BAAI releases AREX-2, a 27B long-horizon agent model

🚀 RELEASES & LAUNCHES

• BAAI releases AREX-2, a 27B long-horizon agent model
  BAAI released AREX-2, a 27B long-horizon agent model.
  • AREX-2 is for machine-learning, algorithmic-programming, and deep-research tasks.
  • The model is a dense Qwen3.8-compatible multimodal system with 27B parameters and a 262,144-token context window.
  • It improves solutions across test-time rounds by proposing, measuring, reflecting, and revising from scores, logs, errors, and timings.
  • The model is available on Hugging Face under the Apache 2.0 license, with GGUF quantizations for local runtimes.
  • AREX-2 transfers its learned self-improvement behavior to deep research without adding new search trajectories.
  Sources 📄 Source: huggingface.co · 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA

📊 BENCHMARKS & EVALS

• Artificial Analysis open-sources AA-AgentPerf-Local for local AI benchmarking
  Artificial Analysis open-sourced AA-AgentPerf-Local to benchmark local AI agents on laptops and workstations.
  • The tool is for individuals and companies evaluating local AI serving on laptops and workstations.
  • AA-AgentPerf-Local replays 8 recorded agentic tasks across 168 model turns, with contexts reaching about 56K tokens.
  • Initial testing covers four hardware types and four 4-bit models, with 14 speculative-decoding configurations published.
  • The RTX 5090 completed supported workloads more than 3.5× faster than the other tested systems, while the DGX Spark beat Ryzen AI Halo by 1.4–1.7× on three models.
  Sources 📄 Source: github.com · 📎 Coverage: artificialanalysis.ai · 👁 via r/LocalLLaMA