional tour construction heuristics. The proposed algorithm can be used by itself or as an initial tour generator for other more complex heuristic optimization algorithms.