GAICo: A Deployed and Extensible Framework for Evaluating Diverse and Multimodal Generative AI Outputs
- URL: http://arxiv.org/abs/2508.16753v2
- Date: Fri, 24 Oct 2025 15:20:55 GMT
- Title: GAICo: A Deployed and Extensible Framework for Evaluating Diverse and Multimodal Generative AI Outputs
- Authors: Nitin Gupta, Pallav Koppisetti, Kausik Lakkaraju, Biplav Srivastava,
- Abstract summary: We present GAICo (Generative AI Comparator): a deployed, open-source Python library that standardizes GenAI output comparison.<n> GAICo provides a unified, framework supporting a comprehensive suite of reference-based metrics for unstructured text, structured data formats, and multimedia.<n>Since its release on PyPI in Jun 2025, the tool has been downloaded over 13K times, across versions, by Aug 2025, demonstrating growing community interest.
- Score: 8.34331981959369
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid proliferation of Generative AI (GenAI) into diverse, high-stakes domains necessitates robust and reproducible evaluation methods. However, practitioners often resort to ad-hoc, non-standardized scripts, as common metrics are often unsuitable for specialized, structured outputs (e.g., automated plans, time-series) or holistic comparison across modalities (e.g., text, audio, and image). This fragmentation hinders comparability and slows AI system development. To address this challenge, we present GAICo (Generative AI Comparator): a deployed, open-source Python library that streamlines and standardizes GenAI output comparison. GAICo provides a unified, extensible framework supporting a comprehensive suite of reference-based metrics for unstructured text, specialized structured data formats, and multimedia (images, audio). Its architecture features a high-level API for rapid, end-to-end analysis, from multi-model comparison to visualization and reporting, alongside direct metric access for granular control. We demonstrate GAICo's utility through a detailed case study evaluating and debugging complex, multi-modal AI Travel Assistant pipelines. GAICo empowers AI researchers and developers to efficiently assess system performance, make evaluation reproducible, improve development velocity, and ultimately build more trustworthy AI systems, aligning with the goal of moving faster and safer in AI deployment. Since its release on PyPI in Jun 2025, the tool has been downloaded over 13K times, across versions, by Aug 2025, demonstrating growing community interest.
Related papers
- AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development [12.50615284537175]
Large language model (LLM) based coding agents increasingly act as autonomous contributors that generate and merge pull requests.<n>We present a longitudinal causal study of agent adoption in open-source repositories using staggered difference-in-differences with matched controls.
arXiv Detail & Related papers (2026-01-20T04:51:56Z) - A Versatile Multimodal Agent for Multimedia Content Generation [66.86040734610073]
We propose a MultiMedia-Agent designed to automate complex content creation tasks.<n>Our agent system includes a data generation pipeline, a tool library for content creation, and a set of metrics for evaluating preference alignment.
arXiv Detail & Related papers (2026-01-06T18:49:47Z) - LoCoBench-Agent: An Interactive Benchmark for LLM Agents in Long-Context Software Engineering [90.84806758077536]
We introduce textbfLoCoBench-Agent, a comprehensive evaluation framework specifically designed to assess large language models (LLMs) agents in realistic, long-context software engineering.<n>Our framework extends LoCoBench's 8,000 scenarios into interactive agent environments, enabling systematic evaluation of multi-turn conversations.<n>Our framework provides agents with 8 specialized tools (file operations, search, code analysis) and evaluates them across context lengths ranging from 10K to 1M tokens.
arXiv Detail & Related papers (2025-11-17T23:57:24Z) - Efficient and Scalable Agentic AI with Heterogeneous Systems [1.8921715645847679]
AI agents are emerging as a dominant workload in a wide range of applications, promising to be the vehicle that delivers the promised benefits of AI to enterprises and consumers.<n>To scale AI agent usage, we need efficient and scalable deployment and agent-serving infrastructure.<n>We present a system design for dynamic orchestration of AI agent workloads on heterogeneous compute infrastructure.
arXiv Detail & Related papers (2025-07-25T19:02:42Z) - ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation [51.297873393639456]
ArtifactsBench is a framework for automated visual code generation evaluation.<n>Our framework renders each generated artifact and captures its dynamic behavior through temporal screenshots.<n>We construct a new benchmark of 1,825 diverse tasks and evaluate over 30 leading Large Language Models.
arXiv Detail & Related papers (2025-07-07T12:53:00Z) - Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
arXiv Detail & Related papers (2025-06-22T16:52:48Z) - Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI [0.36868085124383626]
Review presents a comprehensive analysis of two emerging paradigms in AI-assisted software development: vibe coding and agentic coding.<n> Vibe coding emphasizes intuitive, human-in-the-loop interaction through prompt-based, conversational interaction.<n>Agentic coding enables autonomous software development through goal-driven agents capable of planning, executing, testing, and iterating tasks with minimal human intervention.
arXiv Detail & Related papers (2025-05-26T03:00:21Z) - mAIstro: an open-source multi-agentic system for automated end-to-end development of radiomics and deep learning models for medical imaging [0.0]
mAIstro is an open-source, autonomous multi-agentic framework for end-to-end development and deployment of medical AI models.<n>It orchestrates exploratory data analysis, radiomic feature extraction, image segmentation, classification, and regression through a natural language interface.
arXiv Detail & Related papers (2025-04-30T16:25:51Z) - Inference Optimization of Foundation Models on AI Accelerators [68.24450520773688]
Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI.
As the number of model parameters reaches to hundreds of billions, their deployment incurs prohibitive inference costs and high latency in real-world scenarios.
This tutorial offers a comprehensive discussion on complementary inference optimization techniques using AI accelerators.
arXiv Detail & Related papers (2024-07-12T09:24:34Z) - FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research [70.6584488911715]
retrieval-augmented generation (RAG) has attracted considerable research attention.<n>Existing RAG toolkits are often heavy and inflexibly, failing to meet the customization needs of researchers.<n>Our toolkit has implemented 16 advanced RAG methods and gathered and organized 38 benchmark datasets.
arXiv Detail & Related papers (2024-05-22T12:12:40Z) - Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL)
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
arXiv Detail & Related papers (2023-12-22T17:57:57Z) - Mystique: Enabling Accurate and Scalable Generation of Production AI
Benchmarks [2.0315147707806283]
Mystique is an accurate and scalable framework for production AI benchmark generation.
Mystique is scalable, due to its lightweight data collection, in terms of overhead runtime and instrumentation effort.
We evaluate our methodology on several production AI models, and show that benchmarks generated with Mystique closely resemble original AI models.
arXiv Detail & Related papers (2022-12-16T18:46:37Z) - ProcTHOR: Large-Scale Embodied AI Using Procedural Generation [55.485985317538194]
ProcTHOR is a framework for procedural generation of Embodied AI environments.
We demonstrate state-of-the-art results across 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation.
arXiv Detail & Related papers (2022-06-14T17:09:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.