#Benchmarking

AgentGym provides a comprehensive framework for the development and assessment of large language model-based agents across diverse environments and tasks. Featuring tools such as AgentTraj-L and AgentEval, the platform allows for real-time, scalable exploration of agent capabilities in areas like web navigation and gaming. It is user-friendly and facilitates the integration and autonomous evolution of agents, achieving competitive results with current leading models.

The project leverages data-driven simulation to advance autonomous vehicle testing, focusing on non-reactive environments and effective metric analysis. It employs a unique approach that uses metrics like progress and collision time, with a bird's-eye perspective for short-term simulations. By preventing policy impact on environments, the project ensures detailed open-loop metrics that are well-aligned with closed-loop evaluations, improving traditional error assessments. As part of the AGC 2024 initiative, NAVSIM enhances benchmarking with releases such as v1.1 and v1.0, refining leaderboard features and visualization tools. Supported by the University of Tübingen and NVIDIA Research, the project contributes significantly to autonomous driving research.

Discover a detailed collection of resources including books, courses, and workshops focused on improving .NET performance. This guide features insights from conference talks, blogs, and important contributions in the open-source community. For those looking into areas like benchmarking, threading, and .NET Core enhancements, find the tools and information needed for effective application optimization, serving as a valuable reference for developers keen on mastering .NET diagnostics and performance tuning.

GenRL is an actively developed PyTorch library facilitating reproducible and accessible reinforcement learning research. It features modular implementations, unified interfaces, and over 20 tutorials, all designed to support reliable algorithm development and benchmarking, seamlessly integrating with OpenAI Gym.

CRAB is a versatile framework for deploying and evaluating multimodal language model agents across diverse environments, utilizing intuitive configuration and detailed benchmarking metrics.

Safety-Gymnasium provides a highly scalable library for benchmarking safe reinforcement learning (SafeRL) algorithms, featuring a standardized set of environments and APIs with constraint compatibility. It promotes community integration through diverse tasks such as safe navigation and velocity. Updates are ongoing to advance research in SafeRL environments.

The go-json library is designed to be a high-performance JSON encoder and decoder fully compatible with Go's encoding/json standard library. It provides flexible customization options, supports context propagation during marshalling, enables dynamic field filtering, and allows for colorful JSON string outputs. The development roadmap highlights upcoming enhancements, with user feedback actively shaping new features. Benchmark results showcase its superior speed compared to other JSON libraries, and the straightforward installation process facilitates an easy shift for developers aiming for improved JSON processing in Go.

SEED-Bench offers a structured evaluation setup for multimodal large language models with 28K expertly annotated multiple-choice questions across 34 dimensions. Encompassing both text and image generation evaluations, it includes iterations like SEED-Bench-2 and SEED-Bench-2-Plus. Designed to assess model comprehension in complex text scenarios, SEED-Bench is a valuable resource for researchers and developers looking to compare and enhance model performance. Explore datasets and engage with the leaderboard now.

This resource provides a thorough overview of key SaaS, open-source, and academic recommender systems. It details various platforms offering personalized recommendations, describes their features and considerations, and serves as a neutral guide for those interested in understanding or developing recommendation engines.

This library supports advanced self-supervised learning in computer vision using PyTorch. It offers reproducible code, comprehensive benchmarks, and a modular design, providing scalable solutions for research. Featuring models like SwAV, SimCLR, and MoCo(v2), and supporting large-scale training, VISSL helps evaluate and innovate in learning representations effectively.

This article examines the implementation of Kolmogorov-Arnold Networks (KAN) with a variety of basis functions, including B-Spline, Fourier, and Gaussian RBF, applied to the NeRF equation. Utilizing the nerfstudio framework, the project aims to advance the comprehension and application of neural radiance fields. It provides a detailed comparison of KAN model performance on an RTX-3090, highlighting metrics like speed, PSNR, and SSIM. The article is open to feedback for improvements and provides clear installation and usage instructions.

Discover a framework that improves role-playing in language models through RoleBench dataset and Context-Instruct technique. This project allows LLMs to emulate characters for complex tasks, providing over 168,000 samples for model optimization and adaptation in both proprietary and open-source settings, compatible with top AI models such as GPT-4.