Neural intel Pod

The provided text introduces ProjectEval, a new benchmark for automatically evaluating the project-level code generation capabilities of programming agents by simulating user interactions. ProjectEval aims to address the limitations of existing benchmarks, such as a lack of automated user-centric evaluation and result explainability. The benchmark includes diverse real-world tasks with varying input levels and employs automated test suites that mimic user behavior, alongside traditional code similarity metrics. Findings from ProjectEval highlight key capabilities necessary for programming agents to create practical projects and offer insights for future development in this field.

This research introduces a novel framework using elicitation and execution policies to improve how embodied AI agents express their confidence in complex, open-ended environments like Minecraft. The authors demonstrate that structured reasoning techniques, such as Chain-of-Thought and Plan & Solve, significantly enhance an agent's ability to calibrate its confidence and predict failures. While challenges remain, particularly in abductive reasoning and model consistency, the proposed policies offer a promising approach for more reliable uncertainty assessment in embodied agents.

The provided research introduces VLM-Attention, a novel StarCraft II environment designed to better reflect human perception and decision-making by incorporating RGB visuals and natural language. This framework utilizes vision-language models with specialized mechanisms for unit targeting, knowledge retrieval for tactical decisions, and dynamic role assignment for coordinated multi-agent behavior. Experiments demonstrate that agents powered by these models can perform complex maneuvers without explicit training, rivaling traditional reinforcement learning methods. The work aims to advance human-aligned game AI and provides a new benchmark for multimodal AI in strategic games.

The provided research paper introduces a novel attack method, M-Attack, designed to effectively fool sophisticated commercial large vision-language models like GPT-4.5 and Gemini. The paper highlights the limitations of existing attack strategies which often produce uniform and semantically vague perturbations, failing against these robust models. M-Attack overcomes these issues by focusing on refining semantic details within localized image regions through random cropping and alignment in the embedding space, combined with a model ensemble to capture shared semantic features. This approach achieves surprisingly high success rates, exceeding 90% on several leading models, and introduces a new metric, KMRScore, for more objective evaluation of attack transferability. Ultimately, the work demonstrates a significant advancement in attacking state-of-the-art LVLMs by exploiting their reliance on detailed semantic understanding.

This paper introduces a deep learning-based method for the generalized inverse design of complex multi-port radio-frequency and sub-terahertz electromagnetic structures and integrated circuits. This innovative approach uses deep neural networks to accurately predict the properties of arbitrary-shaped designs, significantly speeding up the synthesis process compared to traditional methods. The authors demonstrate the effectiveness of this technique through various examples, including antennas, filters, and even a broadband millimeter-wave amplifier, showcasing its potential to overcome limitations of conventional design approaches and explore a broader design space. The presented methodology enables a rapid and automated synthesis of sophisticated high-frequency circuits.

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R1 with vision?

The provided files detail OWL, an innovative framework built upon CAMEL-AI, designed for multi-agent collaboration to automate real-world tasks. The README.md offers a comprehensive overview of OWL's features, including toolkits for web interaction, document processing, and code execution, along with installation instructions using various methods like uv, venv, conda, and Docker. The pyproject.toml outlines the project's metadata and dependencies, while README_zh.md provides a Chinese version of the main documentation. The Python scripts run_app.py and run_app_zh.py initiate a web-based user interface in English and Chinese, respectively, for interacting with OWL.

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Unsloth's documentation provides a comprehensive guide to fine-tuning large language models, particularly for beginners. It outlines the benefits of fine-tuning, such as improved domain knowledge and task-specific optimization, and compares it to methods like RAG. The guide details essential steps, including choosing the right model and fine-tuning method like LoRA or QLoRA, preparing datasets, understanding crucial parameters like learning rate and epochs, and avoiding overfitting or underfitting. Practical advice is given on installation, training, evaluation, running, and saving fine-tuned models using Unsloth's tools, even on limited hardware. Ultimately, the documentation aims to empower users to effectively customize LLMs for their specific needs through fine-tuning.