AREX-2

// Overview

AREX-2 is a 27-billion-parameter long-horizon agent model from the Beijing Academy of Artificial Intelligence (BAAI), published on Hugging Face on September 29, 2026 under the Apache 2.0 license together with the paper "AREX-2: Advancing Self-Improving Agents through Long-Horizon Reflective Tasks". It is a dense, Qwen3.8-compatible multimodal model built on Qwen3.8-27B, with a 262,144-token context window.

The model is trained to turn extra test-time rounds into a better answer: it proposes a solution, measures it, reflects on the feedback and revises. BAAI's paper treats this as two connected abilities — reflection, which reads scores, logs, errors and timings to decide what to change, and long-horizon execution, which keeps the improvement loop useful as the task budget grows.

BAAI trained AREX-2 on machine-learning-engineering and algorithmic-programming tasks where progress can be checked directly, together with the existing AREX deep-research data. According to the model card, the self-improvement behaviour learned on coding and ML tasks transfers to deep research without adding new search trajectories.

In BAAI's model card AREX-2 scores 70.7 on the 188-task Frontier-CS Agent Track and 81.8 on MLE-Lite (Any Medal, averaged over three seeds), and 84.0 on BrowseComp, 52.6 on HLE (text-only subset), 92.2 on GAIA and 93.8 on DeepSearchQA. The same tables list GPT-5.6 Sol at 76.4 on Frontier-CS and Kimi-K3 at 91.2 on BrowseComp, so the 27B model does not lead every column.

Alongside the weights, BAAI publishes the evaluation runners and dataset definitions for the six reported tracks in the VectorSpaceLab/AREX-2 GitHub repository. The model card positions AREX-2 for research on long-horizon agents, iterative problem solving, ML engineering, algorithmic coding and tool-augmented deep research.

// Benchmarks

This model's scores

1. DeepSearchQA93.8%
2. GAIA92.2%
3. BrowseComp84%
4. MLE-Lite (Any Medal)81.8%
5. Frontier-CS (Agent Track)70.7
6. HLE (text-only)52.6%

Scores on a 0–100 scale (25-point gridlines); higher is better. Each benchmark links to its published source.

// Strengths

• Trained to refine its own solution over many rounds, reading scores, logs, errors and timings to decide what to change
• 81.8 on MLE-Lite in BAAI's table, above every closed and open model listed there
• 84.0 on BrowseComp, 92.2 on GAIA and 93.8 on DeepSearchQA at 27B parameters
• Apache 2.0 weights with a 262,144-token context and open evaluation code

// Best for

• Reach for it for self-hosted agent loops that iterate on a measurable target — ML experiments, algorithmic coding against a judge, or benchmarked optimisation.
• Reach for it for tool-augmented deep research where a 27B open model fits your hardware.
• Look elsewhere for a general chat assistant: BAAI frames AREX-2 as a research model for long-horizon agents, not a tuned consumer assistant.