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Visgym Diverse Customizable Scalable Environments

ASecurity

Implement techniques from VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents. The suite spans symbolic puzzles, real-image understanding, navigation, and manipulation, and provides flexible controls over difficulty, input representation, planning horizon, and feedback

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  • Added September 9, 2026
research

Security analysis

A100/100

Scanned September 9, 2026

npx -y skills add ADu2021/skillXiv --skill visgym-diverse-customizable-scalable-environments- --agent claude-code

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SKILL.md
---
name: visgym-diverse-customizable-scalable-environments-
title: "VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.16973"
keywords: ["agent", "model", "environment"]
description: "Implement techniques from VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents. The suite spans symbolic puzzles, real-image understanding, navigation, and manipulation, and provides flexible controls over difficulty, input representation, planning horizon, and feedback"
---

## Overview

This skill implements concepts from the research paper [[2601.16973](https://arxiv.org/abs/2601.16973)].

## When to Use

- When you need to implement techniques described in this paper
- When working on problems that this research addresses
- When you want to understand the core concepts and methodology

## When NOT to Use

- This skill provides research-level insights; production implementations may require additional engineering
- Some concepts may require significant tuning for specific use cases
- Always evaluate applicability to your specific problem domain

## Key Concepts

The paper addresses: Modern Vision-Language Models (VLMs) remain poorly characterized in multi-step visual interactions, particularly in how they integrate perception, memory, and action over long horizons. We introduce VisGym, a gymnasium of 17 environments for evaluating and training VLMs. The suite spans symbolic puz...

For detailed methodology and implementation details, refer to the [full paper](https://arxiv.org/html/2601.16973).

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