VAST releases open-source model EdenLive for rendering simple game frames into detailed video
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VAST releases open-source model EdenLive for rendering simple game frames into detailed video

VAST, a company specializing in generative AI in 3D and behind the Tripo platform, has made the EdenLive model publicly available. This model operates in real-time and is capable of taking simplified game frames or existing video, continuously re-rendering them into detailed images in a chosen style, which can range from photorealistic to anime.

The release includes 5B and 14B model weights, code for real-time inference, a technical report, and end-to-end usage demonstrations. It is important to note that EdenLive does not simulate the world on its own; instead, a higher-level program, such as a game engine or code written by a coding agent, is responsible for enforcing rules, tracking world state, and outputting a rough reference video.

After receiving this reference video, EdenLive performs the rendering, guided by the initial frame and a text prompt. VAST asserts that this separation allows game logic to remain explicit and testable, while concentrating the model's limited real-time capabilities solely on the visual appearance.

The approach was first presented in June as part of a larger world research effort known as Project Eden. According to VAST, the 5B model can operate at a frame rate above 24 frames per second at 720p resolution on a single Nvidia RTX 5090 GPU. The README file on GitHub specifies this figure as approximately 24.7 FPS at a resolution of 1280 by 704 pixels, utilizing acceleration from the LightX2V inference framework.

The technical report reports a rate of 29.8 FPS at the same resolution on a single H100, as well as 26.8 FPS at a resolution of 832 by 480 for the 14B model running on eight H100s. VAST states that the total latency from user input to the corresponding frame is in the hundreds of milliseconds, and the system can generate content continuously for several minutes.

Both models are based on the Wan2.2-TI2V-5B and Wan2.1-I2V-14B video models. The inference code and core diffusion, VAE, and decoder weights are distributed under the Apache 2.0 license, although not all components have this license. The visual encoder uses the DINOv3 license, and the image model used in the request-to-game workspace is under the Non-Commercial FLUX License, which restricts the use of this workspace to non-commercial purposes.

VAST also introduced Game Prototype Turbo—a skill that transforms a text prompt into a rough playable game, which EdenLive can then render. VAST demonstrations include a platformer where a character freezes lava to create waypoints, a two-player fighting game where health bars and round timers are tracked by a higher-level system, and video rendering that transforms a person filmed against a blue background into a cartoon character. Furthermore, the model demonstrates replacing human hands with robotic ones in first-person footage to create additional training data for embodied AI.

VAST notes that EdenLive is currently best suited for low-frequency interactions, such as scene inspection and box-pushing puzzles. Fast, intense gameplay, maintaining multi-character consistency, fine control over small objects, and visual stability during long sessions still require refinement.

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Tencent releases open version of Hy Memory 2.0 with workflow and code memory for AI agents
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Tencent releases open version of Hy Memory 2.0 with workflow and code memory for AI agents

Tencent Hunyuan has introduced the second version of its memory framework for AI agents, Hy Memory 2.0, and made all three levels of the stack open source. These levels include a specialized memory model, the framework itself, and a local memory application called MemHub. Users can apply these components either individually or in combination.

Tencent reports that Hy Memory is already being used by internal teams in areas such as finance, news agencies, knowledge management, office agents, social entertainment, and gaming.

The framework is responsible for storing, updating, and developing three types of memory: chat, working, and code. Developers integrate it into their agents via an SDK available on PyPI under the name hy-memory 2.0.2. The new features in version 2.0 are working and code memory. They record the task goal, decisions made, intermediate results, and useful lessons, as well as the conditions applicable to these lessons. Before resuming a long-term task, the system first determines the current task, then retrieves relevant decisions, operational experience, and previous work history.

Chat memory now groups disparate facts into events, indicating time, location, and participants, merges duplicates, and resolves contradictions before passing context to the model.

Framework Testing Results

Tencent stated that it ranked first among open-source frameworks, including Mem0, MemOS, and EverOS, in four different tests. According to the company, working memory showed an average score of 55.31% in trip planning and shopping tasks on the MemoryArena platform. Code memory achieved 64.2% accuracy in question answering and 35.6% task resolution on CodeMemoryBench, as well as 28.3% task resolution on SWE-ContextBench. Chat memory demonstrated 72.6% accuracy on LoCoMo while reducing the number of tokens per question from 1159 to 703, representing a 39.3% decrease.

The layer model, named Hy-Memory-30B-A3B, is a 'mixture of experts' type model with approximately 3 billion active parameters, trained for memory retrieval and conflict resolution. This model is evaluated in a technical report on a fixed sample of 20% of benchmarks across all four memory systems. On this sample, it showed a macro average of 55.49, slightly exceeding the scores of Claude Opus 5 (54.62) and DeepSeek V4.1 Flash (53.64). The model weights are available on Hugging Face.

The MemHub application is a locally oriented tool that includes a command line and a Mac client. After authorizing connected tools, the user imports past sessions into a unified local memory store and supports synchronization of new data. Tencent notes that MemHub is currently integrating with 10 AI tools, including WorkBuddy, Cursor, Codex, Qwen Code, OpenCode, and DeepSeek Harness.

The repository license file on GitHub states that Hy-Memory is released under the Apache 2.0 license.

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