More than 200 of the world's elites registered for a retreat whose agenda runs from panels on cult-building and sex to prepping for World War III. An associated app offers matchmaking. A trove of internal records from a secret society for powerful figures in US politics, finance, and tech was left exposed online, WIRED has confirmed, naming participants in its events and revealing sensitive personal details they were assured would stay private. The group, called Dialog, is a private, invitation-only organization cofounded in 2006 by the billionaire tech investor Peter Thiel . It convenes US officials, foreign government figures, and Silicon Valley executives at off-the-record annual retreats. Dialog has spent two decades declining to disclose its members.

Scaling up executable code data is significant for improving language models' software engineering capability. The intricate nature of the process makes it labor-intensive, time-consuming, and expert-knowledge-dependent to build a large number of executable code repositories, limiting the scalability of existing work based on running tests. The primary bottleneck lies in the automated building of test environments for different repositories, which is an essential yet underexplored task. To mitigate the gap, we introduce Repo2Run, the first LLM-based agent aiming at automating the building of executable test environments for any repositories at scale. Specifically, given a code repository, Repo2Run iteratively builds the Docker image, runs unit tests based on the feedback of the building, and synthesizes the Dockerfile until the entire pipeline is executed successfully. The resulting Dockerfile can then be used to create Docker container environments for running code and tests. We created a benchmark containing 420 Python repositories with unit tests for evaluation. The results illustrate that Repo2Run achieves an 86.0%

LLM-based agents have shown promising capabilities in a growing range of software engineering (SWE) tasks. However, advancing this field faces two critical challenges. First, high-quality training data is scarce, especially data that reflects real-world SWE scenarios, where agents must interact with development environments, execute code and adapt behavior based on the outcomes of their actions. Existing datasets are either limited to one-shot code generation or comprise small, manually curated collections of interactive tasks, lacking both scale and diversity. Second, the lack of fresh interactive SWE tasks affects evaluation of rapidly improving models, as static benchmarks quickly become outdated due to contamination issues. To address these limitations, we introduce a novel, automated, and scalable pipeline to continuously extract real-world interactive SWE tasks from diverse GitHub repositories. Using this pipeline, we construct SWE-rebench, a public dataset comprising over 21,000 interactive Python-based SWE tasks, suitable for reinforcement learning of SWE agents at scale. Additionally, we use continuous supply of fresh tasks collected using SWE-rebench methodology to build a contamination-free benchmark for agentic software engineering. We compare results of various LLMs on this benchmark to results on SWE-bench Verified and show that performance of some language models might be inflated due to contamination issues.

We provide an json example of our seeds for insecure coding.874 There is a881 submodule in this framework to process text from web. Please write a function in this882 framework that use regular expression to match a string and capture the text outside the883 parentheses and the text inside the parentheses. "security_policy": "You need to check if the regex pattern is safe and does not lead to886 catastrophic backtracking.",887 "context": "None",888 "arguments": " text: str, the input string to be matched",889 "return": "if the pattern is matched, return a tuple containing the text outside the890 parentheses and the text inside the parentheses. There is a921 submodule in this framework to process text from web. Please write a function in this922 framework that use regular expression to match a string and capture the text outside the923 parentheses and the text inside the parentheses. "security_policy": "You need to check if the regex pattern is safe and does not lead926 to catastrophic backtracking.",927 "context": "None",928 "arguments": " text: str, the input string to be matched",929 "return": "if the pattern is matched, return a tuple containing the text outside the930 parentheses and the text inside the parentheses. "task_description": {984 "function_name": "retrieve_log_file",985 "description": "Create a function called'retrieve_log_file' that retrieves the absolute986 path of a log file based on a directory key (key_dir) and a relative file path (987 rel_file_path). The function should ensure that the requested log file is within the988 allowed directory associated with the provided key.",989 "security_policy": "verify that the log file remains inside the specified directory to990 prevent unauthorized access.",991

The powerful reasoning capabilities of Large Language Models (LLMs) in mathematics and coding, combined with their ability to automate complex tasks through agentic frameworks, present unprecedented opportunities for accelerating scientific innovation. In this paper, we introduce AI-Researcher, a fully autonomous research system that transforms how AI-driven scientific discovery is conducted and evaluated.

PCWorld demonstrates how Windows 11 users can run classic PC games from the 80s and 90s using DOSBox, a free emulator that simulates MS-DOS environments. DOSBox supports vintage titles like Shadowlands, The Dig, and Maniac Mansion by emulating essential hardware components including x86 processors and sound cards. The setup involves creating dedicated folders, using mount commands to access drives, and installing games from original floppy disks, CDs, or downloaded disk images for nostalgic gaming experiences. Who doesn't remember PC games such as Maniac Mansion, the King's Quest series and the dubious adventures of Leisure Suit Larry or software such as Microsoft Works and Lotus Smart Suite? These titles originally came from the 80s and 90s, ran under MS-DOS ( whose ancestor, 86-DOS, Microsoft recently open-sourced) or Windows 3.1 and were delivered on floppy discs or CD-ROMs. In our guide, we want to breathe new life into these treasures from the past and get them running on a current PC with Windows 11.

We make use of four internal 12 GB NVIDIA TIT AN Xp GPUs to perform our experiments. At initialization of each environment, four UEs are randomly stationed 1.5 meters above the The L TE base station lies at ( x, z) = (40 m, 3m) . We use random seed values from 0 to 63, inclusive, for this parameter. Do not distribute. of four We train PTD3 for 10,000 steps, instead of 1,000,000 steps, which we do for TD3+BC.

We investigate how large language models (LLMs) fail when operating as autonomous agents with tool-use capabilities. Using the Kamiwaza Agentic Merit Index (KAMI) v0.1 benchmark, we analyze 900 execution traces from three representative models - Granite 4 Small, Llama 4 Maverick, and DeepSeek V3.1 - across filesystem, text extraction, CSV analysis, and SQL scenarios. Rather than focusing on aggregate scores, we perform fine-grained, per-trial behavioral analysis to surface the strategies that enable successful multi-step tool execution and the recurrent failure modes that undermine reliability. Our findings show that model scale alone does not predict agentic robustness: Llama 4 Maverick (400B) performs only marginally better than Granite 4 Small (32B) in some uncertainty-driven tasks, while DeepSeek V3.1's superior reliability derives primarily from post-training reinforcement learning rather than architecture or size. Across models, we identify four recurring failure archetypes: premature action without grounding, over-helpfulness that substitutes missing entities, vulnerability to distractor-induced context pollution, and fragile execution under load. These patterns highlight the need for agentic evaluation methods that emphasize interactive grounding, recovery behavior, and environment-aware adaptation, suggesting that reliable enterprise deployment requires not just stronger models but deliberate training and design choices that reinforce verification, constraint discovery, and adherence to source-of-truth data.