Qwen has launched Qwen-AgentWorld-35B-A3B, a 35B-parameter MoE model with only about 3B active parameters per token. It is trained to simulate responses from MCP, terminal, software engineering, Android, web, and OS GUI environments by predicting next observations after agent actions, enabling efficient agent training and environment simulation without real tool execution.
Qwen releases 35B-parameter MoE for agent environment simulation
Qwen3.6-27b-mtp-q8 creates A* pathfinding implementation via autonomous testing
The Qwen3.6-27b-mtp-q8 model successfully generated an A* pathfinding implementation for a Java-based test game using Claude Code locally. The process involved nearly 12 hours of iterative development where the model autonomously created and ran a testing suite.
Qwen3.6-27B with 3-Critic Harness Matches Frontier Quality
A user tested Qwen3.6-27B (8-bit) alongside GLM5.2 using a coding harness that employs three critics—code review, test review, and Playwright e2e—to validate output quality.
ASCII Art Enables Text-Only LLMs to Control VLA Systems
A text-only large language model can be adapted into a Vision--Language--Action controller by using ASCII-rendered visual observations. This approach allows LLMs to interpret visual states through text, enabling them to follow natural-language instructions and generate executable actions in both simulation and on physical manipulators.
CORTIS: Text-Only Adaptation of Spoken Language Models
CORTIS enables task-oriented voice agents to generate structured speech outputs by fine-tuning spoken language models using only text-form task supervision. It outperforms ASR-LLM cascades under acoustic degradation, especially in preserving high-level task semantics, without requiring paired speech-target annotations during training.
Updated Vision Model Benchmark Results and Recommendations
A revised benchmark of local vision language models evaluates 23 models across 30 images with 3 tests each, totaling 2,070 tests and 60 to 70 inference hours. The top-performing model is Qwen3.6 27B (nothink) at Q4 with a 79.6 score, followed by Qwen3.5 4B (nothink) at Q4, and Qwen3-VL 8B at Q8. Key findings include thinking mode degrading vision performance, MoE models underperforming compared to dense models, and Q8 quantization not universally improving results.