Group 1 - The article discusses the increasing prevalence of video interviews and the associated challenges, particularly the potential for cheating using AI technology [1] - A specific incident is highlighted where a candidate was caught using AI during a video interview due to reflections from their glasses, demonstrating the vigilance of interviewers [1] - The article suggests that while video interviews save time for candidates, they also introduce new risks that require careful attention from interviewers to identify dishonest practices [1] Group 2 - The article presents a simple algorithm problem from LeetCode, specifically problem 1281, which involves calculating the difference between the product and sum of the digits of a given integer [2] - Two examples are provided to illustrate the problem: for the input 234, the output is 15, and for the input 4421, the output is 21, showcasing the straightforward nature of the task [2] - The problem is framed as a basic exercise in reading and processing the digits of an integer to derive the required result [2]

Core Viewpoint - The article discusses the challenges faced by job seekers, particularly in the context of campus recruitment, highlighting that the difficulties in securing a job are not solely the fault of the candidates but often due to the hiring practices of companies [1]. Group 1: Job Market Insights - Many candidates feel discouraged and anxious when they cannot find a job, leading to negative mental health outcomes such as depression [1]. - The article reveals that companies may continue to advertise job openings even after filling positions, creating a misleading impression for job seekers [1]. Group 2: Algorithm Problem - The article presents a coding problem from LeetCode, specifically problem number 209, which involves finding the minimum length of a contiguous subarray whose sum is greater than or equal to a given target [4][5]. - The problem is categorized as medium difficulty and requires the use of a sliding window technique to efficiently find the solution [6]. - The provided examples illustrate how to determine the minimum length of the subarray based on the given inputs [6].

Group 1 - The article discusses the challenges faced by students from non-prestigious universities in securing internships, particularly in the context of strict school policies against external internships [1] - Many students express frustration over the school's restrictions, which prevent them from gaining practical experience necessary for employment [1] - The author reflects on their own experience, noting that while their school did not encourage internships, it also did not prohibit them, recognizing the importance of practical experience for job placement [1] Group 2 - The article presents a coding problem from LeetCode, specifically problem number 1509, which involves minimizing the difference between the maximum and minimum values in an array after performing up to three operations [2][12] - The problem allows for changing any element in the array to any value, with the goal of achieving the smallest possible difference between the maximum and minimum values after three operations [12] - The solution involves sorting the array and considering different scenarios of removing elements to minimize the difference, particularly when the array length exceeds four [13]

Core Viewpoint - The performance of large language models (LLMs) in algorithm competitions, specifically the China Collegiate Programming Contest (CCPC), has revealed significant limitations, indicating that while these models can excel in certain tasks, they struggle with unique and creative problem-solving required in competitive programming [10][11]. Group 1: Competition Overview - The 10th China Collegiate Programming Contest (CCPC) recently took place, with Byte's Seed sponsoring and participating through Seed-Thinking, which only managed to solve a simple "check-in" problem [1][3]. - The number of problems in the CCPC final typically ranges from 10 to 13, but specific details about this year's problems have not been disclosed [1]. Group 2: Model Performance - Various models, including Seed-Thinking, o3, o4, Gemini 2.5 Pro, and DeepSeek R1, participated in the competition, with results showing that most models struggled significantly, with DeepSeek R1 failing to solve any problems [5][9]. - The models' performances were evaluated against their expected capabilities based on previous ratings, with many participants expressing surprise at the low scores achieved by these models [3][11]. Group 3: Model Architecture and Training - Seed-Thinking employs a MoE architecture with 200 billion total parameters and 20 billion active parameters, integrating various training methods for STEM problems and logical reasoning [8]. - o3 features a specialized reasoning architecture with 128 layers of Transformer, while o4-mini is optimized for efficiency, reducing parameters significantly while maintaining performance [8]. - Gemini 2.5 Pro supports multi-modal inputs and has a large context window, allowing it to handle extensive documents and codebases [8]. Group 4: Insights on Model Limitations - The results from the CCPC indicate that large models have inherent weaknesses in solving algorithmic problems, which may not be adequately addressed by their training [10][11]. - The competitive programming environment requires unique problem-solving skills that differ from the models' training data, making it challenging for them to perform well [11][12]. Group 5: Comparative Analysis - A benchmark test conducted by Microsoft on various models showed that while all models performed well on known problems, their success rates dropped significantly on unseen problems, particularly in medium and hard categories [14][17]. - Models that utilized reasoning modes demonstrated superior performance compared to their base versions, highlighting the importance of reasoning capabilities in tackling complex algorithmic challenges [17][18].