"AREX-2 is a 27B-parameter long-horizon agent model from the Beijing Academy of Artificial Intelligence (BAAI). It learns to improve a solution over multiple test-time rounds: propose, measure, reflect, and revise. AREX-2 is trained on machine-learning and algorithmic-programming tasks with verifiable feedback, together with the existing AREX deep-research data. The learned self-improvement behavior transfers to deep research without adding new search trajectories. Architecture: Dense Qwen3.8-compatible multimodal model Parameters: 27B Context length: 262,144 tokens Key features Long-horizon self-improvement: turns extra test-time rounds into useful solution refinement. Feedback-driven reflection: reads scores, logs, errors, and timings to decide what to change next. Cross-domain performance: training on coding and machine-learning tasks also improves the model's deep-research performance. Long-horizon reasoning: sustains productive iteration as the task budget grows." Gguf's - https://huggingface.co/mradermacher/AREX-2-GGUF   submitted by   /u/Skyline34rGt [link]   [comments]