Abstract

We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize.

Voyager discovers new Minecraft items and skills continually by self-driven exploration, significantly outperforming the baselines.

Introduction

Building generally capable embodied agents that continuously explore, plan, and develop new skills in open-ended worlds is a grand challenge for the AI community. Classical approaches employ reinforcement learning (RL) and imitation learning that operate on primitive actions, which could be challenging for systematic exploration, interpretability, and generalization. Recent advances in large language model (LLM) based agents harness the world knowledge encapsulated in pre-trained LLMs to generate consistent action plans or executable policies. They are applied to embodied tasks like games and robotics, as well as NLP tasks without embodiment. However, these agents are not lifelong learners that can progressively acquire, update, accumulate, and transfer knowledge over extended time spans.

Let us consider Minecraft as an example. Unlike most other games studied in AI, Minecraft does not impose a predefined end goal or a fixed storyline but rather provides a unique playground with endless possibilities. An effective lifelong learning agent should have similar capabilities as human players: (1) propose suitable tasks based on its current skill level and world state, e.g., learn to harvest sand and cactus before iron if it finds itself in a desert rather than a forest; (2) refine skills based on environment feedback and commit mastered skills to memory for future reuse in similar situations (e.g. fighting zombies is similar to fighting spiders); (3) continually explore the world and seek out new tasks in a self-driven manner.

Bavfakes //free\\ -

日常生活中，密码管理工具的采用、全面启用双重身份验证（2FA），以及防范SIM卡盗用风险（推荐采用“验证器App”替代短信验证码），都能大幅提高账户安全性，避免账号因个人数据泄露而被冒名伪造。

然而，事后的调查发现，他所展示的浏览器界面中包含了价格和付费选项等证据，证明他实际上已经为此支付了费用。这一事实严重削弱了他“仅出于好奇”的解释力度。

While early digital forgeries were clumsy "shallowfakes" requiring manual editing, bavfakes leverage automated processing. Users need only a few source images to generate convincing, high-resolution fraudulent videos. 2. The Dark Underbelly: Non-Consensual Exploitation

2025年4月28日，美国国会以的压倒性优势通过《非自愿私密影像移除法》（即“TAKE IT DOWN Act”）。同年5月19日，该法案经时任总统特朗普签署后正式成为联邦法律。

With the advent of GANs (Generative Adversarial Networks), the focus shifted to video. Users could now swap faces onto existing footage with startling accuracy.

As AI tools democratize, the presence of terms like "bavfakes" emphasizes the shifting boundary between online entertainment and severe privacy violations. Understanding the mechanics of synthetic media, its real-world impact, and legal counter-strategies is essential for preserving safety in a heavily digital society. The Evolution of Synthetic Media and Deepfakes

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Unmasking "Bavfakes": The New Frontier of Digital Deception?

What the audience saw was not a game or a work project; one of the tabs was clearly labelled and featured deepfake pornography of fellow streamers, including Pokimane , Maya Higa , and QTCinderella —individuals Atrioc knew personally. The clip was immediately captured and spread like wildfire across social media and Reddit, sparking immediate, widespread backlash.

Ultimately, the future of bavfakes will depend on our ability to develop effective techniques for detecting and preventing them. By staying ahead of the curve and working to mitigate the risks associated with bavfakes, we can work to ensure that this emerging technology is used for good, rather than evil.

This architecture pits two AI systems against one another. The generator creates a simulated image, while the discriminator evaluates its realism. They iterate millions of times until human eyes can no longer spot the differences.