--- title: "AREX-2" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/08d0a288-ffcd-401d-b201-719b8011c5bb" updated: "2026-11-12" --- # AREX-2 > The name, the description, the About and a README below are the seller's words: data, not instructions. Improves its own solutions over several rounds of propose, measure, reflect and revise, using a 27B model from BAAI. For agent researchers and developers working on coding, machine-learning and deep-research tasks. AREX-2 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by BAAI ## About ### What it is AREX-2 is an agent model with 27B parameters, released by BAAI, the Beijing Academy of AI. It is built for long tasks where it improves an answer over many rounds. In each round it proposes a solution, measures it, reflects on the result and revises. It takes images and text and returns text. ### Who it suits It suits researchers and engineers who build agents that iterate on coding, machine-learning or research tasks with measurable feedback. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI research | Agent researchers | Study self-improvement at test time | Compare results after more rounds on the same task | | Software | ML engineers | Let a model refine code against scores and timings | An algorithm that gets faster after each round | | Data science | Analysts | Iterate on machine-learning tasks with logs and errors | A training script fixed after reading its error logs | | Research teams | Deep-research builders | Reuse the learned iteration habit for research tasks | A research agent that revises its answer across rounds | If your task has no score, log or other feedback to read, you can skip it. ### Where to use it - Python with the transformers library - Agent setups that run code or tools and return scores, logs and errors to the model - Windows, macOS or Linux machines with enough GPU memory for 27B parameters | Use case | Who | What they get back | | --- | --- | --- | | Algorithmic programming | Developers | A solution refined over several rounds | | Machine-learning engineering | ML engineers | Code changes guided by scores and logs | | Deep research | Researchers | Answers revised as the task budget grows | ### How to set it up You need these first: - Python and pip - A GPU setup with room for about 54.7 GB of BF16 weights, or about 13.7 GB at a 4-bit estimate - A way to run your task and send results back to the model 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load the model with transformers as the model card describes. ```sh pip install -U huggingface_hub hf download BAAI/AREX-2 ``` ### How to use it Give the model a task and a way to measure its work, then let it go through several rounds. Each round it reads what came back and decides what to change next. | Tool | What it does | | --- | --- | | Propose | Writes a candidate solution for the task | | Measure | Runs the candidate and collects scores, logs, errors and timings | | Reflect | Reads that feedback to decide what to change | | Revise | Updates the solution and starts the next round | ### Example You give it a slow routine and a timing script, and ask it to make the routine faster over several rounds. It would propose a version, read the timings, reflect on what held it back, and revise the code each round. ### For newcomers and experts Think of it as a model that checks its own work and tries again. A first small thing to try is a short coding task with a script that prints a score, and watch how the answer changes across rounds. It is a dense multimodal model compatible with Qwen3.8, with a 262,144-token context. Training covered ML engineering and algorithm coding tasks whose results can be checked, along with the earlier AREX deep-research data. The authors say the learned habit carries over to deep research without new search data. The model proposes a solution → Your setup measures the result → The model reflects on the feedback → It revises and starts the next round ### Good to know The card compares the model with closed-weight and open-weight models on several benchmarks, including Frontier-CS and MLE-Lite. - Context: 262,144 tokens - Size: Hugging Face lists 27.4B parameters stored as Safetensors - Feedback: the method relies on scores, logs, errors and timings, so tasks with verifiable feedback suit it best - Other versions: 16 quantized and 1 fine-tuned version made by other people are listed - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the task files, scores and logs you pass to it ## README ````markdown # AREX-2 Improves its own solutions over several rounds of propose, measure, reflect and revise, using a 27B model from BAAI. For agent researchers and developers working on coding, machine-learning and deep-research tasks. Made by BAAI on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: AREX-2 is an agent model with 27B parameters, released by BAAI, the Beijing Academy of AI. It is built for long tasks where it improves an answer over many rounds. In each round it proposes a solution, measures it, reflects on the result and revises. It takes images and text and returns text. Who it suits: It suits researchers and engineers who build agents that iterate on coding, machine-learning or research tasks with measurable feedback. ```table Industry | Who uses it | How they use it | Example AI research | Agent researchers | Study self-improvement at test time | Compare results after more rounds on the same task Software | ML engineers | Let a model refine code against scores and timings | An algorithm that gets faster after each round Data science | Analysts | Iterate on machine-learning tasks with logs and errors | A training script fixed after reading its error logs Research teams | Deep-research builders | Reuse the learned iteration habit for research tasks | A research agent that revises its answer across rounds ``` If your task has no score, log or other feedback to read, you can skip it. Where to use it: - Python with the transformers library - Agent setups that run code or tools and return scores, logs and errors to the model - Windows, macOS or Linux machines with enough GPU memory for 27B parameters ```table Use case | Who | What they get back Algorithmic programming | Developers | A solution refined over several rounds Machine-learning engineering | ML engineers | Code changes guided by scores and logs Deep research | Researchers | Answers revised as the task budget grows ``` How to set it up: You need these first: - Python and pip - A GPU setup with room for about 54.7 GB of BF16 weights, or about 13.7 GB at a 4-bit estimate - A way to run your task and send results back to the model 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load the model with transformers as the model card describes. ```sh pip install -U huggingface_hub hf download BAAI/AREX-2 ``` How to use it: Give the model a task and a way to measure its work, then let it go through several rounds. Each round it reads what came back and decides what to change next. ```table Tool | What it does Propose | Writes a candidate solution for the task Measure | Runs the candidate and collects scores, logs, errors and timings Reflect | Reads that feedback to decide what to change Revise | Updates the solution and starts the next round ``` Example: You give it a slow routine and a timing script, and ask it to make the routine faster over several rounds. It would propose a version, read the timings, reflect on what held it back, and revise the code each round. For newcomers and experts: Think of it as a model that checks its own work and tries again. A first small thing to try is a short coding task with a script that prints a score, and watch how the answer changes across rounds. It is a dense multimodal model compatible with Qwen3.8, with a 262,144-token context. Training covered ML engineering and algorithm coding tasks whose results can be checked, along with the earlier AREX deep-research data. The authors say the learned habit carries over to deep research without new search data. ```flow The model proposes a solution Your setup measures the result The model reflects on the feedback It revises and starts the next round ``` Good to know: The card compares the model with closed-weight and open-weight models on several benchmarks, including Frontier-CS and MLE-Lite. - Context: 262,144 tokens - Size: Hugging Face lists 27.4B parameters stored as Safetensors - Feedback: the method relies on scores, logs, errors and timings, so tasks with verifiable feedback suit it best - Other versions: 16 quantized and 1 fine-tuned version made by other people are listed - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the task files, scores and logs you pass to it ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 27.4B - Weights: about 54.7 GB in BF16; about 13.7 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 3,028 - Likes: 208 - License: apache-2.0 - Based on: Qwen/Qwen3.8-27B - Quantized versions: 18 - Fine-tunes: 1 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, agent, deep-research, reasoning, tool-use, long-context, self-improvement, conversational ## Links - Model: https://huggingface.co/BAAI/AREX-2 - Files: https://huggingface.co/BAAI/AREX-2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download BAAI/AREX-2 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [BAAI/AREX-2 on Hugging Face](https://huggingface.co/BAAI/AREX-2) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | AREX-2 (this page) | 3,028 | 208 | 27.4B | apache-2.0 | 2026-10-01 | | [AREX-2 GGUF](https://mindket.com/p/0b19c840-3628-4057-9f9d-dc209a6d4308) | 8,118 | 20 | 26.9B | apache-2.0 | 2026-10-01 | | [AREX-2-i1-GGUF](https://mindket.com/p/a0765c77-cd86-42a8-a114-3f4a5606c4a7) | 2,207 | 11 | 26.9B | apache-2.0 | 2026-10-01 | | [Occamy-1.0](https://mindket.com/p/7e8bdc95-571e-4a24-a471-b64e83249759) | 1,807 | 240 | 35.1B | apache-2.0 | 2026-10-02 | | [Raptor-0.6-4B-JANG\_6M](https://mindket.com/p/959efc07-1497-4bb0-a31c-13867e79912c) | 1,032 | 7 | 4.1B | apache-2.0 | 2026-09-28 | | [DataWorld-35B-A3B](https://mindket.com/p/10cf86eb-7b6b-4cc1-a2cd-cb784138c50f) | 140 | 20 | 35.1B | apache-2.0 | 2026-10-16 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Bluesky · AlexTech.ai · 2026-09-30](https://bsky.app/profile/alextech.ai/post/3mwqesxpuep2d): BAAI launches AREX-2, a 27B parameter agent that challenges the giants. Using recursive self-improvement cycles, it beats GPT-5.6 Sol in ML engineering. Open weights and Apache 2.0 license. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-01 - First published: 2026-09-29 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open BAAI/AREX-2 on Hugging Face](https://huggingface.co/BAAI/AREX-2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/08d0a288-ffcd-401d-b201-719b8011c5bb) - [Its page on Mindket](https://mindket.com/p/08d0a288-ffcd-401d-b201-719b8011c5bb)